CN101356089A - System, method and computer software code for optimizing train operation in view of rail car parameters - Google Patents

Abstract

A method of improving train performance comprising determining railcar parameters for at least one railcar contained within a train, and creating a train trip plan based on the railcar parameters in accordance with at least one operating criterion of the train.

Description

System, method and computer software code for optimizing train operation in view of rail car parameters

This application is based on Provisional Application No. 60/802,147, filed May 19, 2006, and is a continuation-in-part of US Application No. 11/385,354, filed March 20, 2006, the entire contents of which are hereby incorporated by reference.

technical field

The field of the invention relates to rail transportation, and more particularly to identifying rail car parameters for use in improving train operations.

Background of the invention

A locomotive is a complex system with multiple subsystems, each of which is interdependent with other subsystems. Operators typically go aboard the locomotive to ensure proper functioning of the locomotive and its associated freight car load. In addition to ensuring proper functioning of the locomotive, the operator is also responsible for determining the operating speed of the train of which the locomotive is a part and for limiting the forces within the train to acceptable values. To perform this function, the operator must generally have extensive experience operating locomotives and various trains over the given terrain. This knowledge is necessary in order to comply with a specifiable operating speed which may vary with the train's position along the track. In addition, the operator is also responsible for ensuring that the forces within the train remain within acceptable limits.

The railway yard is the hub of the railway transportation system. Rail yards perform many services such as origination, interchange, and termination of freight, locomotive storage and maintenance, assembly and inspection of new trains, maintenance of trains passing through facilities, inspection and maintenance of railcars, and rail car storage. The different services of a railway yard compete for resources such as personnel, equipment and space in different plants, so effectively managing an entire railway yard is a complex operation.

Assembling a new train typically involves assembling it based on the number of times the train load is booked to a given destination and the motive power available to the given train. Typically when assembling a train, the placement of the railcars in the train can be done randomly. More specifically, the arrangement of cars is not performed in accordance with the order in which train operation can be optimized. Train journey optimization can be improved with knowledge of information such as car weights, loads, wheel axial, lateral and/or vertical forces. This type of information can help optimize certain aspects of train operations, such as, but not limited to, fuel/speed optimization for acceleration, deceleration, improved train handling for distributed power or non-distributed power trains, and/or or improved emissions.

There is a continuing need to improve train assembly processes and improve train locomotive operating parameters to reduce fuel costs and over-road transit times. One method disclosed herein uses railcar parameters when assembling a train.

Contents of the invention

Exemplary embodiments of the present invention disclose a system, method, and computer software code for identifying rail car parameters for use in improving train operation. To this end, a method for improving the performance of a train comprises the step of determining rail car parameters for at least one rail car to be included in the train. Another step includes creating a train trip plan based on the railcar parameters according to at least one operating criterion of the train.

In another exemplary embodiment, computer software code for use in a processor to improve train performance is disclosed. The computer software code comprises a computer software module for determining rail car parameters of at least one rail car of the train. Another computer software module is for creating a train trip plan based on the rail car parameters according to at least one operating criterion of the train.

A system for improving train performance by determining rail car parameters is also disclosed. The system includes a rail car parameter measurement system. A central controller is also disclosed. A communication network is further included enabling communication between the measurement system and the central controller. Railcar parameters are measured and provided to the central controller, which then determines a train make up profile for all railcars in the train based on the railcar parameters and/or assigns a train task Determine the itinerary.

Detailed description of the drawings

The invention, briefly described above, will now be described in more detail with reference to specific embodiments of the invention that are illustrated in the appended drawings. It is to be understood that these drawings only depict typical embodiments of the invention and therefore are not to be considered as limiting the scope of the invention. The invention will be described and explained below by using the accompanying drawings with additional features and details, wherein :

Figure 1 depicts an exemplary illustration of a flow diagram of the present invention;

Figure 2 depicts a simplified model of a train that can be used;

Figure 3 depicts an exemplary embodiment of elements of the invention;

Figure 4 depicts an exemplary embodiment of a fuel use/travel time graph;

Figure 5 depicts an exemplary embodiment of segmenting a trip plan;

Figure 6 depicts an exemplary embodiment of a segmentation instance;

Figure 7 depicts an exemplary flow chart of the present invention;

Figure 8 depicts an exemplary illustration of a dynamic display for use by an operator;

Figure 9 depicts another exemplary illustration of a dynamic display for use by an operator;

Figure 10 depicts another exemplary illustration of a dynamic display for use by an operator;

Figure 11 depicts a schematic representation of a system for automatic identification of railcar parameters for improved train operation; and

Figure 12 depicts a flowchart illustrating the steps of automatically identifying rail car parameters for improved train operation.

Detailed description of the invention

Exemplary embodiments of the present invention solve problems in the art by providing systems, methods, and computer software code for identifying rail car parameters for use in improving train operation. Those skilled in the art will recognize that a device, such as a data processing system, including a CPU, memory, I/O, program memory, connecting buses, and other appropriate components, may be programmed or otherwise designed to facilitate Implementation of the method of an exemplary embodiment of the invention. Such a system would include appropriate program means for implementing the exemplary embodiments of the invention.

Generally speaking, the technical effect is to identify rail car parameters and use these parameters to improve train operation. The invention may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules may include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. For example, a software program supporting exemplary embodiments of the invention can be encoded in different languages for use with different computing platforms. It should be understood, however, that other types of computer software technology can also be used to implement the principles supporting the exemplary embodiments of this invention.

Additionally, those skilled in the art will appreciate that the present invention may be practiced with other computer system configurations, including handheld devices, multiprocessor systems, microprocessor-based or programmable consumer electronics, microcomputers, mainframe computers etc. The invention may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including storage devices.

Furthermore, an article of manufacture (e.g., a pre-recorded disk or other similar computer program product) for use with a data processing system may include a storage medium and program means recorded thereon for instructing the data processing system to facilitate Implementation of the method of the present invention. Such devices and articles of manufacture also fall within the spirit and scope of the invention.

The term "locomotive consist" is used throughout the document. As used herein, a locomotive consist may be described as having one or more consecutive locomotives connected together to provide motoring and/or braking capabilities. The locomotives are linked together, where there are no train cars between the locomotives. A train can have more than one locomotive consist in its composition. In particular, there may be one lead group and more than one remote group, such as one group in the middle of the car line and another remote group at the end of the train. Each locomotive consist may have a first locomotive and a trail locomotive(s). Although the first locomotive is generally considered the lead locomotive, those skilled in the art will readily recognize that the first locomotive in a multiple-locomotive consist may be physically located in a physical drag position. Although a locomotive consist is generally viewed as a contiguous locomotive, those skilled in the art will readily recognize that even when at least one car is separated from the locomotive, such as when the locomotive consist is configured for distributed power operation (where throttle and Brake commands are relayed from the lead locomotive to the remote train via a radio link or physical cable), a group of locomotives can also be considered a group. For this reason, the term "locomotive consist" should not be considered a limiting factor when discussing multiple locomotives in the same train.

Embodiments of the present invention will now be described with reference to the drawings. The invention can be implemented in numerous ways, including as a system (including a computer processing system), a method (including a computer-implemented method), an apparatus, a computer-readable medium, a computer program product, a graphical user interface (including a web portal), or an actual A data structure permanently stored in a computer readable memory. Several embodiments of the invention are discussed below.

Figure 1 depicts an exemplary illustration of a flow diagram of an exemplary embodiment of the present invention. As shown, instructions specific to planning a trip onboard or from a remote location (eg, dispatch center 10) are entered. Such input information includes, but is not limited to: train location, group description (e.g. locomotive model), locomotive power description, locomotive traction transmission performance, engine fuel consumption as a function of output power, cooling characteristics, planned travel route (as road signs ( effective track slope and curvature as a function of milepost), or an "effective slope" component reflecting curvature following standard railroad practice), trains represented by carriage composition and load together with effective drag coefficients, desired travel parameters include but are not limited to : start time and location, end location, expected travel time, crew (user and/or operator) identification, crew shift due time and route.

This data can be provided to the locomotive 42 in a variety of ways, such as but not limited to: the operator manually enters the data into the locomotive 42 via an on-board display, transfers the data to a storage device (such as a hardware card and/or USB drive) into a receptacle on the locomotive, and via wireless communication from a central or wayside location 41 such as a track signaling device and/or wayside means (as shown in Figure 3) The information is transmitted to the locomotive 42 . Locomotive 42 and train 31 load characteristics (e.g., drag) may also vary along the route (e.g., with rail and car condition, altitude, ambient temperature), and any of the above may be used as desired method and/or update the plan to reflect such changes by automatically collecting locomotive/train conditions in real time. This includes, for example, by monitoring devices on or off the locomotive(s) 42 to detect changes in locomotive or train characteristics.

The track signaling system determines the allowable train speed. There are many types of track signaling systems and operating rules associated with each signal. For example, some signals have a single light (on/off), some signals have a single lens with multiple colors, and some signals have multiple lights and colors. These signals can indicate that the track is clear and the train can proceed at the maximum allowable speed. They can also indicate the need to slow down or stop. This deceleration may need to be done immediately, or at a certain location (eg, before the next signal or intersection).

Signal status is communicated to the train and/or operator by various means. Some systems have electrical circuits in the track and inductive pick-up coils on the locomotive. Other systems have wireless communication systems. Signaling systems may also require the operator to visually inspect the signal and take appropriate action.

The signaling system can interface with the on-board signaling system and adjust locomotive speed based on input and appropriate operating rules. For signaling systems that require the operator to visually check the status of the signal, the operator screen will present the operator with appropriate signaling options for input based on the train's location. Operating rules and types of signaling systems may be stored in the on-board database 63 as a function of location.

Based on the specification data input into an exemplary embodiment of the present invention, an optimal plan (subject to speed limit constraints along a route with a desired start and end time) that minimizes fuel consumption and/or resulting emissions is calculated to A travel profile 12 is generated. The profile includes optimal speed and power (notch) settings to be followed by the train, expressed as a function of distance and/or time, and such train operating constraints, including but not limited to: maximum notch power and brake settings, and speed limits as a function of location, and expected fuel consumed and emissions produced. In the exemplary embodiment, values for notch settings are selected to obtain throttle change decisions approximately every 10 to 30 seconds. Those skilled in the art will readily recognize that throttle change decisions can occur for longer or shorter durations if needed and/or desired to follow an optimal speed profile. In broad terms, it will be apparent to those skilled in the art that these profiles provide power settings for trains at any level of train level, group level and/or individual train level. Power includes braking power, driving power and air braking power. In another preferred embodiment, the exemplary embodiments of the present invention are capable of selecting a continuous power setting determined to be optimal for the selected profile, rather than operating at conventional discrete power notch settings. Thus, for example, if the optimal profile specifies a notch setting of 6.8 rather than operating at a notch setting of 7, locomotive 42 is able to operate at 6.8. Allowing such intermediate power settings may bring additional efficiency benefits as described below.

As outlined below, the procedure used to calculate the optimal profile can be any of a variety of methods for calculating a power sequence to drive the train 31 to minimize fuel and/or emissions subject to locomotive operation and schedule constraints. In some cases, due to similarities in train configurations, routes and environmental conditions, the desired optimal profile may be close enough to a previously determined profile. In these cases it is sufficient to look up the drive track in the database 63 and try to follow it. When none of the previously calculated plans is suitable, methods for computing a new plan include, but are not limited to, using a differential equation model that approximates the physics of train motion to guide the calculation of an optimal profile. This setup involves choosing a quantitative objective function, typically a weighted sum (integral) of model variables corresponding to specific fuel consumption and emissions production plus a term to penalize excessive throttle changes.

The optimal control formulation is established to minimize a quantitative objective function subject to constraints including, but not limited to, speed limits and minimum and maximum power (throttle) settings. Depending on the planning objective at any time, the problem can be formulated flexibly to minimize fuel subject to the constraints of emissions and speed limits, or to minimize emissions subject to the constraints of fuel consumption and arrival time. It is also possible to establish goals such as minimizing total travel time without constraints on total emissions or fuel consumption, where relaxation of such constraints would be allowed or required for the mission.

Exemplary equations and objective functions are presented throughout the document for minimizing locomotive fuel consumption. These equations and functions are examples only, other equations and objective functions can be used to optimize fuel consumption or to optimize other locomotive/train operating parameters.

Mathematically, the problem to be solved can be stated more precisely. The basic physical properties are expressed as:

$\frac{\mathrm{dx}}{\mathrm{dt}}=v;$ x(0)=0.0; x(T _f )=D

$\frac{\mathrm{dv}}{\mathrm{dt}}=T_e(u,v)-G_a\left(x\right)-R\left(v\right);$ v(0)=0.0; v(T _f )=0.0

Here x is the position of the train, v is the speed of the train, and t is the time (in miles, miles per hour and minutes or hours as the case may be, respectively), and u is the gear (throttle) command input. In addition, D denotes the distance to be traveled, T _f is the expected arrival time along the track over the distance D, T _e is the tractive effort produced by the locomotive consist, and G _a is the ground force which depends on the length of the train, the composition of the train and the terrain on which the train is located. Gravity, R is the drag force of the combination of locomotive and train depending on the net speed. Initial and final speeds can also be specified, however without loss of generality they are set to zero here (the train is at a standstill at the beginning and end). Finally, the model can easily be modified to include other important dynamics, such as the hysteresis between changes in throttle u and the resulting traction or braking forces. Using this model, an optimal control formulation is developed to minimize a quantitative objective function subject to constraints including but not limited to: speed limit and minimum and maximum power (throttle) settings. Depending on the planning objective at any time, the problem can be formulated flexibly to minimize fuel subject to the constraints of emissions and speed limits, or to minimize emissions subject to the constraints of fuel consumption and arrival time.

It is also possible to establish an objective, for example, to minimize the total travel time without constraints on the total emissions or fuel consumption, where relaxation of such constraints is allowed or required for this task. All of these performance measures can be expressed as any linear combination of:

-最小化总燃料消耗

-Minimize total fuel consumption

-最小化行驶时间

- Minimize travel time

- Minimize jockeying (piecewise constant input)

-最小化挡操纵(连续输入)

- Minimize gear manipulation (continuous input)

-最小化总排放消耗。在这个公式中E是每个挡(或功率设置)的排放量(单位是克每马力小时(gm/hphr))。另外可以根据燃料和排放的加权总和来实现最小化。Replace the fuel term F in (1) with the term corresponding to the emissions. For example, for emissions

- Minimize total emissions consumption. In this formula E is the emissions (in grams per horsepower hour (gm/hphr)) for each gear (or power setting). Alternatively minimization can be achieved based on a weighted sum of fuel and emissions.

Therefore, the commonly used and representative objective function is:

$\underset{\mathrm{<span\; class="notranslate">\; u</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span>}{\mathrm{<span\; class="notranslate">\; min</span>}}{\mathrm{<span\; class="notranslate">\; \&alpha;</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}\underset{\mathrm{<span\; class="notranslate">\; 0</span>}}{\overset{{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; f</span>}}}{<span\; class="notranslate">\; \&Integral;</span>}}\mathrm{<span\; class="notranslate">\; f</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; u</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span>\mathrm{<span\; class="notranslate">\; dt</span>}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; \&alpha;</span>}}_{\mathrm{<span\; class="notranslate">\; 3</span>}}{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; f</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; \&alpha;</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}\underset{\mathrm{<span\; class="notranslate">\; 0</span>}}{\overset{{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; f</span>}}}{<span\; class="notranslate">\; \&Integral;</span>}}{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; du</span>}<span\; class="notranslate">\; /</span>\mathrm{<span\; class="notranslate">\; dt</span>}<span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}\mathrm{<span\; class="notranslate">\; dt</span>}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; OP</span>}<span\; class="notranslate">\; )</span>$

The coefficients of the linear combination depend on the importance (weight) given to each term. Note that in equation (OP), u(t) is the optimization variable for the sequential gear positions. If discrete gears are required, eg for older locomotives, the solution to equation (OP) is discretized, which can result in lower fuel savings. Finding the minimum time solution (α ₁ set to 0, α ₂ set to 0 or a relatively small value) is used to find a lower bound (T _f =T _fmin ) for the achievable travel time. In this case, both u(t) and Tf are optimization variables. A preferred embodiment solves equation (OP) with α ₃ set to 0 for various values of T _f for which T _f >T _{f min} . In this latter case, T _f is treated as a constraint.

For those familiar with solutions to such optimal problems, additional constraints are necessary, for example, speed limits along paths:

0≤v≤SL(x)

Or when a minimum time is the goal, the endpoint constraint must hold, e.g. the total fuel consumed must be less than the fuel in the tank, e.g. via:

$\mathrm{<span\; class="notranslate">\; 0</span>}<span\; class="notranslate">\; <</span>\underset{\mathrm{<span\; class="notranslate">\; 0</span>}}{\overset{{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; f</span>}}}{<span\; class="notranslate">\; \&Integral;</span>}}\mathrm{<span\; class="notranslate">\; f</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; u</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span>\mathrm{<span\; class="notranslate">\; dt</span>}<span\; class="notranslate">\; \&le;</span>{\mathrm{<span\; class="notranslate">\; W</span>}}_{\mathrm{<span\; class="notranslate">\; f</span>}}$

Here _WF is the fuel remaining in the tank at time _Tf . Those skilled in the art will readily recognize that equation (OP) can also be expressed in other forms and that the above presentation is an exemplary equation for use in an exemplary embodiment of the present invention.

Emissions referred to in the context of exemplary embodiments of the present invention may refer to cumulative emissions generated in various forms. For example, emissions requirements may set maximum values for nitrogen oxide (NOx) emissions, hydrocarbon (HC) emissions, carbon oxides (COx) emissions, and/or particulate matter (PM) emissions. Other emission limits may include maximum values for electromagnetic emissions, such as limits on radio frequency (RF) power output (measured in watts) at frequencies emitted by locomotives. Another form of emissions is the noise produced by locomotives, usually measured in decibels (dB). Emissions requirements may vary based on time of day, time of year, and/or changes in atmospheric conditions (eg, weather or pollution levels in the air). It is known that emissions adjustments can vary geographically across a rail system. For example, an area of operation (eg, a city or state) may have specified emission targets, and adjacent areas may have different emission targets, such as allowing lower emissions or charging higher fees for a given emission class. Accordingly, an emission profile for a particular geographic region may be compiled to include a maximum emission value for each adjusted emission contained in the profile to meet predetermined emission targets required for that region. Typically, for a locomotive, these emission parameters are determined by power (gear), ambient conditions, engine control method, and the like.

By design, each locomotive must comply with EPA standards for brake-specific emissions, so when emissions are optimized in an exemplary embodiment of the invention, this may be mission total emissions, for which no regulations currently exist. Operation has always been subject to federal EPA requirements. If the key goal during a trip mission is to reduce emissions, the optimal control formula, equation (OP), will be modified to take this trip goal into account. A key flexibility in the optimization setup is that any or all trip objectives can vary by geographic region or mission. For example, for a high-priority train, the minimum time may be the only goal on a route because it is high-priority traffic. In another example, emissions output may vary from state to state along a planned train route.

To solve the resulting optimization problem, in an exemplary embodiment, the present invention transforms the dynamic optimal control problem in the time domain into an equivalent static mathematical programming problem with N decision variables, where the number N depends on the The frequency at which throttle and brake adjustments are made and the duration of the trip. For typical problems, this N can be in the thousands. For example, in one exemplary embodiment, assume a train is traveling on a stretch of track 172 miles southwest of the United States. Utilizing the exemplary embodiment of the present invention, when comparing the trip determined and followed using the exemplary embodiment of the present invention with the actual driver throttle/speed history if the trip was determined by the operator, the fuel expended An exemplary savings of 7.6% can be realized on . Increased savings are realized because the optimization achieved by utilizing exemplary embodiments of the present invention results in a drive strategy with both fewer drag losses and little or no braking losses compared to the operator's trip plan.

To make the optimization described above computationally tractable, a simplified model of the train, such as that shown in Figure 2 and the equations discussed above, can be employed. By driving a more detailed model with the resulting optimal power sequence, a critical refinement of the optimized profile is produced to test whether other thermal, electrical and mechanical constraints are violated, resulting in a speed with the closest approximation to operation Modified profiles for distances that can be achieved without damage to the locomotive or train equipment (ie satisfying additional implicit constraints such as thermal and electrical constraints on locomotive and inter-car forces in the train).

Referring back to FIG. 1 , once a trip begins 12 , a power command 14 is generated to begin planning. According to the operating arrangement of the exemplary embodiment of the present invention, a command is used to cause the locomotive to follow the optimized power command 16 to obtain the optimal speed. The exemplary embodiment of the present invention obtains actual speed and power information 18 from the train's locomotive consist. Due to unavoidable approximations in the models used for optimization, closed-loop calculations corrected to optimal power are obtained to track the desired optimal speed. Correction of such train operating restrictions can be performed automatically or by an operator who has ultimate control of the train at all times.

In some cases, the model used in the optimization can differ significantly from the actual train. This can happen for many reasons including, but not limited to, pickup or setout of extra freight, locomotives that break down on the route, and errors in the initial database 63 or operator input data. For these reasons, a monitoring system is appropriate that utilizes real-time train data to estimate locomotive and/or train parameters 20 in real-time. The estimated parameters are then compared 22 with the assumed parameters used when the trip was initially created. The trip can be re-planned 24 based on any differences between the assumptions and estimates if a sufficiently large saving is to be generated from the new plan.

Other reasons a trip may be replanned include indications from remote locations, such as scheduling and/or operator requests to change goals to align with more global motion planning goals. More global motion planning goals may include, but are not limited to: other train schedules, allowing exhaust to dissipate from tunnels, maintenance operations, and the like. Another reason can be attributed to the failure of on-board parts. Depending on the severity of the disruption, strategies for conducting replanning can be grouped into incremental and major adjustments, as discussed in more detail below. In general, a "new" plan must be derived from the solution of the optimization problem equation (OP) described above, but as described here, faster approximate solutions can often be found.

In operation, locomotive 42 will continuously monitor system efficiency and continuously update the trip plan based on the measured actual efficiency, whenever such an update would improve trip performance. Calculations for re-planning may be performed entirely within the locomotive(s) or may be fully or partially offloaded to a remote location, such as a dispatch or wayside processing facility, where the plan is communicated to the locomotive 42 using wireless technology. Exemplary embodiments of the present invention may also generate efficiency trends that can be used to generate locomotive fleet data on efficiency transfer functions. Fleet-wide data can be used when determining the initial trip plan and for network-wide optimization trade-offs when considering the locations of multiple trains. For example, the travel-time fuel cost trade-off curve shown in Figure 4 reflects the performance of a train on a particular route at that time, updated from an overall average collected for multiple similar trains on the same route. In this way, a central dispatch facility collecting curves like that of Figure 4 from multiple locomotives can use this information to better coordinate overall train movement to gain system-wide advantages in terms of fuel consumption or throughput.

Many events in day-to-day operations can lead to the need to create or modify current execution plans where it is desirable to maintain the same itinerary when a train does not meet or pass another train on time as planned and it needs to make up time Target. Using the actual speed, power and position of the locomotive, a comparison is made between the planned arrival time and the current estimated (predicted) arrival time 25 . The schedule 26 is adjusted as a function of time, and as a function of parameters (detected or changed by the scheduler or operator). This adjustment can be made automatically following the railroad company's requirements as to how such deviations from plan should be handled, or the operator on board the car and the dispatcher can manually present options to jointly decide the best way to get back to plan. Whenever the plan is updated but while the original target (such as but not limited to arrival time) remains the same, additional changes can be included at the same time, such as new future speed limit changes, which affect the original plan once restored. feasibility. In such a situation, if the original trip plan cannot be maintained, or in other words the train cannot meet the original trip plan goals, then as discussed herein, other trip plan(s) may be provided to the operator and /or remote facility, or dispatch.

Replanning can also be done when it is desired to change the original goal. Such rescheduling can be done manually at fixed pre-planned times at the discretion of the operator or dispatcher, or automatically when predefined limits (eg, train operating limits) are exceeded. For example, if execution of the current plan is later than a specified threshold (eg, 30 minutes), an exemplary embodiment of the present invention can reschedule the trip to accommodate the delay or alert the operator and scheduler as described above at the expense of increased fuel. How much time can the crew make up in total (that is, the minimum time to go or the maximum fuel that can be saved within the time constraint). Other triggers for rescheduling can also be foreseen based on fuel consumed or the state of the power pack, including but not limited to arrival time, due to equipment failure and/or temporary misbehavior of equipment (e.g., running too hot or too cold) horsepower loss, and/or gross setup errors detected, these are in the assumed train load. In other words, if the changes reflect a reduction in locomotive performance for the current trip, these can be included in the models and/or equations used in the optimization.

Changes in planning objectives may also arise from the need to coordinate events where the planning of one train takes into account the ability of another train to meet objectives at different levels and arbitration, eg the need for a dispatch room. For example, with train-to-train communication, the coordination of encounters and passing can be further optimized. Thus, communications from other trains can inform the delayed train (and/or schedule) if the train knows it is behind in arriving at the meeting and/or passing location, for example. The operator can then enter information about the delay into the exemplary embodiment of the invention, which will recalculate the train's trip plan. Exemplary embodiments of the present invention can also be used at a high level, or network level, to allow dispatch to determine which train should slow down or speed up if predetermined encounter and/or elapsed time constraints may not be met. As described herein, this is accomplished by trains transmitting data to dispatch to prioritize how each train should alter its planning objectives. Depending on the situation, choices can be made based on schedule or fuel saving benefits.

Exemplary embodiments of the present invention may provide the operator with more than one trip plan for any manually or automatically initiated replan. In one exemplary embodiment, the present invention will provide the operator with different profiles to allow the operator to select an arrival time and understand the corresponding fuel and/or emissions impact. For similar considerations, such information can also be provided to the scheduler, either as a simple list of alternatives or as multiple tradeoff curves as shown in FIG. 4 .

Exemplary embodiments of the present invention are able to learn and adapt to critical changes in trains and power packs, which can be incorporated into current plans and/or future plans. For example, one of the aforementioned triggers is a loss of horsepower. When increasing horsepower over time, or after losing horsepower or when starting a trip, transition logic is utilized to determine when the desired horsepower is achieved. This information can be saved in the locomotive database 61 for use in optimizing future trips or current trips should horsepower losses reoccur.

FIG. 3 depicts an example embodiment of elements that may be part of an example system. A locator element 30 for determining the position of the train 31 is provided. The locator element 30 may be a GPS sensor, or a sensor system, for determining the position of the train 31 . Examples of such other systems may include, but are not limited to, roadside devices such as radio frequency automatic equipment identification (RFAEI) tags, dispatch and/or video determination. Another system may include tachometer(s) on the locomotive and distance calculation from a reference point. As previously mentioned, a wireless communication system 47 may also be provided to allow for communication between trains and/or with remote locations (eg dispatch). Information about travel locations can also be relayed from other trains.

A track characterization element 33 is also provided to provide information about the track, mainly slope, elevation and curvature information. The track characterization element 33 may include an on-board track integrity database 36 . Sensors 38 are used to measure traction 40 being pulled by the locomotive consist 42, throttle settings of the locomotive consist 42, consist 42 configuration information, consist 42 speed, individual locomotive configuration, individual locomotive capabilities, and the like. In an exemplary embodiment, locomotive consist 42 configuration information may be loaded without the use of sensors 38, but entered by other means as described above. In addition, the condition of the locomotives in the locomotive consist can also be considered. For example, if one locomotive in the consist cannot operate above power notch level 5, this information is used when optimizing the trip plan.

Information from the locator element may also be used to determine the proper arrival time of the train 31 . For example, if a train 31 is moving along track 34 toward a destination with no trains behind it, and the train has no fixed arrival deadline to meet, then locator elements (including but not limited to radio frequency automatic equipment identification (RF AEI) tags, Scheduling and/or video determination) can be used to pinpoint the exact location of the train 31. Additionally, inputs from these signaling systems can be used to adjust train speed. Utilizing the on-board track database and locator elements (eg, GPS) discussed below, exemplary embodiments of the present invention are able to adjust the operator interface to reflect signaling system status at a given locomotive location. In situations where the signal status would indicate a speed limit ahead, the planner may choose to reduce the speed of the train to save fuel consumption.

Information from the locator element 30 can also be used to change the planning target according to the distance from the destination. For example, due to the inevitable uncertainty about congestion along the line, a "faster" time target may be adopted on the early part of the line to avoid delays that are statistically likely to occur later. If it happens that no delay occurs on an individual trip, the target for the later part of the trip can be modified to take advantage of the built-in slack time accumulated earlier, compensating for some fuel efficiency. Similar strategies can be invoked with respect to emission limit targets, such as proximity to urban areas.

As an example of a precautionary strategy, if a trip is planned from New York to Chicago, the system may choose to run the train slower at the beginning of the trip or in the middle of the trip or at the end of the trip. Because unknown constraints (such as but not limited to weather conditions, track maintenance, etc.) can develop and become known during a trip, exemplary embodiments of the present invention will optimize the trip plan to account for deceleration at the end of the trip. As another consideration, if traditionally congested areas are known, plans may be chosen to be developed with more flexibility around these traditionally congested areas. Thus, exemplary embodiments of the present invention may also consider weighting/penalty as a function of time/distance into the future and/or based on known/past experience. Those skilled in the art will readily recognize that such planning and re-planning to account for weather conditions, track conditions, other trains on track, etc., can be considered at any time during the trip, with the trip plan adjusted accordingly.

Figure 3 also discloses other elements that may be part of the exemplary embodiments of the present invention. A processor 44 operable to receive information from the locator element 30 , the track characterization element 33 , and the sensor 38 is provided. Algorithm 46 runs within processor 44 . Algorithm 46 is used to calculate an optimized trip plan based on parameters related to locomotive 42, train 31, track 34, and mission objectives as described above. In the exemplary embodiment, the trip plan is built from a model of the train behavior of the train 31 moving along the track 34, where the model is the solution of a non-linear differential equation derived from the physical properties with the simplifying assumptions provided in the algorithm . Algorithm 46 may use information from locator element 30, track characterization element 33, and/or sensors 38 to create a trip plan that minimizes fuel consumption of locomotive consist 42, minimizes emissions of locomotive consist 42, Establish desired travel times, and/or ensure appropriate crew operating times on the locomotive consist 42 . In the exemplary embodiment, a driver or controller element 51 is also provided. As discussed herein, the controller element 51 is used to control the train as it follows the trip plan. In the exemplary embodiment described further herein, the controller element 51 makes train operating decisions autonomously. In another exemplary embodiment, the operator may focus on directing the train to follow the trip plan.

A requirement of the exemplary embodiments of the present invention is the ability to initially create plans and to rapidly modify any plans that are being executed on the fly. Due to the complexity of plan optimization algorithms, this involves creating an initial plan when long distances are involved. When the total length of the trip profile exceeds a given distance, Algorithm 46 can be used to segment tasks, where tasks can be segmented by waypoints. Although only a single algorithm 46 is described, those skilled in the art will readily recognize that more than one algorithm may be utilized, where such algorithms may be combined. A waypoint may include a natural location where a train 31 stops, such as, but not limited to, a siding where a train meeting an oncoming train or passing a current train is scheduled to appear on a single-track railway, or a station siding where carriages are collected and distributed (yard siding) or industrial area, and the planned work location. At such waypoints, the train 31 may be required to arrive at a predetermined location at a predetermined time and to stop or move at a speed within a specified range. The period from arrival at the waypoint to departure from the waypoint is called dwell time.

In an exemplary embodiment, the present invention can take a dedicated systematic approach to breaking down longer trips into smaller segments. Each segment can be somewhat arbitrary in length, but is usually chosen at a natural location such as a stop or where there is a clear speed limit, or at key landmarks defining intersections with other routes. Given a partition or section selected in this way, a driving profile is created for each section of the track, as shown in FIG. The fuel cost/travel time trade-off associated with each segment can be calculated before the train 31 reaches that track segment. A total travel plan can be created from the drive profiles created for each segment. The exemplary embodiment of the present invention distributes the travel time in an optimal manner in all segments of the trip to meet the desired total travel time and to keep the total fuel consumption over all segments as small as possible. An exemplary 3-segment trip is disclosed in FIG. 6 and described below. However, those skilled in the art will recognize that although sections are described, a trip plan may include a single section representing a complete trip.

FIG. 4 depicts an exemplary embodiment of a fuel consumption/travel time graph. As mentioned previously, such a curve 50 is created when calculating the optimal trip profile for the different travel times of each segment. That is, for a given travel time 51, the fuel expended 52 is the result of the detailed driving profile calculated as described above. Once travel times are assigned to each segment, a power/speed schedule is determined for each segment based on the previously computed solution. If there are any waypoint constraints on speed between segments, such as, but not limited to, changes in speed constraints, they are made to cooperate during creation of the optimal trip profile. If the speed limit is only changed in a single segment, then the fuel consumption/travel time curve 50 only needs to be recalculated for the changed segment. This reduces the time that more parts or segments of the trip have to be recalculated. If a locomotive consist or train changes significantly along the route, for example, because of the loss of a locomotive or the take-up or allocation of cars, then the drive profiles for all subsequent segments must be recalculated to create new instances of curve 50 . These new curves 50 will then be used together with the new schedule targets to plan the remainder of the trip.

Once the trip plan is created as described above, the speed and power versus distance trajectory is used to reach the destination at the required trip time with the least amount of fuel and/or emissions. There are a number of methods to perform trip planning. As described in more detail below, in the exemplary embodiment, in the training mode, information is displayed to the operator for the operator to follow in order to obtain the desired power and speed determined from the optimal trip plan. In this mode, the operating information is the suggested operating conditions that the operator should use. In another exemplary embodiment, acceleration is achieved and constant speed is maintained. However, the operator is responsible for using the braking system 52 when the train 31 must slow down. In another exemplary embodiment of the present invention, commands for driving and braking are provided as needed to follow a desired speed-distance route.

A feedback control strategy is utilized to provide corrections to the power control sequence in the profile to correct for events such as, but not limited to, train load changes due to headwind and/or tailwind fluctuations. Another such error may arise from errors in train parameters, such as, but not limited to, train mass and/or drag force, when compared to assumptions in the optimized trip plan. A third type of error may occur due to the information included in the orbit database 36 . Another possible error may include unmodeled performance differences caused by locomotive engines, traction motor thermal deration, and/or other factors. Feedback control strategies compare the actual speed as a function of position to the desired speed in the optimal profile. Based on this difference, a correction to the optimal power profile is added to bring the actual speed closer to the optimal profile. In order to ensure stable regulation, a compensation algorithm can be provided which filters the feedback speed into the power correction to ensure the stability of the closure performance. Compensation may include standard dynamic compensation used by those skilled in the art of control system design to meet performance goals.

Exemplary embodiments of the present invention allow the simplest and therefore fastest means of adapting to changes in trip objectives, which is the rule, not the exception, in railroad operations. In an exemplary embodiment, to determine the fuel-optimal itinerary from point A to point B (where there are stations along the route), and to update the itinerary for the remainder of the trip, once the trip has started, a sub-optimal decomposition of method can be used to find the optimal travel profile. Using modeling methods, computational methods are able to find trip plans with specified travel times and initial and final velocities that satisfy all speed limits and locomotive performance constraints when there are stations. Although the description that follows is directed to optimizing fuel consumption, it can also be applied to optimizing other factors such as but not limited to: emissions, schedule, crew comfort and load impact. This approach can be used in the early stages of developing a trip plan, and more importantly to accommodate changes in goals after a trip has been initiated.

As discussed herein, exemplary embodiments of the present invention may employ the setup illustrated in the exemplary flowchart shown in FIG. 5 , and as an example, a 3-section example is described in detail in FIG. 6 . As shown, a trip can be broken down into two or more segments: T1, T2, and T3. Although discussed here, it is also possible to treat a trip as a single segment. As discussed herein, the presence of segment boundaries may not result in equal segments. Instead, segments use natural or task-specific boundaries. Precompute the optimal trip plan for each segment. If fuel cost versus trip time is the trip goal to be met, then a fuel versus trip time curve is established for each segment. As discussed herein, the curve may be based on other factors that are the goals that the trip plan is to meet. When travel time is the parameter to be determined, the travel time for each segment is calculated when the total travel time constraint is satisfied. FIG. 6 shows speed limits for an exemplary 3-segment 200-mile trip 97 . The grade change over the 200 mile trip 98 is further shown. Also shown is a combined graph 99 representing fuel consumption versus travel time for each segment of the trip.

Using the previously described optimal control setup, the present computational method is able to find a trip plan with a specified travel time and initial and final velocities such that all speed limits and locomotive capacity constraints are satisfied when there are stations. While the ensuing detailed description is directed to optimizing fuel consumption, it can also be used to optimize other factors described herein, such as but not limited to: emissions. Key flexibility is to accommodate expected dwell times at stations and to account for constraints on earliest arrival and departure from a location, which may be required, for example, in single track operations where time to enter or pass a siding is critical.

An exemplary embodiment of the invention finds a fuel-optimized itinerary with M-1 intermediate stations _{D1 ,} ...,DM _-1 traveling from distance _D0 to DM in time T, and The arrival and departure times at these stations are bounded by:

t _min (i)≤t _arr (D _i )≤t _max (i)-Δt _i

t _arr (D _i )+Δt _i ≤t _dep (D _i )≤t _max (i)i=1,...,M-1

where t _arr (D _i ), t _dep (D _i ), and Δt _i are the arrival, departure, and minimum stop times at the i-th station, respectively. Assuming that fuel optimality means minimizing stopping time, t _dep (D _i ) = t _arr (D _i ) + Δt _i eliminates the second inequality above. Assume that for each i=1,...,M, the fuel-optimized trip from D _i-1 to D _i with travel time t (T _min (i)≤t≤T _max (i)) is known . Let F _i (t) denote the fuel consumption corresponding to the trip. If the travel time from D _j-1 to D _j is denoted by T _j , then the arrival time at D _i is given by:

${\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; arr</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; D.</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\underset{\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; i</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; \&Delta;t</span>}}_{\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; )</span>$

where Δt ₀ is defined as 0. The fuel-optimized itinerary from D ₀ to D _M with travel time T is obtained by finding T _i (i=1,...,M), which minimizes

$\underset{i=1}{\overset{m}{\mathrm{\&Sigma;}}}{f}_i\left(T_i\right)$ T _min (i)≤T _i ≤T _max (i)

obey

$t_{\min }\left(i\right)\&le;\underset{j=1}{\overset{i}{\mathrm{\&Sigma;}}}(T_j+{\mathrm{\&Delta;t}}_{j-1})\&le;t_{\max }\left(i\right)-{\mathrm{\&Delta;t}}_i$ i=1,...,M-1

$\underset{\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; m</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; \&Delta;t</span>}}_{\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; T</span>}$

T_j，j＝i+1，...，M而获得的，其最小化Once the trip is in progress, the problem is to redetermine the fuel-optimal solution for the rest of the trip (originally from D ₀ to D _M in time T ) as the trip progresses, but here the disturbance rejection follows the fuel-optimal s solution. Assume that the current distance and speed are x and v respectively, where D _i-1 <x≤D _i . Also, assume that the current time since the start of the trip is t _act . Then, the fuel-optimal solution for the remaining trip from x to DM preserving the original arrival time at _DM is obtained by finding

T _j , j=i+1,..., M, which minimizes

${\stackrel{<span\; class="notranslate">\; ~</span>}{\mathrm{<span\; class="notranslate">\; f</span>}}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; (</span>{\stackrel{<span\; class="notranslate">\; ~</span>}{\mathrm{<span\; class="notranslate">\; T</span>}}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; v</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; +</span>\underset{\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; m</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; )</span>$

obey

${\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; min</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&le;</span>{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; act</span>}}<span\; class="notranslate">\; +</span>{\stackrel{<span\; class="notranslate">\; ~</span>}{\mathrm{<span\; class="notranslate">\; T</span>}}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; \&le;</span>{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; max</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; \&Delta;t</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}$

$t_{\min }\left(k\right)\&le;t_{\mathrm{act}}+{\tilde{T}}_i+\underset{j=i+1}{\overset{k}{\mathrm{\&Sigma;}}}(T_j+{\mathrm{\&Delta;t}}_{j-1})\&le;t_{\max }\left(k\right)-{\mathrm{\&Delta;t}}_k$ k=i+1,...,M-1

${\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; act</span>}}<span\; class="notranslate">\; +</span>{\stackrel{<span\; class="notranslate">\; ~</span>}{\mathrm{<span\; class="notranslate">\; T</span>}}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; +</span>\underset{\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; m</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; \&Delta;</span>}{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; T</span>}$

here, is the fuel consumption for an optimal trip traveling from x to Di in time t with an initial velocity v at x.

As described above, an exemplary approach to achieve more efficient replanning is to establish an optimal solution for segmented station-to-station trips. For a trip from D _i-1 to D _i with travel time T _i , select a set of intermediate points D _ij , j=1, . . . , N _i -1. Let D _i0 =D _i-1 and ${\mathrm{D.}}_{i_i}={\mathrm{D.}}_i.$ Then, the fuel consumption of the optimal trip from D _i-1 to D _i is expressed as

${\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\underset{\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{{\mathrm{<span\; class="notranslate">\; N</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; ij</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; ij</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; v</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; v</span>}}_{\mathrm{<span\; class="notranslate">\; ij</span>}}<span\; class="notranslate">\; )</span>$

所以 $v_{i0}=v_{i{N}_i}=0.$ Here, f _ij (t, v _{i, j-1} , v _ij ) is the vehicle traveling from D _{i, j- 1} to D with initial and final velocities V i, j-1 and V _ij in time t The fuel consumption of the optimal trip of _ij . Also, t _ij is the time in the optimal trip corresponding to the distance D _ij . By definition, $t_{i_i}-{\mathrm{<figure-callout\; id="0"\; label="t\; i"\; filenames="A20078000118500461.png,A20078000118500471.png"\; state="\{\{state\}\}">t</figure-callout>}}_i=T_i.$ Because the train stops at D _i0 and

so $v_{i0}=v_{i{N}_i}=0.$

The above expressions enable the function F _i (t) to be obtained by first determining the function f _ij (·)(1≤j≤N _i ) and then obtaining τ _ij , 1≤j≤N _i and v _ij , 1≤j<N _i and is alternatively determined, which minimizes

${\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\underset{\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{{\mathrm{<span\; class="notranslate">\; N</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; ij</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; \&tau;</span>}}_{\mathrm{<span\; class="notranslate">\; ij</span>}}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; v</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; v</span>}}_{\mathrm{<span\; class="notranslate">\; ij</span>}}<span\; class="notranslate">\; )</span>$

obey

$\underset{\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{{\mathrm{<span\; class="notranslate">\; N</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{\mathrm{<span\; class="notranslate">\; \&tau;</span>}}_{\mathrm{<span\; class="notranslate">\; ij</span>}}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}$

v _min (i, j) ≤ v _ij ≤ v _max (i, j) j = 1, ..., N _i -1

${\mathrm{<span\; class="notranslate">\; v</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; 0</span>}}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; v</span>}}_{{\mathrm{<span\; class="notranslate">\; i</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 0</span>}$

By choosing D _ij (eg, at a speed constraint or meeting point), v _max (i,j)-v _min (i,j) can be minimized, thereby minimizing the extent to which f _ij () needs to be known.

According to the above division, a simpler sub-optimal re-planning method than above is used to limit the re-planning to the time when the train is at the distance point D _ij , 1≤i≤M, 1≤j≤N _i . At point D _ij , the new optimal itinerary from D _ij to D _M can be obtained by finding τ _ik , j<k≤N _i , v _ik , j<k<N _i , and τ _mn , i<m≤M, 1≤n≤N _m , v _mn , i<m≤M, 1≤n<N _m are determined, and the minimum

$\underset{\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{{\mathrm{<span\; class="notranslate">\; N</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; ik</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; \&tau;</span>}}_{\mathrm{<span\; class="notranslate">\; ik</span>}}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; v</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; v</span>}}_{\mathrm{<span\; class="notranslate">\; ik</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; +</span>\underset{\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; m</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}\underset{\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{{\mathrm{<span\; class="notranslate">\; N</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; mn</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; \&tau;</span>}}_{\mathrm{<span\; class="notranslate">\; mn</span>}}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; v</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; v</span>}}_{\mathrm{<span\; class="notranslate">\; mn</span>}}<span\; class="notranslate">\; )</span>$

obey

${\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; min</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&le;</span>{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; act</span>}}<span\; class="notranslate">\; +</span>\underset{\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{{\mathrm{<span\; class="notranslate">\; N</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{\mathrm{<span\; class="notranslate">\; \&tau;</span>}}_{\mathrm{<span\; class="notranslate">\; ik</span>}}<span\; class="notranslate">\; \&le;</span>{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; max</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; \&Delta;t</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}$

$t_{\min }\left(\mathrm{no}\right)\&le;t_{\mathrm{act}}+\underset{k=j+1}{\overset{N_i}{\mathrm{\&Sigma;}}}{\mathrm{\&tau;}}_{\mathrm{ik}}+\underset{m=i+1}{\overset{\mathrm{no}}{\mathrm{\&Sigma;}}}(T_m+{\mathrm{\&Delta;t}}_{m-1})\&le;t_{\max }\left(\mathrm{no}\right)-{\mathrm{\&Delta;t}}_{\mathrm{no}}$ n=i+1,...,M-1

${\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; act</span>}}<span\; class="notranslate">\; +</span>\underset{\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{{\mathrm{<span\; class="notranslate">\; N</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{\mathrm{<span\; class="notranslate">\; \&tau;</span>}}_{\mathrm{<span\; class="notranslate">\; ik</span>}}<span\; class="notranslate">\; +</span>\underset{\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; m</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; \&Delta;t</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; T</span>}$

here

${\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}}<span\; class="notranslate">\; =</span>\underset{\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{{\mathrm{<span\; class="notranslate">\; N</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{\mathrm{<span\; class="notranslate">\; \&tau;</span>}}_{\mathrm{<span\; class="notranslate">\; mn</span>}}$

A further simplification can be obtained by waiting for the recalculation of T _m , i < m ≤ M, until reaching the distance point D _i . Thus, at a point D _ij between D _i-1 and D _i , the above minimization only needs to be performed on τ _ik , j<k≤N _i , vi _ik , j<k<N _i . T _i is increased as needed to accommodate any actual travel time longer than the planned time from D _i-1 to D _ij . If possible, this increase is then compensated for at the distance point D _i by recalculating T _m , i<m≦M.

With respect to the closed-loop structure disclosed above, the total input energy required to move the train 31 from point A to point B consists of the sum of four components, specifically the difference in kinetic energy between points A and B; points A and B the potential energy difference between them; the energy lost due to friction and other drag losses; and the energy dissipated due to the application of the brakes. Assuming the start and end velocities are equal (eg, stationary), the first component is zero. Also, the second component does not depend on the driving strategy. So this is enough to minimize the sum of the last two components.

A constant velocity profile is followed to minimize drag losses. Following a constant speed profile also minimizes total energy input when braking is not required to maintain a constant speed. However, if braking is required to maintain a constant speed, applying the brakes just to maintain a constant speed will most likely increase the total required energy due to the need to replenish the energy expended by braking. The possibility exists that some braking may actually reduce overall energy use by reducing speed variation if the additional braking losses are more than offset by the overall reduction in braking-induced drag losses.

After re-planning is done based on the collection of events described above, the new optimal gear/speed plan can be followed by using the closed-loop control described herein. In some cases, however, there may not be sufficient time to execute the above-mentioned section breakdown plan, especially when critical speed constraints must be considered, and an alternative is required. The exemplary embodiment of the present invention implements this alternative using an algorithm known as "smart cruise control". The smart cruise control algorithm is an efficient method to generate on the fly an energy efficient (and thus fuel efficient) sub-optimal solution for driving the train 31 over known terrain. This algorithm assumes that the position of the train 31 along the track 34 is always known, and the slope and curvature of the track relative to the position is known. This method relies on a particle model of the motion of the train 31 whose parameters can be adaptively estimated from the aforementioned online measurements of the motion of the train.

The smart cruise control algorithm has three main parts: specifically the modified speed limit profile, which serves as an energy-efficient guide centered on speed limit reduction; the ideal throttle or dynamic brake setting profile, which attempts to minimize speed There is a balance between shifting and braking; the mechanism used to combine the latter two components to generate a gear command utilizes a velocity feedback loop to compensate for mismatches in modeled parameters when compared to real parameters. In an embodiment of the invention, intelligent cruise control can adapt to a strategy of no active braking (i.e. the driver is signaled and assumed to provide the necessary braking), or to a strategy of active braking Variants.

The three exemplary components, relative to a cruise control algorithm that does not control dynamic braking, are: a modified speed limit profile, which serves as an energy-efficient guide centered on speed limit reduction; and a notification signal, which notifies the operator when the brakes should be applied; the ideal throttle profile, which attempts to maintain a balance between minimizing speed changes and informing the operator to apply the brakes; a mechanism employing a feedback loop to compensate for mismatches between model parameters and real parameters.

The exemplary embodiment of the present invention also includes a method for identifying key parameter values of the train 31 . For example, for estimating train mass, a Kalman filter and recursive least squares can be used to detect errors that may develop gradually over time.

Figure 7 depicts an exemplary flowchart of the present invention. As previously discussed, a remote facility, such as dispatch 60 (as disclosed in FIG. 3 ), can provide the information. Such information is provided to executive control element 62 as shown. Also provided to executive control element 62 is locomotive modeling information database 63, information from track database 36 such as, but not limited to, track grade information and speed limit information, estimated train parameters such as, but not limited to, train weight and drag coefficient, and the fuel consumption table from the fuel consumption rate estimator 64 . Execution control element 62 provides information to planner 12 , which has been disclosed in more detail in FIG. 1 . Once the trip plan has been calculated, this plan is provided to a drive advisor, driver or controller element 51 . The trip plan is also provided to the executive control element 62 so that it can compare trips when other new data is provided.

As discussed above, the drive advisor 51 can automatically set the notch power, either a pre-established notch setting or an optimal continuous notch power. In addition to providing speed commands to the locomotive 31, a display 68 is provided so that the operator can view what the planner recommends. The operator can also use the control panel 69 . Via the control panel 69, the operator can decide whether to apply the suggested notch power. To this end, the operator can limit the target or suggested power. That is, the operator always has the final authority on what power setting the locomotive consist will be operating at any time. This includes deciding whether to apply the brakes if the trip plan recommends slowing down the train 31 . For example, if operating in a dark area, or if information from a wayside unit cannot be transmitted electronically to the train, and instead the operator views a visual signal from the wayside unit, the operator, based on the information contained in the track database and Visual signals from roadside units to input commands. Depending on how the train 31 is running, information about the fuel measurement is provided to the specific fuel consumption estimator 64 . Since direct measurements of fuel flow are not usually available in a locomotive consist, all information related to fuel consumed so far during a trip and predictions of future subsequent optimal plans is made using calibrated physical models (such as those developed in used in the optimal plan) to achieve. For example, such predictions may include, but are not limited to, using measured total horsepower and known fuel properties to derive cumulative fuel used.

The train 31 also has a locator device 30, such as a GPS sensor, as discussed above. The information is provided to a train parameter estimator 65 . Such information may include, but is not limited to: GPS sensor data, traction/braking force data, braking status data, speed and any changes in speed data. Information about grade and speed limit information is provided to the executive control element 62 along with train weight and drag coefficient information.

Exemplary embodiments of the present invention may also allow for the use of continuously varying power throughout optimization planning and closed-loop control execution. In conventional locomotives, power is often quantified into eight discrete levels. Modern locomotives are capable of continuous variation in horsepower, which can be incorporated into the aforementioned optimization methods. With continuous power, locomotive 42 is able to further optimize operating conditions, for example, by minimizing auxiliary loads and power transmission losses, and fine-tuning the engine horsepower region for optimum efficiency, or to the point of increased emissions margin. Examples include, but are not limited to: minimizing cooling system losses, adjusting alternator voltage, adjusting engine speed, and reducing the number of drive axles. In addition, the locomotive 42 can use the on-board track database 36 and predicted performance requirements to minimize auxiliary loads and power transmission losses to provide optimal efficiency for target fuel consumption/emissions. Examples include, but are not limited to, reducing the number of drive axles on flat terrain, and pre-cooling locomotive engines before entering tunnels.

Exemplary embodiments of the present invention may also use the on-board track database 36 and predicted performance to adjust locomotive performance, for example to ensure adequate speed when the train approaches hills and/or tunnels. For example, this can be expressed as a velocity constraint at a particular location, which becomes part of the generation of the optimal plan created by solving the equation (OP). Additionally, exemplary embodiments of the present invention may incorporate rules for train maneuvering such as, but not limited to, traction effort ramp rate, maximum braking effort ramp rate. These can be incorporated directly into the equations used to optimize the stroke profile, or alternatively into a closed loop regulator that is used to control power application to achieve a target speed.

In a preferred embodiment, the invention is installed only on the lead locomotive of the trainset. Even though the exemplary embodiment of the present invention does not rely on data or interaction with other locomotives, it can be integrated with group managers (as described in U.S. Patent No. 6,691,957 and Patent Application No. 10/429,596 (owned by the assignee, and both Both are hereby incorporated by reference) disclosed), functionalities and/or group optimizer functionalities are combined to increase efficiency. Interaction with multiple trains is also not excluded, as shown in the scheduling example described here by arbitrating two "independently optimized" trains.

Trains with distributed power systems are able to operate in different modes. One mode is that all locomotives in the train operate at the same gear command. Therefore, if the lead locomotive is commanding electric drive-N8, all units within the train will be commanded to produce electric drive-N8 power. Another mode of operation is "independent" control. In this mode, multiple locomotives or locomotive groups distributed throughout the train can operate with different electric drive or brake power. For example, when a train reaches the top of a hill, the lead locomotive (on the downhill slope) can be put on brakes, while the locomotive in the middle or end of the train (on the uphill slope) can be on electric drive. This is done to minimize the tension on the mechanical couplers that connect the rail cars to the locomotive. Traditionally, operating a distributed power system in "standalone" mode required an operator to manually command each remote locomotive or locomotive consist via a display within the lead locomotive. The system will automatically Operate distributed power systems.

When operating on distributed power, an operator within the lead locomotive is able to control the operating functions of the remote locomotives in the remote group through the control system (eg, distributed power control elements). In this way, when running on distributed power, the operator can command each locomotive consist to operate at a different gear power level (or one set can be in electric drive while the others are in braking), where each locomotive consist is individually The locomotive runs under the same gear power. In an exemplary embodiment, an exemplary embodiment of the present invention is mounted on a train, preferably in communication with a distributed power control element, when the notch power level of the remote locomotive consist is expected to be that recommended by the optimized trip plan, An exemplary embodiment of the present invention will communicate this power setting to the remote locomotive consist for execution. As discussed below, the same applies to braking.

Exemplary embodiments of the present invention may be used in locomotive consist where the locomotives are not adjacent (for example with one or more locomotives at the front and others in the middle and rear of the train). Such a configuration is known as distributed power, where the standard connection between the locomotives is replaced by a radio link or auxiliary cables to couple the locomotives externally. When operating on distributed power, an operator within the lead locomotive is able to control the operating functions of the remote locomotives in the set through a control system (eg, a distributed power control element). In particular, when running on distributed power, the operator can command each locomotive consist to operate at a different gear power level (or one set is powered while the others are braked), where each individual locomotive consist All run at the same gear power.

In an exemplary embodiment, an exemplary embodiment of the present invention is mounted on a train, preferably in communication with a distributed power control element, when the notch power level of the remote locomotive consist is expected to be that recommended by the optimal trip plan , the exemplary embodiment of the present invention will communicate this power setting to the remote locomotive consist for execution. As explained below, the same applies to braking. When operating with distributed power, the aforementioned optimization problem can be enhanced to allow additional degrees of freedom, since each remote unit can be independently controlled relative to the lead unit. The value of this is that additional objectives or constraints related to train internal forces can be incorporated into the performance function, assuming a model reflecting the train internal forces is also included. Accordingly, exemplary embodiments of the present invention may include the use of multi-throttle control to better manage forces within the train as well as fuel consumption and emissions.

In a train utilizing the consist manager, the lead locomotive in a locomotive consist may operate at a different notch power setting than the other locomotives in the consist. The other locomotives in the group operate at the same gear power setting. Exemplary embodiments of the present invention may be used in conjunction with a group manager to command the notch power settings of the locomotives in the group. Thus, according to an exemplary embodiment of the present invention, since the consist manager divides the locomotive consist into two groups: the lead locomotive and the trailing unit, the lead locomotive will be commanded to operate at one power gear and the trailing locomotive will be commanded to operate at another Make sure to run under the gear power. In an exemplary embodiment, a distributed power control element may be a system and/or device that accommodates such operations.

Likewise, when the consist optimizer is used with a locomotive consist, exemplary embodiments of the present invention can be used in conjunction with the consist optimizer to determine notch power for each locomotive in the locomotive consist. For example, assume that the trip plan recommends that the notch power of the locomotive consist be set to 4. Based on the position of the train, the group optimizer will take this information and then determine the notch power setting for each locomotive in the group. In this implementation, the efficiency of setting notch power settings via a communication channel within the train is increased. Furthermore, as discussed above, implementation of such a configuration may be performed using a distributed control system.

In addition, as previously discussed, exemplary embodiments of the present invention can be used for continuous correction and re-planning as to when the trainset uses the brakes based on upcoming items of interest, such as but Not limited to railroad crossings, grade changes, approaching sidings, approaching depot yards, and approaching fuel stations, where each locomotive in the set may require different braking options. For example, if a train goes over a hill, the lead locomotive might have to go into braking, while the remote locomotive that didn't reach the top of the hill might have to stay on electric drive.

8, 9 and 10 depict exemplary illustrations of dynamic displays for use by an operator. As provided in Figure 8, a travel profile 72 is provided. Within this outline, the position 73 of the locomotive is provided. Information such as train length 105 and number of cars in the train 106 is also provided. Also provided are elements 107 on track slope, curvature and wayside elements 108 (including bridge location 109 and train speed 110). Display 68 allows the operator to view such information and see where the train is along the route. Information regarding distance and/or estimated time of arrival at locations such as intersection 112 , signals 114 , speed changes 116 , landmarks 118 , and destinations 120 are also provided. An arrival time management tool 125 is also provided to allow the user to determine the fuel savings achieved during the trip. The operator can change the arrival time 127 and see how this affects fuel savings. As discussed herein, those skilled in the art will recognize that fuel savings is but one illustrative example of an objective that can be viewed using a management tool. To this end, depending on the parameter being viewed, other parameters discussed herein can be viewed and evaluated using administrative tools visible to the operator. The operator is also provided with information on how long the crew has been operating the train. In an exemplary embodiment, time and distance information may be expressed as time and/or distance to a particular event and/or location, or it may provide total elapsed time.

As shown in FIG. 9 , the exemplary display provides information about group data 130 , event and location map 132 , arrival time management tool 134 , and action keys 136 . In this display, similar information as discussed above is also provided. The display 68 also provides action keys 138 to allow the operator to reprogram and disengage the exemplary embodiment 140 of the present invention.

Figure 10 depicts another exemplary embodiment of a display. Typical data for a modern locomotive including air brake status 72, an analog speedometer with digital inset 74, and information about traction in pounds force (or in the case of DC locomotives, traction amps) is visible. An indicator 74 is provided to show the current optimal speed in the plan being executed and an accelerometer graph to supplement the reading in mph/minute. Important new data for optimal plan execution is located in the center of the screen, including a scrolling strip chart 76 showing optimal speed and gear settings versus distance (compared to the current history of these variables). In this exemplary embodiment, a locator element is used to obtain the position of the train. As shown, the location may be provided by identifying how far the train is from its final destination, an absolute location, an initial destination, intermediate points, and/or operator input.

The bar chart provides a forecast of the speed change required to follow the optimal plan, which is useful during manual control, and to monitor plan versus actual during automatic control. As discussed herein, the operator can follow the gears or speeds suggested by the exemplary embodiments of the present invention, such as when in the training mode. The vertical bars give a graph of desired and actual gears, which are also displayed numerically below the bar graph. As discussed above, when utilizing continuous power, the display will simply round to the nearest discrete equivalent, the display may be an analog display to show analog equivalents or percentages or actual horsepower/pull.

Key information about the status of the trip is displayed on the screen and shows the current grade 88 encountered by the train, be it the lead locomotive, the grade encountered elsewhere along the train, or the average grade for the entire length of the train. Also disclosed is the distance traveled so far in the plan 90, the cumulative fuel consumed 92, where or how far the next stop is planned 94, the current and predicted time of arrival 96, the expected time to the next stop. The display 68 also shows the maximum possible time to the possible destinations based on the available calculated plans. If a later arrival is required, a reschedule will be performed. Incremental plan data shows the state of fuel and schedule before and after the current optimal plan. Negative numbers mean less fuel or earlier time than planned, positive numbers mean more fuel or later time than planned, and often trade off in the opposite direction (slowing down to save fuel makes the train late and vice versa) .

These displays 68 always provide the operator with a snapshot of where he is in relation to the currently formulated driving plan. This display is for illustrative purposes only, as there are many other ways to display/communicate this information to the operator and/or dispatch. To this end, the information disclosed above may be intermixed to provide a different display than that disclosed.

Other features that may be included in exemplary embodiments of the present invention include, but are not limited to: allow for data logging logs and reports. This information can be stored on the train and downloaded to an off-board system at some point in time. The downloading can occur by manual and/or wireless transmission. This information can also be viewed by the operator via the locomotive display. The data may include, but is not limited to, information such as operator input, time the system has been running, fuel savings, fuel imbalances within the range of locomotives in the train, off-course train trips, system diagnostic issues (e.g. if the GPS sensor failure).

Since trip planning must take into account allowable crew operating time, exemplary embodiments of the present invention may take such information into account when planning a trip. For example, if the maximum time a worker can work is 8 hours, then the itinerary should be formed to include stops for a new worker to replace the current worker. Such designated stop locations may include, but are not limited to: rail yards, meet/pass locations, and the like. If, as the trip progresses, the trip time may be exceeded, the exemplary embodiment of the present invention may be overridden by the operator to comply with criteria determined by the operator. Ultimately, the operator maintains control of the train's speed and/or operating conditions regardless of the train's operating conditions (eg, but not limited to, high load, low speed, train stretch conditions, etc.).

Using an exemplary embodiment of the present invention, a train may operate under multiple operations. In one concept of operation, exemplary embodiments of the present invention may provide commands for controlling propulsion, dynamic braking. The operator then handles all other train functions. In another concept of operation, exemplary embodiments of the present invention may provide commands for controlling propulsion only. The operator then handles dynamic braking and all other train functions. In yet another concept of operation, an exemplary embodiment of the present invention may include commands for controlling propulsion, dynamic braking and airbrake application. The operator then handles all other train functions.

Exemplary embodiments of the present invention may also be used by notifying operators of actions to be taken on upcoming items of interest. Specifically, the predictive logic of exemplary embodiments of the present invention, continuous correction and re-planning of optimized trip plans, track database, capable of notifying operators of upcoming intersections, signals, grade changes, braking actions, sidings, Railway yards, fuel stations, etc. Such notification can occur audibly and/or through an operator interface.

Specifically, using physics-based planning models, train setup information, on-board track databases, on-board operating rules, position determination systems, real-time closed-loop power/braking controls, and sensor feedback, the system will present to the operator and/or notify the desired action. The notification can be visual and/or audible. Examples include intersections where the notification requires the operator to activate the locomotive horn and/or bell, and the notification of "silent" intersections where the operator is not required to activate the locomotive horn or bell.

In another exemplary embodiment, using the physics-based planning model discussed above, train setup information, on-board track database, on-board operating rules, position determination system, real-time closed-loop power/brake control, and Sensor Feedback As shown in Figure 9, an exemplary embodiment of the present invention may provide the operator with information (such as a gauge on a display) that allows the operator to see when the train will arrive at different locations. The system will allow the operator to adjust the trip plan (target arrival time). This information (actual estimated time of arrival or required information derived off-board) can also be communicated to the dispatch center to allow the dispatcher or dispatch system to adjust the target time of arrival. This allows the system to be quickly tuned and optimized for appropriate objective functions such as trade-offs between speed and fuel consumption.

In general, train operation can be improved based on knowledge of the parameters of the railcars that make up the railcars of the train. These parameters may include weight on rails, number of axles, type and characteristics of couplings, speed limitations, axial loads, friction, wind resistance, wheel axial loads, vertical loads and lateral loads. Individual car parameters can in turn affect train load capacity. For example, a set of lightly loaded cars in the center of the train and a heavily loaded car in the back creates a greater chance of derailment when accelerating or pulling in a curve. Furthermore, knowledge of the total car load allows optimization of speed versus train fuel consumption and train emissions. Likewise, knowledge of rail car parameters can lead to faster dispatch from rail yards.

In another exemplary embodiment, information about cargo data may also be included as rail car parameters. This information may include the quantity and type of shipment. For example, suppose a car is carrying liquid. If the compartment is not full, the movement of the fluid can affect the total force that can be carried on the wheels and coupler. This information can be used to further optimize train operations. Likewise, assume that the carriages carry hazardous materials. Since a certain speed limit constraint may be required, this information can be used to further optimize the train operation. When cargo data is not available, sensors can be used to detect load changes, such as those achieved by a partially liquid-filled delivery compartment. In operation, the train is decelerated and axial load changes are measured. In another exemplary embodiment, a hump (such as found in a hump yard) is introduced into the path of the car, wherein axial load changes are measured. Knowledge of fluid displacement and how it affects axial loads can be taken into account to ensure maximum acceleration and deceleration limits are established by the train optimization procedure disclosed above.

In another exemplary embodiment, where sensors are used to detect axial loads, unexpected changes in axial loads may indicate unexpected cargo movement. A signature analysis system is used to distinguish liquid displacement from movement of fixed cargo (such as but not limited to loose boxes). The signature analysis system will detect it as having a high frequency spectrum, while liquid motion will appear as a wider frequency spectrum.

Automatic provision of car properties and manifests for train compositions can advantageously reduce yard setup time and allow overall rail yard network optimization where trade-offs can be made in setup time where cars are placed in an optimal manner are sequenced and often have to be reordered, thus increasing station time. The total setup time can be traded off against the train running time. Providing car characteristics allows the generation of a train manifest with details about crew, scheduling and unloading issues and car characteristics. Provides carriage characteristics allowing load characteristics of train carriages to be formulated as weight, drag, curve performance, allowing train handling with and without DP to be optimized, allowing carriages to fit together in trains and ordering stations to improve networking Traffic capacity, allowing car performance to be input as a limiting factor for train speed by adjusting train handling parameters such as acceleration, deceleration and speed to allow for a reduced chance of derailment for the sake of knowledge of car load and performance / Fuel optimization and/or improved emissions, which may enable one-man operation and allow optimization of cruise control.

Disclosed herein are rail car characterization systems and methods that automatically determine car parameters such as weight, load, wheel axial force, lateral force, and vertical force. The rail car parameters may be provided on the road and/or on the siding during the make-up of the train in a railway yard, such as a hump yard. Rail car parameters can be used for train manifest characterization and can be coupled to rail efficiency tools (such as cruise controllers) to allow for acceleration and/or improved emissions, deceleration, improved train handling with DP or non-DP trains fuel/speed optimization. Rail car parameters may allow optimization of train fuel versus speed by taking into account train handling constraints such as for DP and non-DP operation which may limit speed, acceleration and deceleration. The railcar parameters may allow determination of a trade-off between cross-track train delivery time and yard train makeup time in order to improve overall cargo delivery efficiency. Railcar parameters can be used to provide railroad manifest data that includes car performance characteristics in response to weight, lateral, axial, and vertical axis loads, and car forces. As described herein, car parameter determination may be performed automatically.

Certain types of carriages may be affected by wind and/or air resistance. For example, an empty lumber carrying car has a large surface that acts as a sail affecting the motion of the car. In one aspect, the measured railcar parameters may include determining a drag factor of the railcar. Thus, the car type and the corresponding windage factor and/or windage measurement parameter may be included in the measurement data during the measurement collection process, for example via a visual observer of the trackside car.

In the exemplary embodiment shown in FIG. 1 , rail car parameters for a rail car 200 may be obtained at a rail yard 205 where a predetermined track portion 216 (such as a straight or curved portion) is traversed. , parameters such as cabin weight and wheel forces can be measured. A rail car parameter measurement system 215 (or measurement system) is provided and may include on-board sensors 220 and/or off-board sensors 225, such as force sensors, for measuring rail, wheel, suspension, and/or combinations thereof on the force. Other sensors may include deflection detectors, springs, laser gauges, and the like. The measurement system 215 may include a data collection module 230 for collecting measurement data and may communicate with a central controller 240 (such as a car dispatch system, train composition/manifest system) via a transceiver 235 and may provide the measurement data to a network Scheduling system and train speed/fuel efficiency optimization system. The measurement system may also provide measurement information to and communicate with portable data collection unit 245 , locomotive 250 via locomotive transceiver 255 , wayside electronics unit 260 , and off-board sensors 255 . These systems can share information together in a communication network, where these elements can communicate with each other to exchange information. Such communication may be provided by wireless coupling, such as via radio frequency (RF) and infrared communications, or may be provided by hard-wired means, such as a hard-wired connection between the railcar and locomotive 250 .

In another embodiment, the measurement system 215 may be implemented outside the railway yard, for example on a side track remote from the railway yard. Thus, measurement data can be taken after the train leaves the railway yard 205 and can be fed to the network 265 and the central controller 240 after the train has been assembled in the yard for further action.

In one aspect of the invention, the central controller 240 may include a processor 270 for processing measurement data provided to the train to provide acceleration/deceleration optimization factors to ensure train operating parameters take these constraints into account. In case of optimizing the composition of the car, the acceleration/deceleration can be increased to reduce the mission time, given the additional parameters used for optimization. In the case of a distributed power (DP) train, the normally reduced tractive effort (TE) of the rear unit of the train is better matched to that of the front unit due to the measurements provided for the train's railcars.

In another exemplary embodiment, a feature analysis system 300 is provided. The system 300 may be part of or be in communication with the controller 240 . As discussed above, the signature analysis system 300 can be used to distinguish between liquid displacement and moving stationary cargo.

Additionally, by comparing the current data to previously measured data, previously obtained measurement data can be used to determine abnormal car performance, such as high wheel friction that may be attributable to bearing problems. This allows maintenance to be performed before the carriages are coupled to the train.

Advantageously, the system may provide the ability to trade off station train composition time versus train run time, the ability to optimize power settings for locomotives for a train (e.g., DP train), Power The ability to add more compartments, the ability to optimize fuel/speed settings, and provide compartment diagnostics such as excessive friction, wheel flat spots, and more.

In one aspect of the invention, the on-board sensors may include weight sensors, such as scales or spring deflection sensors on railcars. The off-board sensors 225 may include force gauges located on the track 210 on which the rail cars travel. In one aspect of the invention, when a car is accelerated or decelerated on the track with a known frictional load (deceleration) or applied power (acceleration) (determined, for example, by utilizing accelerometers on the rail car), Can measure weight.

For curved rail measurement sections, on-board sensors 220 may include force detectors, such as vertical, axial and/or horizontal force gauges, to detect wheel forces. In another embodiment, on-board sensors 220 may include yaw (motion) sensors that monitor the wheels while going around a curve (e.g., to observe a decrease in speed while going around a curve); A thermal sensor that detects the heat dissipated while traveling around a curve. Off-board sensors 225 may include dynamometers located on rails 210 to detect deflection of the rails and/or dynamometers located on switches on which railcars travel.

For straight rail measurement sections, on-board sensors 220 may include force detectors to detect wheel forces, such as vertical, axial and/or horizontal force gauges or accelerometers. Off-board sensors 225 may include dynamometers located on the rails, deflection/accelerometers of the rails, and/or dynamometers located on the switches. In another embodiment, the on-board sensors 220 may include yaw (motion) sensors that monitor the wheels while traveling on straight rails (calculated by observing the decrease in speed while traveling around a predetermined straight line), and may include A thermal sensor that detects the heat dissipated while driving.

Data collection may be performed by on-board data collection system 230 or may be performed remotely, such as by utilizing portable data collection unit 245 . Network 265 may include one or more of the following types of networks: wired, wireless, real-time, batch data transfer, store-and-forward, data push (when data is available), data pull (request action prompts in real-time or delayed), and /or manual entry.

In other embodiments, via electronic and hard coupled to central controller 240 (e.g., station/dispatch system), electronic with manual linkage, ) and electronics for manual entry to another system, mechanical and hardware coupled to the depot/dispatch system, mechanical with manual coupling, mechanical with readout and/or manual entry to other systems , can provide a data readout of measurement data from the system. The rail car identification for use by the system may be entered into the system manually or via a rail car identifier 280 which may include electronic tags, radio frequency (RF) tags, and/or barcodes.

In other exemplary embodiments, the survey system configuration may include: manual station scheduling using electronic data entry, manual station scheduling using manual data entry, electronic station scheduling using manual data entry, electronic Electronic station dispatching for data entry, manual manifest composition, electronic manifest composition, manual network system using data from manual station system calculation manual (manual)/search/electronic, using data from electronic station system calculation manual/search /Electronic data artificial network system, electronic network system using data from artificial station system calculation manual/lookup/electronic, electronic network system using data from electronic station system calculation manual/lookup/electronic, electronic network system using data from artificial station system calculation manual/lookup/electronic Manual/Lookup/Electronics Manual Trip Optimizer Using Data From Electronic Station System Calculations Manual/Lookup/Electronic Manual/Lookup/Electronic Using Data From Manual/Lookup/Electronics Electronic Trip Optimizer using data from Electronic Yard System Calculation Manual/Lookup/Electronic, using data from Road/Side Track/Switch System Calculation Manual/Lookup/Manual on Electronics Manual trip optimizer using data from the road/side rail/point switch system calculation manual/lookup/electronics, using data from the road/side rail/point switch system calculation manual/lookup Electronic trip optimizer using data from the manual on /electronic, and/or electronic trip optimizer using data from the road/side track/pointer system calculation manual/lookup/electronic on the electronic.

Measurement data obtained by the system can be used to control train movement to limit operating parameters, equalize operating parameters, relax parameter constraints, optimize operating parameters before/for setup of a trip, optimize operating parameters in real time, optimize operating parameters for the entire trip, optimize Operating parameters for partial trips, optimizing operating parameters using a single input, optimizing operating parameters using multiple sets of data inputs, and/or optimizing diagnostics and car maintenance.

Data sources for storing data into the system may include wayside electronics unit 260, car electronics (such as data collection unit 230), locomotive 250, and/or central controller 240 (such as a yard system or dispatch system). Data receivers for receiving measurement data may include off-line hosts, on-line locomotive systems, on-line station systems, off-line station systems, on-line dispatching systems, off-line dispatching systems, on-line devices, off-line side devices, and on-line network optimizers , offline network optimizer and/or billing system.

Optimization techniques that can be used to process the measured data to produce optimized operating parameters can include successive approximation relaxation methods, sequential Taylor series, neural networks, transformations, empirically based look-up tables, techniques based on first principles of mechanics and/or Kalman filtering.

In an exemplary embodiment, a remote location is provided, such as, but not limited to, a regional and/or national center 310 that maintains a national database 320 of railcar information. This nationwide database 320 can be used as a resource for train construction. It can also be used for model analysis. Due to the type of information provided, this database 320 may also be used by government agencies to address shipping requirements and/or safety issues. Information from the national database is passed between itself and the controller 240 . The information in national database 320 is updated as new information is obtained from controller 240 . Communications between controller 248 and national database 320 may be secured, such as, but not limited to, encryption and/or authentication techniques. In another exemplary embodiment, there may be multiple regional databases in communication with each other as disclosed above with respect to national databases in communication with controller 240 . In an example embodiment, the network 265 through which communications occur may be protected from surreptitious attacks from external agents.

FIG. 2 depicts a flowchart showing steps for identifying rail car parameters for improved train operation. Those skilled in the art will readily recognize that the steps in flowchart 330 can be performed automatically and/or spontaneously. These steps include determining railcar parameters of the railcars of the train (step 335), and creating a train trip plan based on the railcar parameters according to at least one operating criterion of the train (step 340). An additional step includes locating a railcar within the train based on at least one railcar parameter. Additionally, as discussed above, the order of the railcars in the train may be determined based on the railcar parameters. Also as discussed above, these steps (steps 335 and/or 334) may be implemented using computer software code and/or another processor implementation technique.

In another exemplary embodiment, wayside automatic equipment identification (AEI) tag readers as part of a rail sorting system are used to read information (typically manifest information) from railcars. Rail sorting systems require reliable manifests for the task of sorting and forwarding railcars. Most systems retrieve these manifests from a database via the corporate network. After a train is built, the list of cars in that train is uploaded into the database by depot personnel and/or the AEI system. Thus, information can be read from rail vehicles as the train passes the AEI tag reader (typically once the entire train has passed). In an exemplary embodiment, the read information is communicated to the locomotive, and more particularly to the trip optimizer, where the information is used to update the trip plan and/or to create future trip plans. Information can be updated as railcars are added and/or removed at predetermined destinations.

Although the present invention has been described with reference to the exemplary embodiments, it will be understood by those skilled in the art that various changes, omissions and/or additions can be made without departing from the spirit and scope of the present invention, and can be used Equivalents replace elements of the invention. In addition, many modifications may be made to adapt a particular situation or material to the teachings of the invention without departing from its scope. Therefore, it is intended that the invention not be limited to the particular embodiment disclosed as the best mode contemplated for carrying out this invention, but that the invention will include all embodiments falling within the scope of the appended claims. Furthermore, unless expressly stated, any use of the terms first, second, etc. do not denote any order or importance, but are used to distinguish one element from another.

Claims (36)

1. A method for improving train performance, said method comprising: a) determining rail car parameters for at least one rail car to be included in the train; and b) creating a train trip plan based on the rail car parameters according to at least one operating criterion of the train. 2. The method of claim 1, wherein the railcar parameters include at least one of weight, axial load, friction, wind resistance, wheel axial load, vertical load, and lateral load. 3. The method of claim 1, further comprising determining a position of a rail car within the train based on at least one rail car parameter. 4. The method of claim 1, further comprising optimizing at least one of speed-to-train fuel consumption and speed-to-train emissions output based on at least one railcar parameter. 5. The method of claim 1, further comprising transmitting the rail car parameter data to a remote central database. 6. The method of claim 5, further comprising securing communications to and from the remote central database. 7. The method of claim 2, further comprising determining whether the change in axial load is caused by at least one of liquid displacement and displaced stationary cargo. 8. The method of claim 1, further comprising reducing depot setup time for the train based on rail car parameters. 9. The method of claim 1, further comprising determining load characteristics of the railcars in the train based on at least one of weight, drag, and curve performance. 10. The method of claim 1, further comprising optimizing operational performance of at least one of a distributed power train and a non-distributed power train based on rail car parameters. 11. The method of claim 1, further comprising providing a plurality of railcars, wherein an order in which the railcars are arranged in the train is based on a railcar parameter. 12. The method of claim 1, further comprising operating the train according to the created trip plan. 13. The method of claim 1, wherein the step of determining rail car parameters further comprises determining rail car parameters by traversing predetermined track sections. 14. The method of claim 1, further comprising determining the rail car data by reading information contained on an identification tag attached to the rail car. 15. A computer software code for use in a processor to improve train performance, said computer software code comprising: a) a computer software module for determining rail car parameters of at least one rail car of a train; and b) A computer software module for creating a train journey plan from rail car parameters according to at least one operating criterion of the train. 16. The computer software code of claim 15, wherein the rail car parameters include at least one of weight, axial load, friction, wind resistance, wheel axial load, vertical load, and lateral load. 17. Computer software code according to claim 15, further comprising a computer software module for determining the position of the rail car within the train from at least one rail car parameter. 18. The computer software code of claim 15, further comprising a computer software module for optimizing at least one of speed-to-train fuel consumption and speed-to-train emissions output as a function of railcar parameters. 19. The computer software code of claim 15, further comprising a computer software module for transmitting rail car parameter data to a remote central database. 20. Computer software code according to claim 19, further comprising computer software modules for securing communications to and from said remote central database. 21. The computer software code of claim 16, further comprising a computer software module for determining whether the change in axial load is caused by at least one of liquid displacement and displaced stationary cargo within the rail car. 22. The computer software code of claim 15, further comprising a computer software module for determining load characteristics of a car in a train based on at least one of weight, drag and curve performance. 23. The computer software code of claim 15, further comprising a computer software module for optimizing operational performance of at least one of a distributed power train and a non-distributed power train based on rail car parameters. 24. The computer software code of claim 15, further comprising providing a plurality of railcars, wherein the computer software module determines the order of the railcars in the train based on the railcar parameters. 25. The computer software code of claim 15, further comprising a computer software module for operating the train according to the created trip plan. 26. The computer software code of claim 15, further comprising a computer software module for determining the rail car data by reading information contained on an identification tag attached to the rail car. 27. A system for improving train performance by determining rail car parameters, the system comprising: a) Rail car parameter measurement system; b) central controller; c) a communication network to allow communication between the measurement system and the central controller; d) wherein the railcar parameters are measured and provided to a central controller which determines at least one of a train composition profile and a trip plan for a train mission based on the railcar parameters for all railcars within the train . 28. The system of claim 27, wherein the parameter measurement system further includes at least one of on-board sensors and off-board sensors for determining rail car parameters. 29. The system of claim 27, wherein the on-vehicle and off-vehicle sensors determine at least one of weight, axial load, friction, wind resistance, wheel axial load, vertical load, and lateral load. 30. The system of claim 27, further comprising at least one of a remote database and a portable data collection unit. 31. The system of claim 27, wherein the communication network provides communication between at least two of a measurement system, a central controller, a remote database, a portable data collection unit, a wayside unit, an off-board sensor, and a locomotive . 32. The system of claim 30, wherein the communication network is a protected communication network. 33. The system of claim 27, wherein the central controller further comprises a processor. 34. The system of claim 30, wherein the remote database includes rail car parameters for a plurality of rail cars. 35. The system of claim 27, further comprising: a) the identification mark attached to the rail car; b) an identification mark reader positioned proximate to the track on which the railcar is located so as to communicate with said mark to gather information from said mark; c) wherein the central controller communicates with the identification mark reader and the information associated with the identification mark is used to determine the identity of the rail car. 36. The system of claim 35, wherein information associated with the identification marker is provided to the trip optimizer for use in determining the trip plan.

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