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CN112124299B - A hierarchical energy consumption optimization method for intelligent network-connected new energy vehicles - Google Patents

A hierarchical energy consumption optimization method for intelligent network-connected new energy vehicles Download PDF

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CN112124299B
CN112124299B CN202010958502.0A CN202010958502A CN112124299B CN 112124299 B CN112124299 B CN 112124299B CN 202010958502 A CN202010958502 A CN 202010958502A CN 112124299 B CN112124299 B CN 112124299B
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vehicle
energy
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energy consumption
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CN112124299A (en
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彭剑坤
王勇
李志斌
张海龙
谭华春
丁璠
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Southeast University
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W20/00—Control systems specially adapted for hybrid vehicles
    • B60W20/10—Controlling the power contribution of each of the prime movers to meet required power demand
    • B60W20/12—Controlling the power contribution of each of the prime movers to meet required power demand using control strategies taking into account route information
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L15/00—Methods, circuits, or devices for controlling the traction-motor speed of electrically-propelled vehicles
    • B60L15/20—Methods, circuits, or devices for controlling the traction-motor speed of electrically-propelled vehicles for control of the vehicle or its driving motor to achieve a desired performance, e.g. speed, torque, programmed variation of speed
    • B60L15/2045—Methods, circuits, or devices for controlling the traction-motor speed of electrically-propelled vehicles for control of the vehicle or its driving motor to achieve a desired performance, e.g. speed, torque, programmed variation of speed for optimising the use of energy
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W20/00—Control systems specially adapted for hybrid vehicles
    • B60W20/10—Controlling the power contribution of each of the prime movers to meet required power demand
    • B60W20/15—Control strategies specially adapted for achieving a particular effect
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/60—Other road transportation technologies with climate change mitigation effect
    • Y02T10/72—Electric energy management in electromobility
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/80—Technologies aiming to reduce greenhouse gasses emissions common to all road transportation technologies
    • Y02T10/84—Data processing systems or methods, management, administration

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  • Engineering & Computer Science (AREA)
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  • Automation & Control Theory (AREA)
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Abstract

The invention relates to a hierarchical energy consumption optimization method for an intelligent networked new energy automobile, wherein a decision planning layer comprises path planning, action planning and trajectory planning, and the three plans are respectively combined with low energy consumption planning to realize multi-objective collaborative optimization; the vehicle control layer performs energy management on the whole power assembly system through an energy management module on the premise of meeting the decision planning layer; the invention optimizes the energy consumption of the whole automobile into two layers, namely a low-energy-consumption planning task in an intelligent driving domain and an energy management task in a power assembly domain, thereby realizing the layered energy consumption optimization of the intelligent networked new energy automobile.

Description

Intelligent networking new energy automobile layered energy consumption optimization method
Technical Field
The invention relates to a hierarchical energy consumption optimization method for an intelligent networked new energy automobile, in particular to a hierarchical energy consumption optimization method for coordinating low energy consumption planning of an intelligent driving system and energy management of a power system of the new energy automobile.
Background
With the rapid development of the intelligent networked automobile technology, the architecture of the intelligent networked automobile is divided into three layers, wherein the first layer is an environment sensing layer, and complex traffic environment information is sensed and acquired through a vehicle-mounted sensor and networked equipment; the second layer is a decision planning layer, and the intelligent automobile driving system plans a driving instruction for a period of time in the future by understanding the environment information, so that the obstacle avoidance and the execution of the traffic rules are carried out, and the traffic efficiency can be improved; the third layer is a control layer which executes the instructions of the decision planning layer to realize safe driving. Simultaneously, new energy automobile adopts novel driving system as one kind, provides or the car of auxiliary drive power with driving motor, compares traditional fuel automobile, and driving system is more high-efficient.
The new energy automobile is used as a carrier of the intelligent driving system, so that the improvement of energy conservation and emission reduction level, traffic efficiency and safety can be effectively promoted. The development trend of intellectualization and electrification enables an automobile to carry a large number of electronic controllers, the electronic and electric architecture of the automobile gradually develops from the traditional distributed control to the domain control direction, and each part of functions of the automobile electronics can be divided into several fields by the domain controller, such as a power assembly domain, an intelligent cabin domain, an intelligent driving domain and the like. By combining the low-energy-consumption planning of the intelligent driving domain with the energy management of the power assembly domain and implementing the collaborative energy consumption optimization, the actual driving energy consumption of the automobile can be further reduced while the driving safety and the traffic efficiency are met.
Disclosure of Invention
The invention provides a hierarchical energy consumption optimization method for an intelligent networked new energy automobile, which divides the whole automobile energy consumption optimization into two layers, namely a low-energy consumption planning task in an intelligent driving domain and an energy management task in a power assembly domain, so that the hierarchical energy consumption optimization of the intelligent networked new energy automobile is realized.
The technical scheme adopted by the invention for solving the technical problems is as follows:
a hierarchical energy consumption optimization method for an intelligent networked new energy automobile comprises a sensing layer, a decision planning layer and a vehicle control layer, wherein the sensing layer transmits acquired information to the decision planning layer, and the decision planning layer transmits a planning result to the vehicle control layer;
the decision planning layer comprises path planning, action planning and track planning, and the three plans are respectively combined with low-energy-consumption planning to realize multi-objective system optimization;
the vehicle control layer performs energy management on the whole power assembly system through an energy management module on the premise of meeting the decision planning layer;
as a further preferred aspect of the present invention, the step of combining the path planning and the low energy consumption planning specifically includes:
step 11, obtaining a path planning objective function of the intelligent networking new energy automobile
Figure BDA0002679553110000021
Wherein eff (i) represents an efficiency cost item of the ith waypoint, saf (i) represents a driving safety item, sta (i) represents a global reference path stability cost item, and omega is each coefficient;
step 12, combine the path planning with low power consumption planning to obtain a new objective function of
Figure BDA0002679553110000022
Wherein eff (i) represents an efficiency cost item of the ith waypoint, saf (i) represents a driving safety item, sta (i) represents a global reference path stability cost item, cost (i) represents energy consumption consumed between the ith waypoint and the (i-1) th waypoint, and omega is each coefficient;
as a further preferred embodiment of the present invention, the step of combining the action plan and the low energy consumption plan specifically includes:
by giving weight coefficients to a plurality of targets, the objective function of the motion planning and low-energy consumption planning of the vehicle is obtained as
Figure BDA0002679553110000023
Wherein xeffIndicating road traffic efficiency, xsafRepresenting a traffic safety factor, cost representing the energy consumption of driving and the whole traffic, and lambda being each coefficient;
as a further preferred embodiment of the present invention, the step of combining trajectory planning and low energy consumption planning specifically includes:
step 31, obtaining a dynamic model of the vehicle
Figure BDA0002679553110000024
Figure BDA0002679553110000025
Figure BDA0002679553110000026
Figure BDA0002679553110000027
Figure BDA0002679553110000028
Wherein, FcfIndicating the lateral force to which the front wheels of the vehicle are subjected, FcrRepresenting the longitudinal force to which the front wheels of the vehicle are subjected, FlfIndicating the lateral force to which the rear wheel of the vehicle is subjected, FlrRepresenting the longitudinal force to which the rear wheels of the vehicle are subjected, psi representing the yaw rate, (X, Y) representing the current coordinates of the body, (X, Y) representing the current coordinates in the global coordinates of the vehicle, deltafFor the front wheel angle of the vehicle, IzThe moment of inertia of the vehicle body rotating around the vertical direction under a vehicle coordinate system, and m represents the mass of the vehicle;
step 32, obtaining the whole power assembly model
Figure BDA0002679553110000029
Where m denotes the vehicle mass, δ denotes the mass coefficient, CDRepresenting an air resistance coefficient, A representing a windward area, g representing a gravitational acceleration, and alpha (t) representing a gradient;
step 33, obtaining an optimized objective function of the trajectory planning and the low energy consumption planning
Figure BDA0002679553110000031
WhereinThe formula (10) is required to satisfy-deltacar≤δf≤δcar，0≤Preq≤Pcar，δcarIndicates the maximum front wheel angle, P, of the vehiclecarThe maximum output power of the automobile is represented, smo (i) represents the smoothness of the track, Gui (i) represents the deviation degree of the actual track and the reference track, obs (i) represents the danger degree of the obstacle to the track, e (i) represents the energy consumption of the track, and t represents each coefficient;
as a further preferred embodiment of the present invention, energy management of the entire powertrain system is performed on the premise that a decision planning layer is satisfied, and since the new energy vehicle includes an internal combustion engine, an electric motor, and a plurality of multi-motor drive systems driven by coupling of the electric motor, a power balance equation of the drive systems is as follows
Prep＝Pe+Pm+Pb (11)
Wherein, PreqRepresenting the power balance equation, PeDenotes an internal combustion engine, PmDenotes a drive motor, PbIndicating feedback braking;
in the formula (11), the main power source of the multi-motor drive system is a plurality of drive motors, so that the power balance equation of the drive system is
Prep＝Pm1+Pm2+Pb (12)
Wherein, Pm1、Pm2Representing a plurality of drive motors, PbIndicating feedback braking;
then, a high-efficiency interval of the distribution of the working points of the driving motor and other power sources is obtained
Fuel＝σENG(ωENG,TENG) (13)
E＝σMG1(ωMG,TMG) (14)
Wherein Fuel represents the instantaneous Fuel consumption of the engine, ωENGIndicating the rotational speed, T, of the engineENGRepresenting the torque of the engine, E representing the instantaneous power consumption of the electric machine, omegaMGIndicating the rotational speed, T, of the motorMGRepresenting the torque of the motor; finally obtaining an energy management optimization objective function of the whole power assembly system
Figure BDA0002679553110000032
Wherein, JeThe energy cost function is represented, E (t) represents the power consumption of the whole power assembly system, Fuel (t) represents the oil consumption in time t, and P (t) represents the power consumption of other electronic systems in the new energy automobile.
Through the technical scheme, compared with the prior art, the invention has the following beneficial effects:
1. the method takes the energy consumption of the automobile as one of optimization targets of an intelligent networked automobile decision planning layer under the condition of not increasing extra hardware cost and a small amount of computing resources, and realizes low-energy-consumption planning;
2. the power assembly system disclosed by the invention realizes more refined energy management by reasonably utilizing energy consumption planning information, and improves the vehicle dynamic property, the operation comfort and the energy utilization efficiency.
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The invention is further illustrated with reference to the following figures and examples.
FIG. 1 is a schematic structural diagram of energy consumption optimization of an intelligent networked new energy automobile provided by the invention;
fig. 2 is a hierarchical control architecture for energy consumption optimization of the intelligent networked new energy vehicle provided by the invention.
Detailed Description
The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic views illustrating only the basic structure of the present invention in a schematic manner, and thus show only the constitution related to the present invention.
In order to further reduce the actual running energy consumption of the automobile, the application aims to provide an intelligent networking new energy automobile layered energy consumption optimization method, as shown in fig. 1, an information intercommunication mechanism is established between an intelligent driving domain and a power assembly domain, and state parameters of an automobile power assembly are sent to an intelligent sensing and decision upper layer system, so that an automobile decision planning layer takes the energy consumption as one of optimization targets, and low energy consumption planning is realized; and finally, the power system performs energy management based on low energy consumption planning.
As shown in fig. 2, in the optimization method provided by the present application, the entire system based on the optimization method includes a sensing layer, a decision planning layer and a vehicle control layer, and the intelligent driving domain control system obtains traffic environment information, such as traffic light status, real-time traffic flow, distance between front and rear vehicles, driving scene, road status and the like, through a vehicle-mounted sensor, a high-precision map, a vehicle-mounted communication device and the like; the traffic information is used for planning a track and a path of a future period of time, and safe driving of the intelligent internet automobile under the unmanned condition can be realized. In addition, a control system of the power assembly domain can also acquire power system information such as battery state, power demand, power output and the like in real time; some traffic information influences the energy consumption of the automobile, and low energy consumption can be used as one optimization target in a decision planning layer by comprehensively analyzing the traffic environment information and the power system information, so that multi-objective optimization is realized.
And then the vehicle control layer carries out energy management of the whole power assembly system through an energy management module on the premise of meeting the decision planning layer, and the energy management function of the new energy vehicle is to scientifically and reasonably distribute energy to the power system according to the actual operation condition of the vehicle and the difference of the characteristics and strategies of vehicle parts while ensuring the dynamic property and the operation comfort of the vehicle so as to improve the energy utilization rate of the vehicle. Therefore, the working condition information is the premise of energy management, and the power system can realize more efficient energy management, energy conservation and emission reduction on the basis of low-energy-consumption planning.
Specifically, the hierarchical control architecture for energy consumption optimization of the intelligent networked new energy vehicle is shown in fig. 2, and firstly, a sensor, networking equipment and a map which are installed on the intelligent networked new energy vehicle sense the surrounding environment to obtain a digital traffic environment model; according to the traffic environment model, the method can be used for making related decision planning tasks to obtain a decision instruction for the future driving of the intelligent networked automobile, and specifically comprises the following steps: path planning, action planning and trajectory planning considering low energy consumption planning; and finally, realizing vehicle control and task execution according to the vehicle state obtained by the current vehicle model, wherein the control of the transverse and longitudinal motion of the automobile chassis is realized through trajectory tracking control, and the energy-saving control is realized through energy management of a vehicle power assembly system.
Based on the above, the present application performs specific optimization operations:
firstly, combining path planning with low-energy-consumption planning, wherein the path planning of the intelligent network new energy automobile refers to planning an effective path which is free of collision and can safely reach a target point according to performance indexes after an automobile starting point and the target point are given based on information of a global map database. The performance indexes comprise safety, efficiency, stability and other indexes of the vehicle, which are a multi-objective optimization problem, so that a path planning objective function of the intelligent networked new energy automobile is obtained firstly
Figure BDA0002679553110000051
Wherein eff (i) represents an efficiency cost item of the ith waypoint, distance information and time spent are used as indexes for evaluating the efficiency, and the distance and time cost are expected to be as small as possible in the optimization process; saf (i) represents a safety item for driving, which considers whether the path interferes with the impassable area, the farther the vehicle is from the impassable area representing the safer the vehicle; sta (i) represents a global reference path stability cost term, the global reference path should satisfy vehicle kinematics constraint; ω is the coefficient of each term;
then, the path planning is combined with the low-energy-consumption planning to obtain a new objective function of
Figure BDA0002679553110000052
The method comprises the following steps of (1) obtaining an efficiency cost item of an ith road point, (i) representing an efficiency cost item of the ith road point, (sa) representing a driving safety item, (i) representing a global reference path stability cost item, (i) representing energy consumption consumed between the ith road point and an i-1 th road point, and expecting that an optimal driving path selected by the intelligent networking new energy automobile meets the minimum energy consumption; ω is the coefficient of each term.
And secondly, combining the action planning with low-energy consumption planning, wherein the action planning tasks of the intelligent network-connected new energy automobile comprise the actions of driving in and out at a ramp, turning and straight-going at a road intersection, charging and oiling, lane following, vehicle formation, following, lane changing or overtaking and the like. The planning algorithm is generally based on traffic safety, road traffic capacity, travel time reliability, driving comfort, intersection traffic capacity. The method combines the action planning of the vehicle with the low energy consumption planning, can further realize the optimization of traffic energy consumption, and particularly has great influence on the energy consumption of the vehicle by how to take a proper action under the urban traffic environment, the traffic light intersection and the traffic jam driving environment.
By giving weight coefficients to a plurality of targets, the objective function of the motion planning and low-energy consumption planning of the vehicle is obtained as
Figure BDA0002679553110000053
Wherein xeffIndicating road traffic efficiency, xsafRepresenting a traffic safety factor, cost representing the energy consumption of driving and the whole traffic, and lambda being each coefficient; under the intelligent network traffic environment, action instructions of the vehicles are issued to the vehicles by the cloud control platform, the operation control platform obtains a series of instructions according to macroscopic traffic information, and partial instructions are decided by the vehicles according to the current environment.
And thirdly, combining the trajectory planning with low-energy-consumption planning, wherein the trajectory planning of the intelligent networking new energy automobile refers to the planning of the driving trajectory of the automobile by accurately, reliably and efficiently sensing the driving state and the environmental information of the automobile in a small range and combining the dynamic performance and the time factor of the automobile. The planned trajectory comprises a time-dependent speed, acceleration, travel time status, curvature of the trajectory; and selecting an optimal motion track from the generated motion feasible track cluster, and considering both the driving safety and the comfort of the vehicle. And finally, the track information is transmitted to a motion control system, and after the motion control system receives the detailed information of the planned track, the posture of the vehicle is controlled to enable the vehicle to run along the planned track, so that the aim of automatically controlling the intelligent vehicle is fulfilled.
The trajectory planning is combined with the low-energy-consumption planning, the optimization target is to find an optimal speed trajectory to enable the safety, comfort and energy consumption economy of the vehicle in the whole time domain to be maximum, and the planned trajectory can generate an optimal trajectory containing states of speed, acceleration, time, trajectory curvature, required power and the like. Since the overall vehicle energy consumption needs to be comprehensively considered, the controlled object needs to consider a vehicle dynamics model and a powertrain model in addition to a basic vehicle kinematics model.
First consider a simple vehicle dynamics model
Figure BDA0002679553110000061
Figure BDA0002679553110000062
Figure BDA0002679553110000063
Figure BDA0002679553110000064
Figure BDA0002679553110000065
Wherein, FcfIndicating the lateral force to which the front wheels of the vehicle are subjected, FcrRepresenting the longitudinal force to which the front wheels of the vehicle are subjected, FlfIndicating the lateral force to which the rear wheel of the vehicle is subjected, FlrRepresenting the longitudinal force to which the rear wheels of the vehicle are subjected, psi representing the yaw rate, (X, Y) representing the current coordinates of the body, (X, Y) representing the current coordinates in the global coordinates of the vehicle, deltafFor the front wheel angle of the vehicle, IzThe moment of inertia of the vehicle body rotating around the vertical direction under a vehicle coordinate system, and m represents the mass of the vehicle;
then, the whole power assembly model is considered
Figure BDA0002679553110000066
Where m denotes the vehicle mass, δ denotes the mass coefficient, CDRepresenting an air resistance coefficient, A representing a windward area, g representing a gravitational acceleration, and alpha (t) representing a gradient;
the method aims at considering the safety, comfort, track error and low energy consumption of vehicle running, and obtains an optimized objective function of track planning and low energy consumption planning in the alternative tracks
Figure BDA0002679553110000067
Wherein the formula (10) satisfies-deltacar≤δf≤δcar，0≤Preq≤Pcar，δcarIndicates the maximum front wheel angle, P, of the vehiclecarThe maximum output power of the automobile is represented, smo (i) represents the smoothness of the track, Gui (i) represents the deviation degree of the actual track and the reference track, obs (i) represents the danger degree of the obstacle to the track, e (i) represents the energy consumption of the track, and t represents coefficients.
And finally, on the premise of meeting a decision planning layer, carrying out energy management on the whole power assembly system, wherein the energy management control method of the intelligent network new energy automobile directly influences the dynamic property, economy, comfort and emission of the whole automobile, and according to the actual operation condition of the automobile and the difference of the characteristics and strategies of automobile parts, the intelligent network new energy automobile carries out energy management based on low-energy-consumption planning and reasonably distributes energy to different systems so as to improve the energy utilization rate of the automobile. The multi-energy power assembly control system, the motor and the control system thereof, and the battery and the management system technology thereof are the core of research in the field of new energy automobiles. The new energy automobile comprises a coupling driving system formed by an internal combustion engine and a motor and a multi-motor driving system driven by a plurality of motors in a coupling way, so that the power balance equation of different driving systems is
Prep＝Pe+Pm+Pb (11)
Wherein, the main power sources of the coupling driving system composed of the internal combustion engine and the motor are the internal combustion engine, the driving motor and the feedback brake, PreqRepresenting the power balance equation, PeDenotes an internal combustion engine, PmDenotes a drive motor, PbIndicating feedback braking;
in the formula (11), the main power sources of the multi-motor drive system are a plurality of drive motors and feedback braking, so that the power balance equation of the drive system is
Prep＝Pm1+Pm2+Pb (12)
Wherein, Pm1、Pm2Representing a plurality of drive motors, PbIndicating feedback braking;
according to the actual driving required torque and the actual vehicle speed, the energy management strategy dynamically distributes the driving power of different power sources, so that the working points of the motor and other power sources are distributed in a high-efficiency interval
Fuel＝σENG(ωENG,TENG) (13)
E＝σMG1(ωMG,TMG) (14)
Wherein Fuel represents the instantaneous Fuel consumption of the engine, ωENGIndicating the rotational speed, T, of the engineENGRepresenting the torque of the engine, E representing the instantaneous power consumption of the electric machine, omegaMGIndicating the rotational speed, T, of the motorMGRepresenting the torque of the motor; the energy management of the automobile power system is an optimization control problem, and finally an energy management optimization objective function (namely, minimum energy consumption) of the whole power assembly system is obtained
Figure BDA0002679553110000071
Wherein, JeThe energy cost function is represented, E (t) represents the electricity consumption of the whole power assembly system, Fuel (t) represents the oil consumption in time t, P (t) represents the power consumption of other electronic systems in the new energy automobile, and the power consumption is directly provided by a battery, wherein the power consumption comprises the energy consumption of electronic systems in a driving cabin of an automobile air conditioner, a display and the like, and electronic parts such as a vehicle-mounted computing unit, a sensor and the like; the aim of the intelligent networked new energy automobile is to minimize fuel consumption Fuel (t) in time t and minimize the electricity consumption E (t) of the whole power system.
In summary, the optimization method provided by the application has the advantages that the first layer is a low-energy-consumption planning layer for intelligent driving, and the energy consumption of the whole vehicle needs to be comprehensively considered, so that the controlled object needs to consider a vehicle kinematic model in addition to a basic traffic model, and the vehicle kinematic model describes the motion of the vehicle through complex interaction between tires and a road surface so as to realize the control of the lateral motion and the longitudinal motion of the vehicle; the low energy consumption planning belongs to a multi-objective optimization problem based on the information of the current environment and the vehicle model. The second layer is an energy management layer of the power system, and compared with the traditional vehicles, the new energy vehicles represented by hybrid vehicles, pure electric vehicles and fuel cell vehicles introduce motors, electric controls and batteries, provide a new power driving mode for vehicle movement, and greatly improve the energy-saving space of the whole vehicle. For hybrid electric vehicles and fuel cell vehicles, the energy management strategy can realize reasonable distribution of the output power of dynamic systems such as engines, batteries, motors, fuel cell units and the like, thereby realizing improvement of fuel economy and reduction of indexes such as emission and the like; for a pure electric vehicle, the driving and braking processes, the multi-motor driven torque distribution, the gear shifting strategy and the like all relate to energy management to improve the energy utilization rate of the pure electric vehicle.
It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
The meaning of "and/or" as used herein is intended to include both the individual components or both.
The term "connected" as used herein may mean either a direct connection between components or an indirect connection between components via other components.
In light of the foregoing description of the preferred embodiment of the present invention, many modifications and variations will be apparent to those skilled in the art without departing from the spirit and scope of the invention. The technical scope of the present invention is not limited to the content of the specification, and must be determined according to the scope of the claims.

Claims (3)

1.一种智能网联新能源汽车分层式能耗优化方法，其特征在于：包括感知层、决策规划层以及车辆控制层，感知层将获取的信息传递至决策规划层，决策规划层将规划结果输送至车辆控制层；1. A layered energy consumption optimization method for intelligent network-connected new energy vehicles, characterized in that: comprising a perception layer, a decision planning layer and a vehicle control layer, the perception layer transmits the acquired information to the decision planning layer, and the decision planning layer will The planning results are sent to the vehicle control layer; 其中，决策规划层包括路径规划、动作规划以及轨迹规划，将前述三种规划分别与低能耗规划结合实现多目标系统优化；Among them, the decision planning layer includes path planning, action planning and trajectory planning, and the aforementioned three plans are respectively combined with low-energy planning to achieve multi-objective system optimization; 车辆控制层通过能量管理模块在满足决策规划层的前提下，进行整个动力总成系统的能量管理；The vehicle control layer manages the energy of the entire powertrain system through the energy management module under the premise of satisfying the decision-making planning layer; 前述将路径规划与低能耗规划进行结合的步骤具体为：The aforementioned steps of combining path planning with low-energy-consumption planning are as follows: 第11步，获取智能网联新能源汽车的路径规划目标函数Step 11: Obtain the path planning objective function of the intelligent network-connected new energy vehicle
Figure FDA0003167094950000011
Figure FDA0003167094950000011
 其中，Eff(i)表示第i个路点的效率代价项，Saf(i)表示行车的安全项，Sta(i)表示全局参考路径稳定代价项，ω是各项系数；Among them, Eff(i) represents the efficiency cost term of the i-th waypoint, Saf(i) represents the safety term of driving, Sta(i) represents the global reference path stability cost term, and ω is the coefficient of each item; 第12步，将路径规划与低耗能规划结合，得到新的目标函数为Step 12: Combine path planning with low-energy-consumption planning to obtain a new objective function as
Figure FDA0003167094950000012
Figure FDA0003167094950000012
 其中，Eff(i)表示第i个路点的效率代价项，Saf(i)表示行车的安全项，Sta(i)表示全局参考路径稳定代价项，Cost(i)表示第i个路点与第i-1个路点之间消耗的能耗，ω是各项系数；Among them, Eff(i) represents the efficiency cost item of the ith waypoint, Saf(i) represents the safety item of driving, Sta(i) represents the stability cost item of the global reference path, Cost(i) represents the ith waypoint and The energy consumption between the i-1th waypoint, ω is the coefficient of each item; 前述将动作规划与低能耗规划进行结合的步骤具体为：The above-mentioned steps of combining action planning with low energy consumption planning are as follows: 通过对若干目标赋予权重系数，获取车辆的动作规划与低能耗规划的目标函数为By assigning weight coefficients to several objectives, the objective functions of vehicle action planning and low energy consumption planning are obtained as
Figure FDA0003167094950000013
Figure FDA0003167094950000013
 其中xeff表示道路交通通行效率，xsaf表示交通安全系数，cost表示行车与整个交通能耗，λ是各项系数。Among them, x eff represents the efficiency of road traffic, x saf represents the traffic safety factor, cost represents the energy consumption of driving and the whole traffic, and λ is the various coefficients. 2.根据权利要求1所述的智能网联新能源汽车分层式能耗优化方法，其特征在于：前述将轨迹规划与低能耗规划进行结合的步骤具体为：2. The layered energy consumption optimization method for an intelligent network-connected new energy vehicle according to claim 1, characterized in that: the aforementioned step of combining trajectory planning and low energy consumption planning is specifically: 第31步，获取车辆的动力学模型Step 31, get the dynamic model of the vehicle
Figure FDA0003167094950000014
Figure FDA0003167094950000014
Figure FDA0003167094950000021
Figure FDA0003167094950000021
Figure FDA0003167094950000022
Figure FDA0003167094950000022
Figure FDA0003167094950000023
Figure FDA0003167094950000023
Figure FDA0003167094950000024
Figure FDA0003167094950000024
 其中，Fcf表示车辆前轮受到的侧向力，Fcr表示车辆前轮受到的纵向力，Flf表示车辆后轮受到的侧向力，Flr表示车辆后轮受到的纵向力，ψ表示偏航角速度，(X，Y)表示车身的当前坐标，(x，y)表示车辆全局坐标下的当前坐标，δf为车辆的前轮转角，Iz为车身在车辆坐标系下绕垂向转动的转动惯量，m表示车辆质量；Among them, F cf represents the lateral force on the front wheel of the vehicle, F cr represents the longitudinal force on the front wheel of the vehicle, F lf represents the lateral force on the rear wheel of the vehicle, Flr represents the longitudinal force on the rear wheel of the vehicle, ψ represents Yaw angular velocity, (X, Y) represents the current coordinates of the vehicle body, (x, y) represents the current coordinates in the global coordinates of the vehicle, δ f is the front wheel angle of the vehicle, I z is the vertical direction of the vehicle body in the vehicle coordinate system The moment of inertia of rotation, m represents the mass of the vehicle; 第32步，获取整个动力总成模型Step 32, get the entire powertrain model
Figure FDA0003167094950000025
Figure FDA0003167094950000025
 其中，m表示车辆质量，δ表示质量系数，CD表示空气阻力系数，A表示迎风面积，g表示重力加速度，α(t)表示坡度；Among them, m is the vehicle mass, δ is the mass coefficient, C D is the air resistance coefficient, A is the windward area, g is the acceleration of gravity, and α(t) is the slope; 第33步，获取轨迹规划与低能耗规划的优化目标函数Step 33: Obtain the optimization objective function of trajectory planning and low-energy planning
Figure FDA0003167094950000026
Figure FDA0003167094950000026
 其中，公式(10)需满足-δcar≤δf≤δcar，0≤Preq≤Pcar，δcar表示该车辆的最大前轮转角，Pcar表示汽车最大输出功率，smo(i)项表示轨迹的平顺性，Gui(i)项表示实际轨迹与参考轨迹的偏离程度，obs(i)表示障碍物对轨迹的危险程度，e(i)表示轨迹的能耗，t表示各项系数。Among them, formula (10) needs to satisfy -δ car ≤δ f ≤δ car , 0≤P req ≤P car , δ car represents the maximum front wheel angle of the vehicle, P car represents the maximum output power of the vehicle, and the term smo(i) Represents the smoothness of the trajectory, Gui(i) represents the deviation of the actual trajectory from the reference trajectory, obs(i) represents the danger of obstacles to the trajectory, e(i) represents the energy consumption of the trajectory, and t represents the coefficients. 3.根据权利要求2所述的智能网联新能源汽车分层式能耗优化方法，其特征在于：在满足决策规划层的前提下，进行整个动力总成系统的能量管理，由于新能源汽车包含内燃机、电机以及多个电机耦合驱动的多电机驱动系统，因此驱动系统的功率平衡方程为3. The layered energy consumption optimization method for an intelligent network-connected new energy vehicle according to claim 2, characterized in that: under the premise of satisfying the decision-making planning layer, the energy management of the entire powertrain system is performed, because the new energy vehicle The multi-motor drive system includes an internal combustion engine, an electric motor, and multiple motors coupled to drive, so the power balance equation of the drive system is Prep＝Pe+Pm+Pb (11) Prep =P e +P m +P b (11) 其中，Preq表示功率平衡方程，Pe表示内燃机，Pm表示驱动电机，Pb表示反馈制动；Among them, Preq represents the power balance equation, Pe represents the internal combustion engine, Pm represents the drive motor, and Pb represents the feedback braking; 在公式(11)中，多电机驱动系统的主要动力源为若干驱动电机，因此驱动系统的功率平衡方程为In formula (11), the main power source of the multi-motor drive system is several drive motors, so the power balance equation of the drive system is Prep＝Pm1+Pm2+Pb (12) Prep =P m1 +P m2 +P b (12) 其中，Pm1、Pm2表示若干个驱动电机，Pb表示反馈制动；Among them, P m1 and P m2 represent several drive motors, and P b represents feedback braking; 接着获取驱动电机以及其他动力源工作点分布的高效率区间Then obtain the high-efficiency range of the operating point distribution of the drive motor and other power sources Fuel＝σENG(ωENG,TENG) (13)Fuel=σ ENG (ω ENG ,T ENG ) (13) E＝σMG1(ωMG,TMG) (14)E=σ MG1 (ω MG ,T MG ) (14) 其中，Fuel表示发动机的瞬时油耗，ωENG表示发动机的转速，TENG表示发动机的转矩，E表示电机的瞬时电耗，ωMG表示电机的转速，TMG表示电机的转矩；最终获取整个动力总成系统的能量管理优化目标函数Among them, Fuel represents the instantaneous fuel consumption of the engine, ω ENG represents the speed of the engine, T ENG represents the torque of the engine, E represents the instantaneous power consumption of the motor, ω MG represents the speed of the motor, and T MG represents the torque of the motor; Energy management optimization objective function of powertrain system
Figure FDA0003167094950000031
Figure FDA0003167094950000031
 其中，Je表示能量代价函数，E(t)表示整个动力总成系统的电耗，Fue l(t)表示在时间t内的油耗，P(t)表示新能源汽车内其他电子系统的功耗。Among them, J e represents the energy cost function, E(t) represents the power consumption of the entire powertrain system, Fuel(t) represents the fuel consumption in time t, and P(t) represents the power consumption of other electronic systems in the new energy vehicle. consumption.
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