CN105857312B - A kind of highway heavy truck speed travels optimization method - Google Patents

Abstract

The invention discloses a method for optimizing the speed of a heavy truck on an expressway, which comprises the following steps: establishing a longitudinal dynamics model of the vehicle, establishing an engine model of the vehicle, and designing a nonlinear model predictive controller. The present invention adopts the strategy of model predictive control, considers the fuel economy of heavy-duty trucks running on highways and the constraints of physical actuators, and uses the method of nonlinear model predictive control to optimize and obtain the optimal engine torque under the road information at the current stage, thereby obtaining The speed of the vehicle with the best fuel economy, and the aging coefficient of the nonlinear model predictive controller can be set according to the driver's requirements for the timeliness of freight, so as to balance the relationship between the timeliness of freight and fuel economy. Effectively reducing the fuel consumption of heavy-duty trucks on highways can also ensure the timeliness of freight, save energy and reduce greenhouse gas emissions.

Description

A Speed Driving Optimization Method for Heavy Trucks on Expressway

technical field

The invention relates to a method for improving the fuel economy of a heavy truck on an expressway, in particular to a method for optimizing the speed of a heavy truck on an expressway.

Background technique

While automobiles bring convenience and speed to people, they also bring enormous pressure to energy supply and environmental protection of countries all over the world. Freight transportation is a core part of the global economy, and the demand for road freight transportation is increasing year by year. However, road transportation accounts for a large proportion of global energy consumption and greenhouse gas emissions, accounting for about 26% of global energy consumption, and highway freight transportation is the main form of road transportation. Therefore, a large number of related researches are devoted to reducing the fuel consumption of highway heavy trucks to improve the fuel economy of road transportation. In order to further reduce the fuel consumption of heavy trucks on highways, the present invention optimizes the speed of heavy trucks running on highways.

At present, the speed optimization control strategies for vehicles at home and abroad mainly include constant speed cruise control and adaptive cruise control. Although constant speed cruise can fix the speed of the vehicle at a specific value to keep the vehicle running at a constant speed and achieve the effect of reducing fuel consumption to a certain extent, the speed is not necessarily the best fuel economy speed under the current road conditions. The function of high-speed cruise is too single and there are certain limitations. Adaptive cruise control is a driver assistance system developed on the basis of traditional vehicle constant speed cruise control, which can automatically adjust the vehicle speed by detecting vehicle status information (gear, speed, etc.), so as to ensure a safe distance. However, there are relatively few vehicles on the expressway, and large trucks should drive in the low-speed lane on the right, and there are fewer situations involving car following and gear shifting than on urban roads. Adaptive cruise control is more suitable for passenger vehicles with relatively dense traffic flow. It determines the driving speed of its own vehicle by judging the driving state of the vehicle in front. However, when heavy trucks are driving on expressways, the traffic flow is relatively sparse and there are fewer vehicles in front. The purpose is to determine the optimal fuel economy speed based on the current road information. Therefore, this paper proposes a method based on predictive control to optimize the speed of heavy-duty trucks on expressways. According to the current road information of the vehicle and the driver's demand for timeliness of freight, the best fuel economy engine torque is optimized. Reduce the fuel consumption of the vehicle.

Contents of the invention

The invention provides a method for optimizing the speed of heavy trucks on highways, adopting the strategy of model predictive control, considering the fuel economy of heavy trucks on highways and the constraints of physical actuators, and applying the method of nonlinear model predictive control to optimize the current Optimum engine torque under stage road information, so as to obtain the vehicle speed with the best fuel economy, and can set the aging coefficient of the nonlinear model predictive controller according to the driver's requirements for the timeliness of freight, so as to balance the freight The relationship between timeliness and fuel economy can not only effectively reduce the fuel consumption of highway heavy trucks but also ensure the timeliness of freight, save energy and reduce greenhouse gas emissions.

The object of the present invention is achieved through the following technical solutions:

A method for speed optimization of expressway heavy trucks, comprising the following steps:

Step 1. Establish the longitudinal dynamics model of the vehicle: ignore the axle load transfer of the front and rear axles, and use a simplified single-degree-of-freedom model to characterize the longitudinal dynamics of the vehicle;

Step 2. Establish the engine model of the vehicle: collect a large amount of experimental data, and establish a numerical model of the fuel consumption of the engine, which is used to represent the relationship between the fuel consumption rate of the engine per unit time, the engine torque, and the engine speed;

Step 3, nonlinear model predictive controller design: Based on the vehicle longitudinal dynamics model established in step 1 and the engine model established in step 2, a nonlinear model predictive controller with constraints is designed considering diesel engine fuel economy, and the The current road information and the vehicle's own speed are input into the nonlinear controller, and the model predictive control method is used to predict the future dynamics of the system, and optimize at the same time, determine the current optimal torque of the engine, and output it to the vehicle system, so that the vehicle can operate at the optimum speed. Drive at a fuel-efficient speed.

The beneficial effects of the present invention are:

1. The present invention reasonably optimizes the engine torque with the best fuel economy through the collection of road information and its own speed, effectively reducing the fuel consumption of heavy trucks running on expressways.

2. Reduce the driver's driving burden to a certain extent, because the controller directly controls the engine torque to change the speed of the vehicle, so the driver does not need to operate the accelerator and brake pedals during this process, and the high-speed Most of the road conditions on the highway are straight lines, and only the current direction of the steering wheel needs to be corrected. However, when an emergency occurs, the driver can still depress the brake pedal to control the vehicle.

3. According to the three-dimensional map of engine throttle opening, engine output torque and engine speed and the three-dimensional map of engine throttle opening, engine speed and fuel consumption rate, the data is interpolated and fitted to obtain engine output torque and engine output The numerical relationship between the torque and the fuel consumption rate is used to establish an accurate numerical model of the fuel consumption of the heavy truck engine and the universal characteristic curve of the engine.

Description of drawings

Figure 1 is a schematic diagram of vehicle force analysis;

Figure 2 is a map of engine torque-engine speed-throttle opening;

Figure 3 is a fuel consumption rate-engine speed-throttle opening map;

Fig. 4 is a fuel consumption rate-engine speed-engine torque fitting map;

Figure 5 is the universal characteristic curve of the engine;

Figure 6 is a simulation comparison diagram of total fuel consumption;

Fig. 7 is a comparison diagram of vehicle speed simulation;

Fig. 8 is a comparison diagram of vehicle engine torque simulation.

Detailed ways

The invention provides a method for optimizing the speed of heavy-duty trucks on highways, the method comprising the following steps:

Step 1. In order to facilitate the analysis and control of the vehicle system, a vehicle longitudinal dynamics model is established according to Newton's second law, ignoring the axle load transfer of the front and rear axles, and a simplified single-degree-of-freedom model is used to characterize the longitudinal dynamics of the vehicle, as shown in Figure 1 , and its kinetic equation is:

Among them, m is the mass of the vehicle, in kg; v is the longitudinal velocity of the vehicle, in m/s; F _engine , F _grad , F _rolling , and F _air are the vehicle's engine traction, road gradient resistance, rolling resistance and air resistance, respectively, in All N.

Among them, T _t is the engine torque, the unit is Nm; i _g is the transmission ratio of the vehicle transmission; i ₀ is the transmission ratio of the vehicle final drive; η _t is the transmission efficiency of the vehicle drive train; r is the radius of the wheel, the unit is m .

F _grad = mg sin(θ) (3)

Among them, g is the gravitational acceleration, the unit is m/s ² ; θ is the road slope, the unit is rad.

F _rolling ＝ mgC _r cos(θ) (4)

Among them, _Cr represents the coefficient of rolling resistance.

Among them, _CD is air resistance coefficient; ρ is air density, unit is kg/m ³ ; A is vehicle frontal area, unit is m ² ; v is vehicle longitudinal velocity, unit is m/s.

In summary, the longitudinal dynamic equation of the vehicle can be expressed in the following form:

Step 2. Establish the engine model of the vehicle: collect a large amount of experimental data, and establish a numerical model of the fuel consumption of the engine, which is used to represent the relationship between the fuel consumption rate of the engine per unit time, the engine torque, and the engine speed;

In order to accurately analyze the fuel consumption of vehicles, an accurate numerical model of fuel consumption of heavy-duty truck diesel engines is established. Extract the three-dimensional map of engine torque, engine speed and throttle opening of a heavy-duty truck diesel engine, as shown in Figure 2, and the three-dimensional map of fuel consumption rate, engine speed and throttle opening, as shown in Figure 3. The numerical model of engine fuel consumption represents the relationship between the fuel consumption rate of a diesel engine per unit time, engine torque, and engine speed.

Since the two diesel engine characteristic maps in Figure 2 and Figure 3 both contain the engine throttle opening, the data of the two maps can be linearly interpolated in MATLAB through the interp1 function to eliminate the shared throttle opening, and then use the MATLAB tool The box cftool fits the integrated data of engine torque, engine speed and fuel consumption rate, and obtains the polynomial function of normalized fuel consumption rate, engine torque and engine speed with an accuracy of ^10-6 :

f _fuelrate (n,T)＝p ₀₀ +p ₁₀ n+p ₀₁ T+p ₂₀ n ² +p ₁₁ nT+p ₀₂ T ² +p ₂₁ n ² T+p ₁₂ nT ² +p ₀₃ T ³ (7 )

The fitting parameters obtained by the MATLAB toolbox cftool are shown in Table 1:

Table 1 Fitting parameters of engine fuel consumption numerical model

Fitting parameters value p ₀₀ 0.002892 p ₁₀ 0.00209 p ₀₁ 0.001245 p ₂₀ 0.0005709 p ₁₁ 0.0009704 p ₀₂ -0.0004742 p ₂₁ 0.0002821 p ₁₂ -0.0002978 p ₀₃ -7.293e-005

According to the integrated data of engine torque, engine speed and fuel consumption rate, a three-dimensional map of fuel consumption rate, engine torque and engine speed is drawn, as shown in Figure 4. By projecting the obtained engine fuel consumption map on the x-y plane, the engine universal characteristic curve of the heavy-duty truck diesel engine can be obtained, as shown in Figure 5.

The fuel consumption numerical model of the engine is obtained, and the fuel consumption rate at the current moment and the total fuel consumption per unit time can be easily obtained when the engine speed and engine torque at any time are known.

Step 3. Design of nonlinear model predictive controller: Based on the vehicle longitudinal dynamics model established in step 1 and the numerical model of fuel consumption established in step 2, design a nonlinear model predictive control with constraints considering the actual driving conditions of the expressway According to the current road information and the vehicle's own speed, the model predictive control method is used to predict the future dynamics of the system, and at the same time to optimize, determine the optimal engine torque, and output it to the vehicle system, so that the vehicle can obtain the current maximum Best fuel economy speed.

The design of the nonlinear model predictive controller in the above step three includes the following steps:

(1) Control problem description:

When optimizing the driving speed of heavy trucks on highways, the present invention selects the torque Tt of the engine as the control variable, namely u=T _t , and selects the longitudinal speed of the vehicle as the state quantity, namely x=v. In order to meet the fuel economy and timeliness of the vehicle during the speed optimization process, the present invention adopts a model predictive control method to optimize the engine torque of the vehicle, so as to achieve the purpose of optimizing the vehicle speed. According to the longitudinal dynamic equation of the vehicle, the prediction model used in the optimization process is sorted out, as follows:

The specific meaning of each parameter in the formula has been introduced in step 2, and will not be repeated here. According to the engine fuel consumption model and the normalized parameters are substituted in, the energy consumption model in the optimization process is sorted out, as follows:

f _fuelrate (n,T)＝0.002892+0.00209n+0.001245T+0.0005709n ² +0.0009704nT-0.0004742T ² +0.0002978n ² T-0.0002978nT ² +7.293e ^-5 T ³ (9)

Since the engine speed has the following relationship with the vehicle speed:

Among them, n is the engine speed, the unit is r/min, and ω _e is the engine angular velocity unit, rad/s.

Therefore, the functional relationship between the fuel consumption rate, the engine speed, and the engine torque can be converted into a functional relationship between the fuel consumption rate, the vehicle speed, and the engine speed.

So far, the speed optimization of expressway heavy trucks can be organized into the following form:

s.t.

T _{t_min} ≤T _t ≤T _{t_max} (12)

v _min ≤ v ≤ v _max (13)

Equation (11) is the objective function for the speed optimization of expressway heavy trucks, where N is the prediction step size in the model predictive control method, Δt is the forward prediction time of each step in the prediction time domain, and the fuel consumption rate and the prediction step size of each step The product of time length is accumulated in N steps, and the accumulated value is minimized through the optimization algorithm, so as to achieve the least fuel consumption in the predicted time domain, which directly reflects the fuel economy in the speed optimization process; formula (12) is the optimization process The engine torque is constrained. Due to the limitation of the inherent properties of the engine, there are limits on the maximum and minimum values of the torque, where T _min and T _max are the minimum and maximum values that the engine torque can achieve, and the unit is N; Equation (13 ) is the speed limit for heavy-duty trucks driving on the expressway. According to the "Expressway Traffic Management Measures", freight vehicles should drive in the slow lane, and the speed limit is 60km/h-100km/h, where v _min and v _max are respectively the vehicle's The minimum and maximum driving speeds, in m/s; Equation (14) is a time-limited constraint on vehicle driving, balancing the relationship between cargo transportation time and fuel consumption. If the vehicle can be driven as much as possible simply to reduce fuel consumption slow, but this is very unreasonable for the transportation of goods, and it may lead to overdue delivery of goods. Therefore, it is necessary to reduce the fuel consumption of vehicles and ensure the timeliness of goods transportation. The variables in formula (14) The expression for is as follows:

s=v (N Δt) (15)

Among them, s is the distance traveled by the vehicle in the predicted time domain at the current speed in the predicted time domain, and the unit is m;

s _min =v _min (N·Δt) (16)

Among them, s _min is the distance traveled by the vehicle in the predicted time domain at a limited minimum speed in the predicted time domain, that is, the minimum distance that the vehicle can travel in the predicted time domain, in m;

s _max = v _max (N Δt) (17)

Among them, s _max is the distance traveled by the vehicle in the predicted time domain at a limited maximum speed, that is, the maximum distance that the vehicle can travel in the predicted time domain, in m;

κ in formula (14) is the proportional coefficient of human control, which can be controlled between the optimal fuel consumption and the shortest time to reach the designated location. The larger the κ, the lower the fuel consumption and the longer the time to reach the destination. High time is short.

(2) Solving the control problem:

In the expressway heavy truck speed optimization process, the present invention utilizes the fmincon function in MATLAB to solve the designed nonlinear model predictive controller, and the parameters of the controller are as shown in table 2:

Table 2 Nonlinear Model Predictive Controller Parameters

parameter T _{t_min} , T _{t_max} v _min , v _max N value -50,650 (60/3.6,100/3.6) 16

Due to the inevitable interference of the external environment in the actual driving process, the prediction model only considers the longitudinal dynamics of the vehicle, and does not consider the influence of external disturbances in the driving process. Therefore, in the optimization process, if all the N speed values of the calculated optimal engine torque sequence are directly used to control the vehicle, it will cause model mismatch and the optimized speed effect will be worse. Therefore, in the actual solution process, we combine the idea of model predictive control and apply the first value of the optimal engine torque sequence obtained at each moment to the vehicle to achieve rolling optimization, thereby reducing the influence of other disturbance factors.

(3) Control algorithm simulation verification

In order to verify the functionality of the designed expressway heavy-duty truck speed optimization scheme, a nonlinear model predictive controller was built in MATLAB/SIMULINK, and a joint simulation was performed with the high-precision truck simulation software TRUCKSIM. TRUCKSIM provides high-precision truck models as The controlled object simulates the driving state of the truck in reality to the greatest extent.

Under the above-mentioned joint simulation platform, the simulation experiment of simulating the working conditions of the expressway was carried out. At a slope of 0.03753, the expressway traveled 300m in a straight line, and the initial speed of the vehicle was set to 70km/h, and the aging coefficient κ of the controller was selected as 70; in order to visually verify the The non-linear model predicts the reduction of fuel consumption of heavy-duty trucks on the highway under the action of the controller. Under the same road conditions, there is no controller. If the heavy-duty truck is driven at a constant speed of 70km/h, there will be controllers and no controllers. The simulation results under the action of the controller are compared, as shown in Figure 6-8.

From the simulation results, it can be seen that under the action of the controller, the fuel consumption of heavy-duty trucks on the highway is effectively reduced. It can be seen from Figure 6 that the total fuel consumption under the control of the controller is lower than that without control In this case, the fuel consumption is 0.035031kg and 0.038101kg respectively, and the fuel saving is about 7.35%. In this case, the speed of the vehicle with the controller is always higher than that without the controller, which also verifies the designed nonlinear control. The timeliness of the controller proves the effectiveness of the designed controller for speed optimization of heavy trucks on expressways.

Claims (5)

1. A method for expressway heavy truck speed optimization, is characterized in that, comprises the following steps: Step 1, establishing a vehicle longitudinal dynamics model; Step 2. Establish the engine model of the vehicle: collect experimental data and establish a numerical model of fuel consumption of the engine to represent the relationship between the fuel consumption rate of the engine per unit time, the engine torque, and the engine speed; Step 3, nonlinear model predictive controller design: Based on the vehicle longitudinal dynamics model established in step 1 and the engine model established in step 2, a nonlinear model predictive controller with constraints is designed considering diesel engine fuel economy, and the The current road information and the vehicle's own speed are input into the nonlinear controller, and the model predictive control method is used to predict the future dynamics of the system, and optimize at the same time, determine the current optimal torque of the engine, and output it to the vehicle system, so that the vehicle can operate at the optimum speed. Drive at a fuel-efficient speed. 2. the method for a kind of expressway heavy-duty truck speed optimization as claimed in claim 1, is characterized in that, the vehicle longitudinal dynamics model that described step 1 establishes is: Among them, m is the mass of the vehicle, in kg; v is the longitudinal speed of the vehicle, in m/s; T _t is the engine torque, in Nm; i _g is the transmission ratio of the vehicle transmission; i ₀ is the transmission ratio of the final drive of the vehicle; _t is the transmission efficiency of the vehicle drive train; _r is the radius of the wheel, the unit is m; g is the acceleration of gravity, the unit is m/s ² ; θ is the road slope, the unit is rad; C _r is the coefficient of rolling resistance; Drag coefficient; ρ is air density, unit kg/m ³ ; A is vehicle frontal area, unit m ² . 3. the method for a kind of expressway heavy truck speed optimization as claimed in claim 1, is characterized in that, described step 2 sets up the engine model of vehicle as: f _fuelrate (n,T)＝0.002892+0.00209n+0.001245T+0.0005709n ² +0.0009704nT-0.0004742T ² +0.0002978n ² T-0.0002978nT ² +7.293e ^-5 T ³ Among them, T is the engine output torque, the unit is N m; n is the engine speed, the unit is r/min. 4. the method for a kind of expressway heavy-duty truck speed optimization as claimed in claim 3, is characterized in that, the concrete process that described step 2 sets up the engine model of vehicle is: Extract the three-dimensional map of engine torque, engine speed and throttle opening of heavy-duty truck diesel engines, as well as the three-dimensional map of fuel consumption rate, engine speed and throttle opening; Perform linear interpolation on the data of the two three-dimensional maps in MATLAB through the interp1 function to eliminate the shared throttle opening, and then use the MATLAB toolbox cftool to fit the integrated data of engine torque, engine speed and fuel consumption rate , to get the polynomial function of the normalized fuel consumption rate, engine torque and engine speed with an accuracy of 10 ^-6 ; According to the integrated data of engine torque, engine speed, and fuel consumption rate, draw a three-dimensional map of fuel consumption rate, engine torque, and engine speed, and project the obtained engine fuel consumption map on the x-y plane to obtain a heavy truck Engine universal characteristic curve of diesel engine. 5. the method for a kind of expressway heavy truck speed optimization as claimed in claim 1, is characterized in that, described step 3 nonlinear model predictive controller design comprises the following steps: (1) Control problem description: The expressway heavy truck speed optimization is organized into the following form: T _{t_min} ≤T _t ≤T _{t_max} (12) v _min ≤ v ≤ v _max (13) Described formula (11) is the objective function of expressway heavy-duty truck speed optimization, and wherein N is the prediction step size in the model predictive control method, and Δt is the time length of each step forward prediction in the prediction time domain; Said formula (12) is the constraint to the engine torque in the optimization process, wherein _Tmin and _Tmax are respectively the minimum value and the maximum value that the engine torque can reach, unit N; Described formula (13) is to the limitation of the speed of heavy-duty truck when driving on expressway, wherein v _min and v _max are the minimum and maximum travel speed of vehicle respectively, unit m/s; The formula (14) is the time constraint on vehicle running, where: s=v·(N·Δt); s _min =v _min ·(N·Δt); s _max =v _max ·(N·Δt) ; κ is the proportional coefficient of human control; (2) Solve the control problem.