Torque control method for distributed drive electric vehicle based on model predictive control

Abstract

The application discloses a torque control method for a distributed drive electric vehicle based on model prediction control and relates to the technical field of automatic driving steering control, and solves the technical problem that vehicle control is insufficient in stability and energy saving, and the technical solution is as follows: in order to improve the stability of vehicle control and realize the energy saving of the distributed electric vehicle, a torque vector distribution framework is designed, total torque is generated according to the speed control demand of the upper driver, a proportional integral control algorithm is designed, and steering behavior is obtained through a driving simulator. Then, the optimized torque vector is distributed to the front axle and the rear axle. The lower layer generates direct yaw torque control input through the longitudinal differential force of the left wheel and the right wheel, so as to ensure the control stability of the vehicle.

Description

Technical Field

[0001] This application relates to the field of autonomous vehicle technology, specifically to autonomous driving steering control technology, and more particularly to a torque control method for distributed drive electric vehicles based on model predictive control. Background Technology

[0002] Electric vehicles (EVs), due to their zero-emission characteristics, are considered one of the most effective green transportation tools in the environmental protection field. Distributed EVs employing multi-power systems can offer more control schemes through different torque vectoring methods. However, limited driving range has become a significant factor restricting the development of the EV industry. Developing advanced battery technologies such as higher energy density and ultra-fast charging methods has become a focus of extensive research. Furthermore, improving the efficiency of in-wheel motors is also an effective way to reduce energy consumption. Since the motors in distributed EVs are independently controllable, this can be achieved through reasonable torque vectoring. It should be noted that the yaw motion control caused by the differential torque input between the left and right wheels may lead to vehicle instability. Therefore, how to design a torque vectoring framework that improves vehicle handling stability while achieving energy conservation in distributed EVs is an important problem that needs to be solved. Summary of the Invention

[0003] This application provides a torque control method for distributed drive electric vehicles based on model predictive control. The technical objective is to optimize the torque vector distribution framework of distributed drive electric vehicles to ensure their energy-saving performance and stability.

[0004] The above-mentioned technical objective of this application is achieved through the following technical solution:

[0005] A torque control method for a distributed drive electric vehicle based on model predictive control, comprising:

[0006] S1: Construct a tire dynamics model;

[0007] S2: Construct a model predictive controller based on the tire dynamics model, and construct the optimization objective function of the model predictive controller;

[0008] S3: Obtain the reference centroid sideslip angle and reference yaw rate through the vehicle's two-degree-of-freedom model, and design a stability cost function aimed at improving vehicle stability based on the reference centroid sideslip angle and reference yaw rate;

[0009] S4: Construct the optimal control problem based on the optimization objective function and stability cost function of the model predictive controller, solve the optimal control problem to obtain the required yaw moment, and distribute the required drive torque to the four wheels according to the yaw moment;

[0010] S5: Control the torque of the distributed drive electric vehicle according to the required driving torque of the four wheels;

[0011] Step S2 includes:

[0012] S21: Discretize the tire dynamics model and obtain the model predictive controller using the Euler method, as follows:

[0013] ;

[0014] in, express Vehicle status at any given time; express Motor torque input at any given moment; Indicates the sampling time;

[0015] S22: Predict the controller based on the model, if the current time is The predictive control input of the system is expressed as: Then the system predicts the state. Represented as:

[0016] ;

[0017] in, Indicates control of line of sight;

[0018] S23: Construct the optimization objective function of the model predictive controller based on the system's predicted state. This optimization objective function is expressed as:

[0019] ;

[0020] ;

[0021] in, Indicates total torque input The cost function, , and These represent the proportional coefficient and the integral coefficient, respectively. This indicates the deviation between the actual speed and the target speed; Indicates the minimum permissible torque input. Indicates the maximum permissible torque input; Indicates the weighting coefficient; and These represent the torque inputs for the front and rear wheels, respectively. Indicates the current time; This represents the cost function that considers both the effective tire torque and the longitudinal slip ratio. This represents the front wheel reference torque, which is the most effective torque input to the front wheels at the current wheel speed. This represents the rear wheel reference torque, which is the most effective torque input to the rear wheels at the current wheel speed. This indicates the longitudinal slip ratio of the front wheels; This indicates the longitudinal slip ratio of the rear wheel; , , and All of these represent weighting coefficients.

[0022] The beneficial effects of this application are as follows: The torque control method for distributed drive electric vehicles based on model predictive control described in this application decouples torque vector control at different control layers, achieving motor efficiency optimization and vehicle lateral motion stability control. To improve vehicle handling stability while achieving energy conservation in distributed electric vehicles, a torque vector distribution framework is designed. The upper layer, where the driver's speed control demand generates the total torque, employs a proportional-integral (PI) control algorithm, and steering behavior is obtained through a driving simulator. The in-wheel motor energy efficiency diagram based on dynamometer testing is used as the standard for optimizing the torque distribution strategy. Then, the optimized torque vector is distributed to the front / rear axle. The lower layer generates a direct yaw torque control input through the longitudinal force difference between the left and right wheels to ensure vehicle handling stability. Attached Figure Description

[0023] Figure 1 This is a flowchart of the torque control method for distributed drive electric vehicles based on model predictive control described in this application;

[0024] Figure 2 Efficiency diagram of distributed drive motor;

[0025] Figure 3 This is a schematic diagram of a two-degree-of-freedom dynamic model of a vehicle. Detailed Implementation

[0026] The technical solution of this application will be described in detail below with reference to the accompanying drawings.

[0027] Figure 1 This is a flowchart of the torque control method for distributed drive electric vehicles based on model predictive control described in this application. Figure 1 The method specifically includes:

[0028] S1: Construct a tire dynamics model.

[0029] In this embodiment of the application, the process of constructing the tire dynamics model is as follows:

[0030] S11: The wheel drive mechanics model considering tire slippage is expressed as:

[0031] (1)

[0032] , ;

[0033] in, Indicates tires Longitudinal force, Indicates tires Moment of inertia about the z-axis Indicates tires Motor torque input, Indicates the tire's rolling radius. and They represent tires. angular velocity and longitudinal velocity, Indicates tires longitudinal stiffness, Indicates the front wheel. Indicates the rear wheel; Indicates tires The longitudinal slip ratio.

[0034] S12: The rotational motion of the tire, derived from the wheel drive mechanics model, is expressed as follows:

[0035] (2)

[0036] Among them, tires longitudinal slip ratio Decide on the tires longitudinal force .

[0037] Considering the nonlinearity of tires, their longitudinal stiffness and cornering stiffness are not constant values ​​during actual driving. Therefore, the improved tire... longitudinal stiffness and turning stiffness The magic formula is derived from the following:

[0038] (3)

[0039] in, Indicates tires Longitudinal force; Indicates tires Lateral forces; Indicates the road-tire friction coefficient; Indicates tires Vertical force; , , , , and All represent the fitted parameters; Indicates tires Longitudinal stiffness; Indicates tires Side slip angle.

[0040] Due to the attachment ellipse, the forces acting on the tire are rewritten as:

[0041] , (4)

[0042] , , (5)

[0043] in, Indicates the corrected tire Longitudinal force; Indicates the corrected tire Lateral forces.

[0044] S13: Obtain the corrected tire longitudinal stiffness in real time based on the corrected tire stress conditions. and turning stiffness , represented as:

[0045] (6)

[0046] In summary, the state-space equations for wheel motion, i.e., the tire dynamics model, are derived as follows:

[0047] (7)

[0048] S2: Construct a model predictive controller based on the tire dynamics model, and construct the optimization objective function of the model predictive controller.

[0049] Specifically, step S2 includes:

[0050] S21: Considering the uncertainties in tire longitudinal velocity and tire longitudinal stiffness, model predictive control is used to nonlinearize the model. In the design of the model predictive controller, equation (7) is discrete. The tire dynamics model is then discretized, and according to the Euler method, the discrete equation is rewritten as:

[0051] (8)

[0052] in, express Vehicle status at any given time; express Motor torque input at any given moment; Indicates the sampling time.

[0053] S22: It is worth noting that in the design of MPC (Model Predictive Controller), and Updates are performed at different sampling times. This ensures the accuracy of the model predictive controller and avoids inappropriate control inputs. The core of the model predictive controller is the prediction of the system's future state. The optimal control input can be calculated by minimizing the cost function constructed from the system tracking error. Let represent the system's predicted line of sight as equal to the control line of sight. .

[0054] Then, based on the model-predicted controller, if we assume the current time is... The predictive control input of the system is expressed as: Then the system predicts the state. Represented as:

[0055] (9)

[0056] in, Indicates control of line of sight;

[0057] S23: Construct a model to predict the target function of the controller based on the predicted state of the system.

[0058] First, in the front-to-rear axle torque distribution process, the driver's torque input requirements must be met. The PI controller is designed to simulate the driver's driving / braking behavior when tracking a target vehicle speed. The total torque input can be expressed as:

[0059] (10)

[0060] in, and These represent the proportional coefficient and the integral coefficient, respectively. This indicates the deviation between the actual speed and the target speed.

[0061] The cost function for the total torque input is then expressed as:

[0062] (11)

[0063] in, Indicates the minimum permissible torque input. Indicates the maximum permissible torque input; Indicates the weighting coefficient; and These represent the torque inputs for the front and rear wheels, respectively. Indicates the current moment.

[0064] Then, in the upper-level design, energy efficiency is the primary consideration, with the basic approach being to provide an efficient operating range for the in-wheel motor. Therefore, a wheel speed efficiency mapping function is constructed. Based on... Figure 2 The motor efficiency diagram shown uses the most effective torque input to the front and rear wheels at the current wheel speed as the reference torque. The torque distribution strategy is optimized. The average wheel speed of the left and right wheels is used to match the most effective torque. Then, the calculated optimized front and rear axle torques are evenly distributed to the left and right wheels. In addition, low tire slippage rate is used as an optimization index to improve vehicle stability. The cost function considering effective tire torque and longitudinal slip rate is expressed as:

[0065] (12)

[0066] in, This represents the front wheel reference torque, which is the most effective torque input to the front wheels at the current wheel speed. This represents the rear wheel reference torque, which is the most effective torque input to the rear wheels at the current wheel speed. This indicates the longitudinal slip ratio of the front wheels; This indicates the longitudinal slip ratio of the rear wheel; , , and All of these represent weighting coefficients.

[0067] For the cost function (12), considering vehicle longitudinal stability control, the front wheels are preferentially matched to the reference torque. When the front axle of the vehicle approaches the tire grip limit, rapid acceleration or deceleration may cause the vehicle to sideslip. Therefore, It has a large value. Furthermore, the differential torque input generated by yaw motion control and steering behavior leads to a large tire slip ratio on one side of the axle. Distributed drive electric vehicles require an effective control scheme to avoid drive wheel tire slippage. Therefore, in the cost function... In, state variables and The value is chosen to have a larger left and right wheel slip ratio to suppress over-rotation. Additionally, to avoid conflicts between torque optimization results and driver intent, a logical check is added. If the optimized rear wheel torque input is opposite to the driver's total torque input, the rear wheel torque input is zero.

[0068] In summary, the objective function for the model predictive controller can be expressed as:

[0069] ;

[0070] ;

[0071] S3: Obtain the reference center of gravity sideslip angle and reference yaw rate through the vehicle's two-degree-of-freedom model, and design a stability cost function aimed at improving vehicle stability based on the reference center of gravity sideslip angle and reference yaw rate.

[0072] Specifically, the lower-level controller generates a direct yaw torque control input through the longitudinal force difference between the left and right wheels to ensure the vehicle's handling stability, including:

[0073] S31: Construct a two-degree-of-freedom model of the vehicle, such as Figure 3 As shown, the two-degree-of-freedom model of the vehicle is represented as follows:

[0074] (13)

[0075] in, Indicates the overall vehicle weight. Indicates the longitudinal speed of the vehicle. Indicates the centroid sideslip angle. Indicates yaw rate. This indicates the distance from the vehicle's center of gravity to the front axle. This indicates the distance from the vehicle's center of gravity to the rear axle. Indicates the lateral stiffness of the front axle tires. Indicates the rear axle tire lateral stiffness. This represents the moment of inertia of the entire vehicle. This indicates the yaw moment experienced by the vehicle. Indicates the steering angle of the front wheels.

[0076] S32: Using the yaw rate and sideslip angle as yaw references, the reference yaw rate and reference sideslip angle are obtained through a stable yaw motion response. Considering that the yaw torque control input will affect the static response of the sideslip angle, the reference sideslip angle is chosen to be zero to ensure vehicle stability. Therefore, the reference sideslip angle and reference yaw rate are expressed as:

[0077] (14)

[0078] in, This represents the reference center of gravity sideslip angle, which has a value of 0 to ensure vehicle stability. This represents the reference yaw rate.

[0079] S33: Based on the reference centroid sideslip angle and the reference yaw rate, a stability cost function aimed at improving vehicle stability is designed. This stability cost function is then expressed as:

[0080] ;

[0081] (15)

[0082] in, , and All represent weighting coefficients; This represents the minimum value of the yaw moment input. This indicates the maximum value of the yaw moment input.

[0083] S4: Construct the optimal control problem based on the optimization objective function and stability cost function of the model predictive controller, solve the optimal control problem to obtain the required yaw moment, and distribute the required drive torque to the four wheels according to the yaw moment.

[0084] Specifically, step S4 includes:

[0085] S41: Combining the cost functions (11), (12), and (15), the optimal control problem is constructed as follows:

[0086] ;

[0087] S42: Solve the optimal control problem using a nonlinear programming solver to obtain the required yaw torque. Distribute the required drive torque to the four wheels based on the required yaw torque, expressed as:

[0088] ;

[0089] in, This indicates the drive torque of the vehicle's left front wheel. This indicates the drive torque of the vehicle's right front wheel. This indicates the drive torque of the vehicle's left rear wheel. This indicates the drive torque of the vehicle's right rear wheel. This indicates the width of the vehicle's track.

[0090] S5: Controls the torque of the distributed drive electric vehicle according to the required driving torque of the four wheels.

[0091] The above are exemplary embodiments of this application, and the scope of protection of this application is defined by the claims and their equivalents.

Claims

1. A torque control method for a distributed drive electric vehicle based on model predictive control, characterized in that, include: S1: Construct a tire dynamics model; S2: Construct a model predictive controller based on the tire dynamics model, and construct the optimization objective function of the model predictive controller; S3: Obtain the reference centroid sideslip angle and reference yaw rate through the vehicle's two-degree-of-freedom model, and design a stability cost function aimed at improving vehicle stability based on the reference centroid sideslip angle and reference yaw rate; S4: Construct the optimal control problem based on the optimization objective function and stability cost function of the model predictive controller, solve the optimal control problem to obtain the required yaw moment, and distribute the required drive torque to the four wheels according to the yaw moment; S5: Control the torque of the distributed drive electric vehicle according to the required driving torque of the four wheels; Step S2 includes: S21: Discretize the tire dynamics model and obtain the model predictive controller using the Euler method, as follows: ； in, express Vehicle status at any given time; express Motor torque input at any given moment; Indicates the sampling time; S22: Predict the controller based on the model, if the current time is The predictive control input of the system is expressed as: Then the system predicts the state. Represented as: ； in, Indicates control of line of sight; S23: Construct the optimization objective function of the model predictive controller based on the system's predicted state. This optimization objective function is expressed as: ； ； in, Indicates total torque input The cost function, , and These represent the proportional coefficient and the integral coefficient, respectively. This indicates the deviation between the actual speed and the target speed; Indicates the minimum permissible torque input. Indicates the maximum permissible torque input; Indicates the weighting coefficient; and These represent the torque inputs for the front and rear wheels, respectively. Indicates the current time; This represents the cost function that considers both the effective tire torque and the longitudinal slip ratio. This represents the front wheel reference torque, which is the most effective torque input to the front wheels at the current wheel speed. This represents the rear wheel reference torque, which is the most effective torque input to the rear wheels at the current wheel speed. This indicates the longitudinal slip ratio of the front wheels; This indicates the longitudinal slip ratio of the rear wheel; , , and All of these represent weighting coefficients.

2. The method as described in claim 1, characterized in that, In step S1, the construction of the tire dynamics model is represented as follows: ； in, Indicates tires Longitudinal slip ratio; Indicates tires Motor torque input; Indicates the rolling radius of the wheel; Indicates tires longitudinal velocity; Indicates tires Moment of inertia about the z-axis; Indicates tires Corrected longitudinal stiffness; , Indicates the front wheel. Indicates the rear wheel; ； ； ； ， ， ； in, Indicates the corrected tire Longitudinal forces; Indicates tires Longitudinal force; Indicates the road-tire friction coefficient; Indicates tires Vertical force; , and All represent the fitted parameters; Indicates tires Longitudinal stiffness; Indicates tires Side slip angle.

3. The method as described in claim 2, characterized in that, Step S3 includes: S31: Construct a two-degree-of-freedom model of the vehicle, which is represented as follows: ； in, Indicates the overall vehicle weight. Indicates the longitudinal speed of the vehicle. Indicates the centroid sideslip angle. Indicates yaw rate. This indicates the distance from the vehicle's center of gravity to the front axle. This indicates the distance from the vehicle's center of gravity to the rear axle. Indicates the lateral stiffness of the front axle tires. Indicates the rear axle tire lateral stiffness. This represents the moment of inertia of the entire vehicle. This indicates the yaw moment experienced by the vehicle. Indicates the front wheel steering angle; S32: The reference centroid sideslip angle and reference yaw rate are obtained through a two-degree-of-freedom vehicle model, expressed as: ； in, This represents the reference center of gravity sideslip angle, which has a value of 0 to ensure vehicle stability. Indicates the reference yaw rate; S33: Based on the reference centroid sideslip angle and the reference yaw rate, a stability cost function aimed at improving vehicle stability is designed. This stability cost function is then expressed as: ； ； in, , and All represent weighting coefficients; This represents the minimum value of the yaw moment input. This indicates the maximum value of the yaw moment input.

4. The method as described in claim 3, characterized in that, Step S4 includes: S41: The optimal control problem is expressed as: ； S42: Solve the optimal control problem using a nonlinear programming solver to obtain the required yaw torque. Distribute the required drive torque to the four wheels based on the yaw torque, as follows: ； in, This indicates the drive torque of the vehicle's left front wheel. This indicates the drive torque of the vehicle's right front wheel. This indicates the drive torque of the vehicle's left rear wheel. This indicates the drive torque of the vehicle's right rear wheel. This indicates the width of the vehicle's track.

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