Composite braking control method for distributed driving electric vehicle

Abstract

The invention relates to the field of electric vehicle compound control, in particular to a distributed driving electric vehicle compound brake control method which specifically comprises the following steps: S1, establishing an upper layer controller; s2, establishing a lower layer controller; and S3, establishing a vehicle model data monitoring unit. On the basis of control of the distributed driving electric automobile, an upper-layer controller, a lower-layer controller and a detection unit of an automobile body are established, a circulating detection process is formed, braking selection according to a time sequence and a control time domain is constructed by the upper-layer controller according to the actual driving road condition, and then braking distribution work is completed by the lower-layer controller. And finally, energy recovery of the electric automobile can be completed while the stability of the automobile is kept.

Description

Technical Field

[0001] The invention relates to the field of electric vehicle compound control, and in particular to a distributed drive electric vehicle compound braking control method. Background Art

[0002] Electric vehicles have become the object of vigorous development in various countries due to their advantages of energy saving and environmental protection, but the lack of driving range still affects the popularity of electric vehicles. Braking energy recovery technology converts kinetic energy into electrical energy through a feedback system and stores it in the battery during braking, which can effectively improve the economy and driving range of the entire vehicle. Distributed drive electric vehicles have four wheel hub motors, and each wheel can recover braking energy.

[0003] At present, the research on brake energy recovery of distributed drive electric vehicles mainly focuses on the distribution of braking force between the front and rear axles and the distribution of motor braking force and friction braking force of each wheel. However, under specific working conditions, such as braking conditions on split roads and braking conditions on curves, there are few studies on the impact of left and right braking force distribution on the braking stability and brake energy recovery of the whole vehicle. Therefore, it is necessary to study the composite braking control that comprehensively considers brake energy recovery and yaw stability. Reasonable distribution of the braking force of the four wheels can effectively improve the yaw stability of distributed drive electric vehicles in response to specific working conditions and maximize the brake energy recovery efficiency of distributed electric vehicles. Summary of the invention

[0004] The technical problem to be solved by the present invention is to overcome the defects of the prior art and provide a distributed drive electric vehicle compound braking control method to solve the poor stability and shaking phenomenon caused by braking when the electric vehicle brakes and recovers braking energy in the prior art.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0006] A first aspect of the present invention provides a distributed drive electric vehicle compound braking control method, which specifically comprises the following steps:

[0007] S1. Establish an upper controller, which includes a longitudinal speed tracking controller and a yaw moment controller. The longitudinal speed tracking controller takes the target vehicle speed and the actual vehicle speed as input, obtains the braking strength by calculation, and obtains the longitudinal moment; the yaw moment controller takes the current vehicle parameter data as input, calculates the required yaw moment value, and obtains the yaw moment;

[0008] S11. The longitudinal speed tracking controller calculates the braking intensity through the PID control method to obtain the required longitudinal braking torque;

[0009] S12. The yaw moment controller uses the ideal yaw angular velocity and the ideal sideslip angle of the center of mass of the two-degree-of-freedom vehicle model as the reference input of the MPC model predictive controller. Through the model predictive control method, the first time domain element in the optimized output sequence is taken as the model predictive control output, that is, the required yaw moment value.

[0010] Preferably, the braking intensity in step S11 is z, which specifically includes:

[0011]

[0012] Among them, K p ,K i ,K d are proportional, integral and differential coefficients respectively; v d is the target speed, v is the current actual speed;

[0013] The longitudinal braking torque specifically includes:

[0014] T bref =zmgr w ;

[0015] Among them, z is the braking strength, m is the vehicle curb weight, g is the gravitational acceleration, r w is the wheel rolling radius;

[0016] S2. Establish a lower controller, which coordinates and distributes the longitudinal torque and yaw torque obtained in step S1 through a torque distribution controller, and distributes the motor torque and friction braking torque of the left front wheel, the right front wheel, the left rear wheel, and the right rear wheel respectively;

[0017] S3. Establish a vehicle model data monitoring unit to collect the actual speed and vehicle parameters of the current electric vehicle. The vehicle parameters include the current vehicle mass, gravity acceleration and wheel rolling radius, and output them to the upper controller of step S1 in real time to complete the loop calculation.

[0018] Preferably, the parameters of the yaw rate and the ideal center-of-mass sideslip angle in step S12 specifically include:

[0019]

[0020] Among them, β d is the ideal center of mass sideslip angle, r d is the ideal yaw rate, a and b are the distances from the center of mass of the vehicle to the front and rear axles, respectively, and v x is the longitudinal velocity of the vehicle, k f ,k r are the cornering stiffness of the front and rear axles, m is the curb weight of the vehicle, δ is the front wheel turning angle, and K is the stability factor. The stability factor specifically includes:

[0021]

[0022] Among them, k r is the rear axle cornering stiffness, k f is the front axle cornering stiffness.

[0023] Preferably, in step S12, based on the yaw moment generated by the automobile when turning or driving on an open road, the yaw acceleration and the moment of inertia are calculated for the two-degree-of-freedom vehicle model, and the MPC controller constructs a braking control time domain based on the timing model according to the timing increments. By selecting the braking control time domain of the timing model, the actual yaw angular velocity and the actual sideslip angle of the center of mass of the vehicle are made to coincide with the corresponding ideal sideslip angle of the center of mass and the ideal yaw angular velocity.

[0024] Preferably, the lower-level torque distribution controller takes the required longitudinal braking torque and the required yaw torque as inputs and the tire utilization rate as the optimization target, and solves the required motor braking torque and required friction braking torque of each wheel after distribution.

[0025] Preferably, the weight coefficients of the required motor braking torque and the required friction braking torque of each wheel allocated by the lower-level torque distribution controller meet the following requirements:

[0026] 1) When the electric braking torque provided by the motor at the current motor speed can meet the braking torque required by the lower-level controller, the weight coefficient of the motor braking torque is 1, and the weight coefficient of the friction braking force is 0, that is, the required braking torque is only provided by the motor braking torque;

[0027] 2) When the electric braking torque that the machine can provide at the current motor speed cannot meet the braking torque required by the lower-level controller, the weight coefficient of the electric machine braking torque is increased first, and the electric machine braking torque is used first. The remaining required braking torque is supplemented by the friction braking torque, that is, the required braking torque is provided by both the electric machine braking torque and the friction braking torque.

[0028] A second aspect of the present invention provides a distributed drive electric vehicle compound braking control system for implementing the method of the first aspect, characterized in that it includes an upper controller, a lower controller and a vehicle model data monitoring unit;

[0029] The upper controller includes a longitudinal speed controller and a yaw moment controller. The longitudinal speed controller is used to calculate the braking intensity through the target speed and the actual speed of the electric vehicle, thereby obtaining the required longitudinal braking torque; the yaw moment controller is used to calculate the required yaw moment value according to the current parameters of the electric vehicle, thereby obtaining the yaw moment;

[0030] The lower controller includes a torque distribution controller, which is used to distribute the motor braking and friction braking of the electric vehicle;

[0031] The vehicle model data monitoring unit includes an ECU control module and a sensor module. The sensor module is used to collect vehicle parameters and transmit the data back to the ECU control module.

[0032] According to a third aspect of the present invention, a computer device is provided, comprising a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method steps of the first aspect or the system functions of the second aspect when executing the computer program.

[0033] According to a fourth aspect of the present invention, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the method steps of the first aspect or the system functions of the second aspect are implemented.

[0034] Compared with the prior art, the present invention has the following beneficial effects:

[0035] The present invention is based on the control of distributed drive electric vehicles, establishes an upper-level controller, a lower-level controller and a detection unit of the vehicle body itself and constitutes a cyclic detection process, wherein the upper-level controller constructs braking selection according to the timing and control time domain according to the actual driving road conditions, and then the lower-level controller completes the braking distribution work, and finally can complete energy recovery for the electric vehicle while maintaining the stability of the vehicle. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0037] Figure 1 It is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0038] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0039] like Figure 1 As shown, the present invention provides a distributed drive electric vehicle compound braking control method, the specific steps are as follows:

[0040] Step 1: Establish the upper longitudinal velocity tracking controller;

[0041] The longitudinal speed tracking controller takes the difference between the target speed of the electric vehicle and the actual speed of the electric vehicle at the current moment as input, and outputs the braking intensity through the PID controller. The required longitudinal braking torque is calculated according to the formula, where the calculation formula of the braking intensity z is as follows:

[0042]

[0043] Among them, K p , K i , K d are proportional, integral and differential coefficients respectively; v d is the target speed, v is the current actual speed;

[0044] The required longitudinal braking torque is calculated as follows:

[0045] T bref =zmgr w ;

[0046] Among them, z is the braking strength, m is the vehicle curb weight, g is the gravitational acceleration, r w is the wheel rolling radius;

[0047] Step 2: Establish the upper yaw moment stability controller;

[0048] Step 2.1: Establish a two-degree-of-freedom reference model of the vehicle;

[0049] A linear two-degree-of-freedom vehicle model is used as a reference model for vehicle stability. In order to better judge whether the vehicle can maintain good stability during braking, the vehicle yaw rate and center of mass sideslip angle are selected as evaluation indicators.

[0050] Ideal center of mass sideslip angle β in steady state d and the ideal yaw rate r d The formula is as follows:

[0051]

[0052] Among them, a and b are the distances from the center of mass of the vehicle to the front axle and rear axle respectively, and v x is the longitudinal velocity of the vehicle, k f , k r are the cornering stiffness of the front and rear axles, m is the vehicle curb weight, δ is the front wheel turning angle, and K is the stability factor:

[0053]

[0054] Step 2.2: MPC controller solves the required additional yaw moment

[0055] When a car is turning or driving on an open road, a yaw moment M is generated. z The two-degree-of-freedom vehicle model formula is as follows:

[0056]

[0057] in, is the lateral acceleration, r is the yaw angular velocity, is the yaw angular acceleration, Iz is the moment of inertia of the vehicle around the z axis, β is the sideslip angle of the center of mass, M z is the yaw moment.

[0058] Select the state variables center of mass sideslip angle β and yaw angular velocity r, and arrange the formula as follows:

[0059]

[0060] Choose the state equation x = [βr] T , output equation y = [βr] T , input u=M Z , the front wheel steering angle δ is a known quantity, which is used as the system disturbance and is written as the following state equation:

[0061]

[0062] In the above formula:

[0063]

[0064] ψ=δ;

[0065]

[0066] Use Euler method to discretize the continuous system state equation:

[0067]

[0068] In the above formula The system sampling time Δt, in order to eliminate the interference ψ(t), redefine the state variable as the increment of the original state variable and the input as the increment of the original input:

[0069]

[0070] The incremental state equation is:

[0071]

[0072] The MPC controller predicts the time domain as N P , the control time domain is N C ,Assume that the interference value ψ(t) does not change after time t, that is, Δψ(t+j)=0,j≥1, then t+1,t+2,t+3,…,t+N p The corresponding discrete states are expressed as follows:

[0073]

[0074] The constraint output of the controlled system is:

[0075]

[0076] The above prediction equation can be rewritten as:

[0077] Y(t)=M t Δx(t)+H t ΔU(t)+Z t ΔΓ(t)+Ty(t);

[0078] in,

[0079]

[0080] The ideal yaw rate and ideal sideslip angle calculated in step 2.1 are used as the reference input of the MPC model predictive controller. The reference input at time t is denoted as w(t) = [β d (t),r d (t)] T , the reference input in the control time domain is W(t)=[w(t+1) T w(t+2) T … w(t+p) T ] T In order to achieve the ideal control effect, the actual yaw rate and the actual sideslip angle of the vehicle are allowed to coincide with the corresponding ideal values ​​more quickly. Considering that the control amount of the vehicle should not fluctuate greatly, the performance index function is selected to minimize the square of the difference between the ideal value and the actual value of the two state variables and the sum of the squares of the control input, that is:

[0081]

[0082] Set the constraints as follows:

[0083]

[0084] In the above formula, Q = diag[q β q r ] is the weight matrix of the output variable, where q β ,q r represent the weights of the sideslip angle and yaw rate, respectively. is the weight matrix of the control variables, where Represents the weight of the yaw moment.

[0085] By changing the weight matrix, the performance index function can be optimized. After calculating the optimal output, the first time domain element in the sequence is taken as the control output, that is, the required yaw moment value.

[0086] Step 3: Establish the lower-level torque distribution controller

[0087] Establish the lower layer torque distribution controller to complete the required longitudinal torque T in the above steps bref and yaw moment M Z Coordinate allocation.

[0088] T bref =F x ·r w ;

[0089] According to the longitudinal dynamics equation of the vehicle:

[0090]

[0091] Where, T bref is the total required longitudinal moment, F x is the total required longitudinal force of the wheel, F xfl ,F xfr ,F xrl ,F xrr They are the longitudinal force of the left front wheel, the longitudinal force of the right front wheel, the longitudinal force of the left rear wheel, the longitudinal force of the right rear wheel, and d is the wheel track.

[0092] The relationship between the longitudinal force of each wheel and the motor braking torque and friction braking torque is as follows:

[0093]

[0094] In the above formula, T mfl ,T mfr ,T mrl ,T mrr are the motor torques of the left front wheel, right front wheel, left rear wheel, and right rear wheel respectively, T hfl ,T hfr ,T hrl ,T hrr They are the friction braking torques of the left front wheel, right front wheel, left rear wheel, and right rear wheel, respectively, r w is the wheel rolling radius.

[0095] Describing the above formula in matrix form, we get v = B·u

[0096]

[0097] v=[F x M Z ] T

[0098]

[0099] In order to reasonably and efficiently distribute the torque acting on the four wheels and improve the stability of the vehicle, the tire utilization rate is taken as the optimization target. After simplifying the lateral force, the optimization objective function is obtained as follows: Respectively represent the left front wheel, right front wheel, left rear wheel and right rear wheel, F xi is the longitudinal force of the four wheels, F zi is the vertical force of the four wheels, and μ is the road adhesion coefficient.

[0100] According to the constraints and the optimization objective function, we have By solving, the return value is the required motor force and required friction braking force after allocation. Respectively represent the left front wheel, right front wheel, left rear wheel and right rear wheel; among them, T max is the maximum torque of the motor.

[0101]

[0102] In order to take into account the effect of braking energy recovery, the motor braking should be used as much as possible. Under the premise of ensuring braking stability, the weight matrix can be improved. The distribution weight of motor braking in the process.

[0103] Specifically, when the vehicle is driving, the driver perceives the traffic information of the surrounding roads through vision and hearing, and collects the current motion state of the vehicle through electronic auxiliary equipment such as the dashboard or navigator in the car. After making logical decisions in the brain, the driver manipulates the steering wheel, accelerator pedal, brake pedal and other components to manipulate the movement of the vehicle. Therefore, it is a very important part of modeling and simulation. The upper longitudinal speed tracking controller of the present invention controls the vehicle speed. The difference between the target vehicle speed and the actual vehicle speed at the current moment is used as input, and the braking intensity is output through the PID controller, thereby calculating the required longitudinal braking torque.

[0104] When a car is turning or driving on a split road, a yaw moment is generated. The upper yaw moment controller of the present invention first designs a linear two-degree-of-freedom vehicle model as a reference model for vehicle stability, and selects the vehicle yaw angular velocity and the center of mass slip angle as evaluation indicators. The ideal yaw angular velocity and the ideal center of mass slip angle calculated by the two-degree-of-freedom vehicle model are used as the reference input of the MPC model predictive controller. Through the model predictive control method, the first time domain element in the optimized output sequence is taken as the model predictive control output, that is, the required yaw moment value.

[0105] The calculated longitudinal braking torque and yaw torque are further distributed in the lower-level torque distribution controller. In order to reasonably and efficiently distribute the torque acting on the four wheels and improve the stability of the vehicle, the tire utilization rate is used as the optimization objective function, the longitudinal torque demand, yaw torque demand, the maximum torque available from the motor, and the maximum torque that the road surface can provide are used as constraints, and the motor force and friction braking force of each wheel are used as control variables. The effective set algorithm is used to solve the objective function and complete the longitudinal braking torque and yaw torque distribution.

[0106] The weight coefficients of the required motor torque and required friction braking torque of each wheel allocated by the lower-level torque distribution controller meet the following requirements:

[0107] 1) When the electric braking torque provided by the motor at the current motor speed can meet the braking torque required by the lower-level controller, the weight coefficient of the motor braking torque is 1, and the weight coefficient of the friction braking force is 0, that is, the required braking torque is only provided by the motor braking torque;

[0108] 2) When the electric braking torque that the machine can provide at the current motor speed cannot meet the braking torque required by the lower-level controller, the weight coefficient of the electric machine braking torque is increased first, and the electric machine braking torque is used first. The remaining required braking torque is supplemented by the friction braking torque, that is, the required braking torque is provided by both the electric machine braking torque and the friction braking torque.

[0109] The lower-level torque distribution controller distributes the motor force and friction braking force of each wheel to input into the simulation vehicle model, thereby obtaining information such as the actual vehicle speed.

[0110] In another embodiment, a distributed drive electric vehicle compound brake control system is provided, comprising an upper controller, a lower controller and a vehicle model data monitoring unit;

[0111] The upper controller includes a longitudinal speed controller and a yaw moment controller. The longitudinal speed controller is used to calculate the braking intensity through the target speed and the actual speed of the electric vehicle, thereby obtaining the required longitudinal braking torque; the yaw moment controller is used to calculate the required yaw moment value according to the current parameters of the electric vehicle, thereby obtaining the yaw moment;

[0112] The lower controller includes a torque distribution controller, which is used to distribute the motor braking and friction braking of the electric vehicle;

[0113] The vehicle model data monitoring unit includes an ECU control module and a sensor module. The sensor module is used to collect vehicle parameters and transmit the data back to the ECU control module.

[0114] In another embodiment, a computer device includes a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above method or the above system functions when executing the computer program.

[0115] In another embodiment, a computer-readable storage medium stores a computer program, which implements the steps of the above method or the above system functions when executed by a processor.

[0116] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A distributed drive electric vehicle compound braking control method, characterized in that: The specific steps include: S1. Establish an upper controller, which includes a longitudinal speed tracking controller and a yaw torque controller, wherein the longitudinal speed tracking controller takes the target vehicle speed and the actual vehicle speed as input, obtains the braking intensity by calculation, and then obtains the longitudinal torque; The yaw moment controller calculates the required yaw moment value based on the current vehicle parameter data as input to obtain the yaw moment; S2. Establish a lower controller, which coordinates and distributes the longitudinal torque and yaw torque obtained in step S1 through a torque distribution controller, and distributes the motor torque and friction braking torque of the left front wheel, the right front wheel, the left rear wheel, and the right rear wheel respectively; S3. Establish a vehicle model data monitoring unit to collect the actual speed and vehicle parameters of the current electric vehicle. The vehicle parameters include the current vehicle mass, gravity acceleration and wheel rolling radius, and output them to the upper controller of step S1 in real time to complete the loop calculation.

2. A distributed drive electric vehicle compound braking control method according to claim 1, characterized in that: Step S1 also includes: S11. The longitudinal speed tracking controller calculates the braking intensity by a PID control method, thereby obtaining the required longitudinal braking torque; S12. The yaw moment controller uses the ideal yaw angular velocity and the ideal sideslip angle of the center of mass of the two-degree-of-freedom vehicle model as the reference input of the MPC model predictive controller. Through the model predictive control method, the first time domain element in the optimized output sequence is taken as the model predictive control output, that is, the required yaw moment value.

3. A distributed drive electric vehicle compound braking control method according to claim 2, characterized in that: The braking intensity in step S11 is z, which specifically includes: Among them, K p ,K i ,K d are proportional, integral and differential coefficients respectively; v d is the target speed, v is the current actual speed; The longitudinal braking torque specifically includes: T bref =zmgr w ; Among them, z is the braking strength, m is the vehicle curb weight, g is the gravitational acceleration, r w is the wheel rolling radius.

4. A distributed drive electric vehicle compound braking control method according to claim 2 or 3, characterized in that: The parameters of the yaw rate and the ideal center of mass sideslip angle in step S12 specifically include: Among them, β d is the ideal center of mass sideslip angle, r d is the ideal yaw rate, a and b are the distances from the center of mass of the vehicle to the front and rear axles, respectively, and v x is the longitudinal velocity of the vehicle, k f ,k r are the cornering stiffness of the front and rear axles, m is the curb weight of the vehicle, δ is the front wheel turning angle, and K is the stability factor. The stability factor specifically includes: Among them, k r is the rear axle cornering stiffness, k f is the front axle cornering stiffness.

5. A distributed drive electric vehicle compound braking control method according to claim 4, characterized in that: In S12, based on the yaw moment generated by the car when turning or driving on an open road, the yaw acceleration and inertial momentum are calculated for the two-degree-of-freedom vehicle model, and the MPC controller constructs a braking control time domain based on the timing model according to the timing increment. By selecting the braking control time domain of the timing model, the actual yaw angular velocity and the actual sideslip angle of the center of mass of the vehicle are made to coincide with the corresponding ideal sideslip angle of the center of mass and the ideal yaw angular velocity.

6. A distributed drive electric vehicle compound braking control method according to claim 1 or 2, characterized in that: The lower-level torque distribution controller takes the required longitudinal braking torque and the required yaw torque as input and the tire utilization rate as the optimization target to solve the required motor braking torque and required friction braking torque of each wheel after allocation.

7. A distributed drive electric vehicle compound braking control method according to claim 6, characterized in that: The weight coefficients of the required motor torque and required friction braking torque of each wheel allocated by the lower-level torque distribution controller meet the following requirements: 1) When the electric braking torque provided by the motor at the current motor speed can meet the braking torque required by the lower-level controller, the weight coefficient of the motor braking torque is 1, and the weight coefficient of the friction braking force is 0, that is, the required braking torque is only provided by the motor braking torque; 2) When the electric braking torque that the machine can provide at the current motor speed cannot meet the braking torque required by the lower-level controller, the weight coefficient of the electric machine braking torque is increased first, and the electric machine braking torque is used first. The remaining required braking torque is supplemented by the friction braking torque, that is, the required braking torque is provided by both the electric machine braking torque and the friction braking torque.

8. A distributed drive electric vehicle composite brake control system, used to implement the method described in any one of claims 1 to 5, characterized in that: It includes an upper controller, a lower controller and a vehicle model data monitoring unit; The upper controller includes a longitudinal speed controller and a yaw moment controller. The longitudinal speed controller is used to calculate the braking intensity through the target speed and the actual speed of the electric vehicle, thereby obtaining the required longitudinal braking torque; the yaw moment controller is used to calculate the required yaw moment value according to the current parameters of the electric vehicle, thereby obtaining the yaw moment; The lower controller includes a torque distribution controller, which is used to distribute the motor braking and friction braking of the electric vehicle; The vehicle model data monitoring unit includes an ECU control module and a sensor module. The sensor module is used to collect vehicle parameters and transmit the data back to the ECU control module. A computer device comprises a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of any one of the methods of claims 1 to 5 or the functions of the system of claim 6 when executing the computer program.

9. A computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the method according to any one of claims 1 to 5 or the functions of the system according to claim 6.

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