Regenerative braking optimization method and system based on energy recovery trend failure judgment

Abstract

The invention discloses a regenerative braking optimization method and system based on energy recovery trend failure judgment. The regenerative braking optimization method comprises the steps that state information of a vehicle in the driving process is obtained through an in-vehicle sensor system and a state estimator; the energy recovery trend failure judgment module establishes an energy recovery trend model according to the received data, judges an energy recovery trend failure state and sends a signal to the control module; if the vehicle does not enter the trend failure state, the vehicle continues a normal regenerative braking control process; if the vehicle enters a trend failure state, under the condition that the braking force of an existing motor is not changed, the braking performance is guaranteed while the recovery efficiency is met by adjusting the braking force distribution coefficient of a front axle and a rear axle of the vehicle, considering the vehicle state and fully utilizing ground attachment conditions; and the execution module executes according to the instruction of the control module. When the braking energy recovery trend is failed, an effective dynamic adjustment mechanism is entered, and the energy recovery efficiency and the braking performance stability are guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of automobile energy recovery, and in particular to a regenerative braking optimization method and system based on energy recovery trend exhaustion judgment. Background Art

[0002] With the widespread use of electric vehicles, regenerative braking technology has become a key link in improving energy utilization. During the deceleration or braking process of electric vehicles, the motor can convert the vehicle's kinetic energy into electrical energy and recharge it back to the battery, thereby extending the vehicle's cruising range. However, traditional regenerative braking control strategies are often relatively simple, making it difficult to fully utilize the maximum efficiency of regenerative braking under complex driving conditions, and lack an effective dynamic adjustment mechanism when the braking energy recovery trend is exhausted, resulting in limited energy recovery efficiency and insufficient stability of braking performance. In this case, part of the braking force that the vehicle originally relied on the energy recovery system will be weakened, resulting in problems such as extended braking distance and increased wear of the braking system. In addition, the lack of braking performance stability caused by the exhaustion of the braking energy recovery trend will make the feel of the brake pedal unpredictable. A normal braking system should provide a linear braking feel, that is, there is a stable correspondence between the force the driver presses on the brake pedal and the deceleration of the vehicle. However, when there is a problem with the energy recovery system, especially when there is a lack of adjustment mechanism near the limit, the feel of the brake pedal may become very "soft" or "hard". For example, during braking, the driver may feel a sudden change in pedal force, which is due to problems in the coordination between the energy recovery system and the mechanical braking system. This abnormal pedal feel will interfere with the driver's judgment of braking operation, resulting in untimely braking or excessive braking.

[0003] The defects currently existing in the vehicle regenerative braking system control method include:

[0004] 1. The vehicle energy recovery efficiency is limited and the recovery potential is not fully tapped. Actual driving conditions are complex and changeable. As the vehicle speed decreases, the motor power will also decrease. When the vehicle speed drops to the point where the motor output is lower than the upper limit of the battery charging power, mechanical braking has to intervene, resulting in the inability to fully utilize the kinetic energy and greatly reducing the energy recovery efficiency. In addition, the lack of an accurate vehicle braking mathematical model makes it difficult to accurately evaluate and predict the braking energy recovery potential under different working conditions, making it impossible to achieve more refined energy recovery control, resulting in some energy recovery opportunities being ignored.

[0005] 2. Poor braking comfort. The regenerative braking capacity will change with the vehicle speed and battery status. If it is not well coordinated with the basic braking, it will cause the deceleration to fluctuate during braking, affecting the comfort of the driver and passengers.

[0006] 3. The braking force distribution and coordinated control are complex. During the braking process, the braking force distribution between mechanical braking and regenerative braking needs to be precisely coordinated to achieve the best energy recovery and braking effect. However, since the vehicle's braking system is relatively complex and involves multiple components and control links, it is not easy to achieve perfect coordination between the two, and the braking force distribution strategy under different working conditions still needs to be further optimized. Summary of the invention

[0007] 1. Technical issues to be resolved

[0008] Based on the above problems, the present invention provides a regenerative braking optimization method and system based on energy recovery trend exhaustion judgment, which solves the problem that when the braking energy recovery trend is exhausted, there is a lack of effective dynamic adjustment mechanism, resulting in limited energy recovery efficiency, insufficient stability of braking performance, and abnormal feeling of the brake pedal.

[0009] (II) Technical solution

[0010] Based on the above technical problems, the present invention provides a regenerative braking optimization method based on energy recovery trend exhaustion judgment, comprising:

[0011] S1, using the in-vehicle sensor system and state estimator to obtain the state information of the vehicle driving process, and transmit the state information to the energy recovery trend failure judgment module and the control module, the state information includes the motor speed n r and vehicle acceleration a V ;

[0012] S2. The energy recovery trend failure judgment module establishes an energy recovery trend model based on the received data: calculates the regenerative braking power P at the current moment r , by continuous N p The regenerative braking energy E in the time interval Δt r Perform curve fitting, calculate the slope k of the energy recovery curve obtained by fitting, set the trend exhaustion threshold k0, if k≤k0, determine it as the energy recovery trend exhaustion state, and send a signal to the control module to enter step S4, otherwise, enter step S3;

[0013] S3, the control module controls the vehicle to continue the normal regenerative braking control process and enters step S5;

[0014] S4: The vehicle enters a trend exhaustion state. The control module adjusts the braking force distribution coefficient of the front and rear axles of the vehicle without changing the existing motor force, taking into account the vehicle state and making full use of the ground adhesion conditions, so as to meet the recovery efficiency while ensuring the braking performance: The constraints of the front and rear axle braking force distribution coefficient of the vehicle include:

[0015] ECE regulations restrict:

[0016]

[0017] Constraints of the ideal braking force distribution curve:

[0018]

[0019] Constraints on the road surface adhesion coefficient:

[0020]

[0021] Solve the braking performance optimization problem of electric vehicles when the energy recovery is close to the trend exhaustion state. The optimization goal is:

[0022]

[0023] According to the calculated optimal braking force distribution coefficient, the rear axle braking force is calculated as follows:

[0024] F br =F reg +F hvd =(1-β)F Xb

[0025]

[0026] Where β is the front axle braking force distribution coefficient, a and b are the horizontal distances from the front and rear axles to the center of mass, L is the distance between the front and rear axles, and h is g is the height from the ground to the center of mass, z is the braking intensity, defined as the vehicle acceleration a V The ratio of gravitational acceleration g, α m is the proportional coefficient, indicating that partial braking energy recovery is performed appropriately under the premise of ensuring braking stability, φ f and φ r are the utilization adhesion coefficients of the front and rear wheels respectively, μ is the road adhesion coefficient, F Xb is the total braking force, F br is the rear axle braking force, F reg is the motor regenerative braking force, F hvd is the front axle hydraulic braking force, i g is the transmission ratio, i o is the main reducer transmission ratio, η t is the mechanical efficiency of the transmission system and the wheel radius is r;

[0027] S5. The execution module executes according to the instructions of the control module and continuously monitors the status information of the vehicle.

[0028] Further, in S2, the calculation of the regenerative braking power P at the current moment rinclude:

[0029]

[0030] Where η m is the power generation efficiency of the motor, T m is the regenerative braking torque of the motor, T N is the rated torque of the motor, P n is the power of the motor, n r is the actual speed of the motor, n n is the rated speed of the motor.

[0031] Further, in S2, the regenerative braking energy E r for: The slope k is:

[0032] Furthermore, in S3, when the vehicle continues the normal regenerative braking control process, the vehicle braking force distribution coefficient is a fixed value set at the factory, and the control module determines the appropriate regenerative braking and mechanical braking ratio according to the current working conditions, and performs the braking operation through the execution module.

[0033] Furthermore, the state information also includes the vehicle speed v and the battery power SOC. When the battery power SOC exceeds the set threshold SOC max When the vehicle speed v is lower than the set threshold v min , regenerative braking will not be performed.

[0034] Furthermore, the present invention also discloses an electric vehicle regenerative braking optimization system based on energy recovery trend exhaustion judgment, and operates the electric vehicle regenerative braking optimization method based on energy recovery trend exhaustion judgment, including: an in-vehicle sensor system, a state estimator, an energy recovery trend exhaustion judgment module, a control module and an execution module.

[0035] Furthermore, the present invention also discloses a non-transitory computer-readable storage medium, wherein the non-transitory computer-readable storage medium stores computer instructions, and the computer instructions enable the computer to execute the method.

[0036] (III) Beneficial effects

[0037] The above technical solution of the present invention has the following advantages:

[0038] (1) The present invention realizes the refined monitoring and prediction of the regenerative braking energy recovery trend by judging whether the energy recovery of the electric vehicle has reached a trend exhaustion state. When it is judged that the trend exhaustion state has been reached, the front and rear axle braking force distribution coefficients of the electric vehicle will be dynamically adjusted, so as to maximize the energy recovery while ensuring the stability of the braking performance. The execution module strictly follows the instructions of the control module to accurately control the power generation state of the brake hydraulic system and the motor to ensure that the vehicle decelerates smoothly and the energy recovery process proceeds smoothly. During the entire braking process, the various modules cooperate with each other to effectively overcome the problems existing in the existing electric vehicle regenerative braking in many aspects such as energy recovery efficiency, braking comfort, braking force distribution and control. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] The features and advantages of the present invention will be more clearly understood by referring to the accompanying drawings, which are schematic and should not be construed as limiting the present invention in any way. In the accompanying drawings:

[0040] Figure 1 A flow chart of a regenerative braking optimization method based on energy recovery trend exhaustion according to an embodiment of the present invention;

[0041] Figure 2 A reference structural diagram of a rear-wheel drive electric vehicle according to an embodiment of the present invention;

[0042] Figure 3 This is a vehicle braking force analysis diagram according to an embodiment of the present invention. DETAILED DESCRIPTION

[0043] The specific implementation of the present invention is further described in detail below in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0044] The overall process of the present invention is as follows Figure 1 As shown, it includes the acquisition of vehicle driving state information, the establishment of energy recovery trend model, the adjustment of the braking force distribution coefficient of the front and rear axles of the vehicle, and the braking execution and system monitoring. The present invention takes a two-axle rear-drive electric vehicle as an example. Figure 2 As shown, the specific steps are as follows:

[0045] S1. Using the in-vehicle sensor system and the state estimator to obtain the state information of the vehicle driving process, the state information includes the vehicle speed v, the motor speed n, the battery power SOC, and the vehicle acceleration a V And other parameter information, and transmit these status information to the energy recovery trend exhaustion judgment module and the control module.

[0046] S2. The energy recovery trend failure judgment module establishes an energy recovery trend model based on the received data: calculates the regenerative braking power P at the current moment r , by continuous Np The regenerative braking energy E in the time interval Δt r Perform curve fitting, calculate the slope k of the fitted energy recovery curve, set the trend exhaustion threshold k0, if k≤k0, it is determined to be an energy recovery trend exhaustion state, and send a signal to the control module, otherwise, the vehicle continues the normal regenerative braking control process.

[0047] S21, first calculate the regenerative braking power P at the current moment r , the calculation formula is as follows:

[0048]

[0049] Where η m is the power generation efficiency of the motor, T m is the regenerative braking torque of the motor, expressed as:

[0050]

[0051] Where, T N is the rated torque of the motor, P n is the power of the motor, n r is the actual speed of the motor, n n is the rated speed of the motor.

[0052] S22, through the continuous N p The regenerative braking energy E within a time interval Δt (for example, data is collected every 0.1s) r Perform curve fitting, E r for

[0053]

[0054] The energy recovery curve can be fitted by the least squares method and its slope can be calculated.

[0055] S23. Set the trend exhaustion threshold k0. If |k|≤k0, it is determined to be an energy recovery trend exhaustion state, and a signal is sent to the control module to enter step S4. Otherwise, the vehicle continues the normal regenerative braking control process and enters step S3.

[0056] When the slope k approaches zero, it indicates that the trend exhaustion state is about to begin. Therefore, the trend exhaustion threshold k0 is set. If |k|≤k0, it is determined to be an energy recovery trend exhaustion state and a signal is sent to the control module. If the threshold is not reached, the vehicle continues the normal regenerative braking control process.

[0057] During braking, the power battery acts as an energy storage device and performs regenerative braking to recover energy. However, when the following conditions occur, the regenerative braking control mode is not entered, and the judgment of whether to enter the trend exhaustion state is not performed:

[0058] If the battery SOC is already at a high level, the regenerative braking mode will not be exited, which will cause overcharging, thereby increasing the heat and pressure of the battery, and further affecting its safety and life. In order to ensure the safety of the power battery and extend its service life, the recovery of braking energy will be stopped when the SOC exceeds a certain threshold. The threshold selected by the present invention is SOC max =0.95, that is, when SOC reaches or exceeds 95%, the regenerative braking energy is stopped. Therefore, the regenerative braking torque is corrected as follows:

[0059]

[0060] In addition, to ensure the stability and reliability of the braking effect, when the vehicle speed v is low, regenerative braking is not performed. Generally, v is selected. min =20km / h.

[0061] S3, the vehicle continues the normal regenerative braking control process and enters step S5;

[0062] When the slope k does not reach the threshold and the vehicle continues the normal regenerative braking control process, the vehicle braking force distribution coefficient is a fixed value set at the factory. The control module determines the appropriate regenerative braking and mechanical braking ratio according to the current working conditions, and performs the braking operation through the execution module. In the normal regenerative braking stage, based on the vehicle's driving speed and braking requirements, for example, when driving at low speed and braking lightly, a larger proportion of regenerative braking is preferred.

[0063] S4: Enter the trend exhaustion state, and adjust the braking force distribution coefficient of the front and rear axles of the vehicle, taking into account the vehicle state and making full use of the ground adhesion conditions, so as to meet the recovery efficiency while ensuring the braking performance.

[0064] When the trend is exhausted, the braking force provided by the motor will decrease. Blindly applying mechanical braking force will cause the driver's brake pedal to change and cause misoperation. Directly cutting off the electric braking force and using hydraulic braking force will reduce the energy recovery efficiency. However, by adjusting the braking force distribution coefficient of the front and rear axles of the vehicle, considering the vehicle state and making full use of the ground adhesion conditions, the recovery efficiency can be met while ensuring the braking performance.

[0065] like Figure 3 As shown in the figure, when the vehicle brakes, the moment balance analysis of the vehicle and the front and rear wheel contact points is:

[0066]

[0067] In the formula, F Zf and F Zr are the normal forces on the front and rear wheels respectively; a and b are the horizontal distances from the front and rear axles to the center of mass respectively; L is the distance between the front and rear axles; G is the vehicle gravity; du / dt is the vehicle deceleration; h g is the height from the ground to the center of mass; z is the braking intensity, defined as the vehicle deceleration a V The ratio of gravitational acceleration g is:

[0068]

[0069] During braking, if the braking intensity is large enough, the front and rear wheels will be locked and slip. At this time, the total braking force F Xb =Gμ, and du / dt=μg (μ is the road adhesion coefficient), then equations (4) and (5) can be written as follows:

[0070]

[0071] According to equations (4) to (8), as the braking intensity increases, the normal reaction force F of the front wheel Zf Increase, the normal reaction force F of the rear wheel Zr When the braking intensity approaches the road adhesion coefficient, the front and rear wheels will lock and slip, and the vehicle can provide maximum braking force.

[0072] In order to achieve the required braking force, the braking system will apply braking force to the front and rear axles at the same time. The distribution ratio is usually determined by comparing the braking force applied by the front axle with the total braking force, which is expressed as β:

[0073]

[0074] In the formula, F μ is the total braking force of the vehicle brake; F μ1 F is the front axle brake force; μ2 Provides braking force for the rear axle brake.

[0075] When the driver brakes, if the front wheel is close to locking or both the front and rear wheels are close to locking, the braking force applied by the front wheel brake is equal to the braking force F provided by the road surface to the front wheel. Xb1 . Therefore, the following relationship holds:

[0076]

[0077] Similarly, when the rear wheel is close to locking or both the front and rear wheels are close to locking, the braking force F provided by the road surface to the rear wheel can be calculated. Xb2 :

[0078]

[0079] When the car applies braking force to produce a certain deceleration braking, the key to ensure that the wheels do not lock is that the braking force cannot exceed the adhesion that the wheels can provide. If the braking force is greater than the road adhesion, the wheels may lock, resulting in loss of control. Therefore, when the car's adhesion coefficient to the road is close to or greater than the braking intensity, it means that the road provides enough grip to support the braking force, thereby avoiding the occurrence of wheel lock. At this time, the adhesion conditions of the ground are fully utilized, and the braking force distribution of the car is more reasonable. In order to quantitatively describe the degree of utilization of the road adhesion conditions, the concept of utilization adhesion coefficient φ is introduced. i

[0080]

[0081] In the formula, F Xbi is the ground braking force on the i-th axle of the vehicle, F Zi is the normal reaction force exerted by the ground on the i-th axis.

[0082] The front and rear wheel adhesion coefficients when the front or rear wheels are locked in advance or the front and rear wheels are locked at the same time can be obtained as follows:

[0083]

[0084] In the formula, φ f and φ r They are the utilized adhesion coefficients of the front and rear wheels respectively. When the utilized adhesion coefficient is close to the braking strength, the adhesion conditions of the road surface can be used more effectively, thus achieving a more reasonable braking force distribution.

[0085] To ensure the safety of vehicle braking, the braking force distribution must meet ECE regulations:

[0086] (1) In the range of braking intensity z = 0.15 to 0.8, the front axle's utilization adhesion coefficient should be greater than the rear axle's utilization adhesion coefficient;

[0087] (2) In the range of road adhesion coefficient 0.2 to 0.8, the braking intensity satisfies: z ≥ 0.1 + 0.85 (μ - 0.2);

[0088] In the ECE regulations, Article (1) limits the stability of the braking direction during the braking process of the vehicle, and Article (2) imposes certain constraints on the braking efficiency of the vehicle, and the following inequality constraints can be obtained. Substituting μ = 0.2 and μ = 0.8 into z ≥ 0.1 + 0.85 (μ - 0.2) respectively, the range of z is 0.1 ≤ z ≤ 0.61, and the utilization adhesion coefficient of the front and rear wheels is:

[0089]

[0090] When the braking intensity is 0.15≤z≤0.8, the utilization adhesion coefficient of the front and rear wheels meets the condition:

[0091] φ f ≥φ r (16)

[0092] Substituting equations (13) to (14) into equations (15) and (16), we obtain the following inequality set:

[0093]

[0094] Under the constraints of ECE regulations, the adhesion utilization coefficient of the front wheels is always greater than that of the rear wheels, thus ensuring braking stability.

[0095] For ideal braking force distribution, the front and rear wheels reach the locking limit at the same time, resulting in the front and rear wheels having the same utilized adhesion coefficient. Therefore, the braking force distribution between the front and rear wheels follows the following relationship:

[0096]

[0097] The braking force distribution curve described by equation (18) is the I curve, which represents the ideal braking force distribution curve. During vehicle braking, it is unsafe for the rear wheels to lock first. If the braking force is distributed in the range above the I curve, the rear wheels will lock first. Therefore, the braking force should be distributed in the range below the I curve to ensure braking safety:

[0098]

[0099] Then the constraint condition of braking force distribution ratio is:

[0100]

[0101] At the same time, during braking, the utilization coefficient of the wheel must be lower than the adhesion coefficient of the road surface. In addition, according to formula (19), the utilization coefficient of the front wheel should be greater than the utilization coefficient of the rear wheel. Therefore, the following only considers the constraint of the utilization coefficient of the front wheel, that is, the utilization coefficient of the front wheel should be less than the adhesion coefficient of the road surface, that is,

[0102] φ f <μ (21)

[0103] The constraint condition of the braking force distribution ratio can be obtained as follows:

[0104]

[0105] Therefore, in order to optimize the braking force distribution ratio, equations (17), (20) and (22) are used as constraints to solve the braking performance optimization problem when the electric vehicle energy recovery is in a state close to trend exhaustion. The optimization objectives are:

[0106] F target =min{(φ f -α m z) 2 +(φ r -α m z) 2} (twenty three)

[0107] Among them, α m It is the proportional coefficient, which means that partial braking energy recovery should be carried out appropriately under the premise of ensuring braking stability.

[0108] During braking, the total braking force in an electric vehicle is:

[0109]

[0110] Among them, F Xb is the total braking force, F bf is the front axle braking force, F br is the rear axle braking force, F reg is the motor regenerative braking force, F hvd Hydraulic braking force for the front axle.

[0111] According to the calculated optimal braking force distribution ratio, the rear axle braking force is calculated as follows according to formula (4):

[0112] F br =F reg +F hvd =(1-β)F Xb (25)

[0113]

[0114] Among them, i g is the transmission ratio, i o is the main reducer transmission ratio, η t is the mechanical efficiency of the drive train and the wheel radius is r.

[0115] Therefore, when the energy recovery of electric vehicles is in a state of exhaustion, the braking safety problem caused by the reduction of braking force can be avoided by adjusting the braking force distribution coefficient of the front and rear axles of the vehicle without changing the existing electric motor braking force.

[0116] S5. Braking execution and system monitoring: The execution module executes according to the instructions of the control module and continuously monitors the vehicle status information.

[0117] The execution module accurately controls the brake hydraulic system and the power generation state of the motor according to the instructions of the control module to ensure that the vehicle decelerates smoothly and the energy recovery process proceeds smoothly. During the entire braking process, the various parameters of the vehicle are continuously monitored. If abnormal conditions are found, the control strategy is adjusted in time to ensure that electric vehicles can use regenerative braking more effectively under different braking conditions, improve energy utilization and ensure the stability of braking performance.

[0118] Finally, it should be noted that the above-mentioned control method can be converted into software program instructions, which can be implemented by using a control system including a processor and a memory, or by computer instructions stored in a non-transitory computer-readable storage medium. The above-mentioned integrated unit implemented in the form of a software functional unit can be stored in a computer-readable storage medium. The above-mentioned software functional unit is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) or a processor (processor) to perform some steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, ROM), random access memory (Random Access Memory, RAM), disk or optical disk and other media that can store program codes.

[0119] In summary, the above-mentioned regenerative braking optimization method and system based on energy recovery trend exhaustion judgment has the following beneficial effects:

[0120] (1) The present invention realizes the refined monitoring and prediction of the regenerative braking energy recovery trend by judging whether the energy recovery of the electric vehicle has reached a trend exhaustion state. When it is judged that the trend exhaustion state has been reached, the front and rear axle braking force distribution coefficients of the electric vehicle will be dynamically adjusted, so as to maximize the energy recovery while ensuring the stability of the braking performance. The execution module strictly follows the instructions of the control module to accurately control the power generation state of the brake hydraulic system and the motor to ensure that the vehicle decelerates smoothly and the energy recovery process proceeds smoothly. During the entire braking process, the various modules cooperate with each other to effectively overcome the problems existing in the existing electric vehicle regenerative braking in many aspects such as energy recovery efficiency, braking comfort, braking force distribution and control.

[0121] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the embodiments of the present invention are described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present invention, and such modifications and variations shall fall within the scope defined by the appended claims.

Claims

1. A regenerative braking optimization method based on energy recovery trend exhaustion judgment, characterized in that: include: S1, using the in-vehicle sensor system and state estimator to obtain the state information of the vehicle driving process, and transmit the state information to the energy recovery trend failure judgment module and the control module, the state information includes the motor speed n r and vehicle acceleration a V ; S2. The energy recovery trend failure judgment module establishes an energy recovery trend model based on the received data: calculates the regenerative braking power P at the current moment r , by continuous N p The regenerative braking energy E in the time interval Δt r Perform curve fitting, calculate the slope k of the energy recovery curve obtained by fitting, set the trend exhaustion threshold k0, if k≤k0, determine it as the energy recovery trend exhaustion state, and send a signal to the control module to enter step S4, otherwise, enter step S3; S3, the control module controls the vehicle to continue the normal regenerative braking control process and enters step S5; S4: The vehicle enters a trend exhaustion state. The control module adjusts the braking force distribution coefficient of the front and rear axles of the vehicle without changing the existing motor force, taking into account the vehicle state and making full use of the ground adhesion conditions, so as to meet the recovery efficiency while ensuring the braking performance: The constraints of the front and rear axle braking force distribution coefficient of the vehicle include: ECE regulations restrict: Constraints of the ideal braking force distribution curve: Constraints on the road surface adhesion coefficient: Solve the braking performance optimization problem of electric vehicles when the energy recovery is close to the trend exhaustion state. The optimization goal is: According to the calculated optimal braking force distribution coefficient, the rear axle braking force is calculated as follows: F br =F reg +F hvd =(1-β)F Xb Where β is the front axle braking force distribution coefficient, a and b are the horizontal distances from the front and rear axles to the center of mass, L is the distance between the front and rear axles, and h is g is the height from the ground to the center of mass, z is the braking intensity, defined as the vehicle acceleration a V The ratio of gravitational acceleration g, α m is the proportional coefficient, indicating that partial braking energy recovery is performed appropriately under the premise of ensuring braking stability, φ f and φ r are the utilization adhesion coefficients of the front and rear wheels respectively, μ is the road adhesion coefficient, F Xb is the total braking force, F br is the rear axle braking force, F reg is the motor regenerative braking force, F hvd is the front axle hydraulic braking force, i g is the transmission ratio, i o is the main reducer transmission ratio, η t is the mechanical efficiency of the transmission system and the wheel radius is r; S5. The execution module executes according to the instructions of the control module and continuously monitors the status information of the vehicle.

2. The regenerative braking optimization method based on energy recovery trend exhaustion judgment according to claim 1 is characterized in that: In S2, the calculation of the regenerative braking power P at the current moment r include: Where η m is the power generation efficiency of the motor, T m is the regenerative braking torque of the motor, T N is the rated torque of the motor, P n is the power of the motor, n r is the actual speed of the motor, n n is the rated speed of the motor.

3. The regenerative braking optimization method based on energy recovery trend exhaustion judgment according to claim 1 is characterized in that: In S2, the regenerative braking energy E r for: The slope k is:

4. The regenerative braking optimization method based on energy recovery trend exhaustion judgment according to claim 1 is characterized in that: In S3, when the vehicle continues the normal regenerative braking control process, the vehicle braking force distribution coefficient is a fixed value set at the factory. The control module determines the appropriate regenerative braking and mechanical braking ratio according to the current working conditions, and performs the braking operation through the execution module.

5. The regenerative braking optimization method based on energy recovery trend exhaustion judgment according to claim 1 is characterized in that: The state information also includes the vehicle speed v and the battery power SOC. When the battery power SOC exceeds the set threshold SOC max When the vehicle speed v is lower than the set threshold v min , regenerative braking will not be performed.

6. A regenerative braking optimization system based on energy recovery trend exhaustion judgment, characterized in that: The regenerative braking optimization method based on energy recovery trend failure judgment as described in any one of claims 1 to 5 comprises: an in-vehicle sensor system, a state estimator, an energy recovery trend failure judgment module, a control module and an execution module.

7. A non-transitory computer-readable storage medium, characterized in that: The non-transitory computer-readable storage medium stores computer instructions, which cause the computer to execute the method according to any one of claims 1 to 5.

Images