A control method, an electrically driven vehicle, a storage medium, and a computer program product

Abstract

The application relates to the technical field of electric drive vehicles, and provides a control method, an electric drive vehicle, a storage medium and a computer program product. The control method is used for corner braking of an electric drive vehicle and comprises the following steps: controlling the electric drive vehicle to enter a race track mode; acquiring a corner curvature, a road surface adhesion coefficient and a driving wheel slip rate; calculating a brake pressure relief rate based on a nonlinear mapping model, wherein the nonlinear mapping model is: wherein the brake pressure relief rate is represented by b, the normalized corner curvature is represented by K, the normalized road surface adhesion coefficient is represented by mu, the normalized driving wheel slip rate is represented by S, alpha is in the range of [0.3, 0.45], beta is in the range of [0.15, 0.25], gamma is in the range of [0.25, 0.35], and C is in the range of [0.05, 0.1]; and controlling mechanical brake pressure to be relieved at the brake pressure relief rate and controlling motor braking. The relief strategy of the mechanical brake pressure can be more accurately matched with a corner limit state, and controllability is improved.

Description

Technical Field

[0001] This application relates to the field of electric vehicle technology, specifically to a control method, an electric vehicle, a storage medium, and a computer program product. Background Technology

[0002] Electric vehicles are vehicles that use electricity as a power source and drive the wheels through an electric motor. In related technologies, electric vehicles have the following drawbacks in cornering braking and power control: Traditional cornering control adjusts braking based on a single signal such as steering angle or yaw rate. However, a single signal is difficult to reflect the vehicle's true dynamics in a corner, resulting in a mismatch between the mechanical braking pressure relief strategy and the cornering limit state. Summary of the Invention

[0003] This application provides a control method, an electric vehicle, a storage medium, and a computer program product, which can more accurately match the pressure relief strategy of mechanical braking with the cornering limit state, thereby improving controllability.

[0004] The technical solution of this application embodiment is implemented as follows: This application provides a control method for cornering braking of an electric vehicle, including: Control the electric vehicle to enter track mode, wherein the track mode is the active braking control mode for the electric vehicle to enter a curve. By obtaining the curve curvature, road surface adhesion coefficient, and drive wheel slip ratio, the brake depressurization rate is calculated based on a nonlinear mapping model. The nonlinear mapping model is as follows: ,in, Let represent the brake depressurization rate, K represent the normalized curvature, μ represent the normalized road adhesion coefficient, S represent the normalized drive wheel slip ratio, α represent the weighting coefficient of the normalized curvature, β represent the weighting coefficient of the normalized road adhesion coefficient, γ represent the weighting coefficient of the normalized drive wheel slip ratio, and C represent the base offset compensation constant, where α∈[0.3,0.45], β∈[0.15,0.25], γ∈[0.25,0.35], and C∈[0.05,0.1]. Control the mechanical braking pressure to release pressure according to the braking pressure release rate, and control the motor braking.

[0005] The control method provided in this application embodiment can control an electric vehicle to enter a track mode when it enters a curve. In track mode, the curve curvature, road surface adhesion coefficient, and drive wheel slip ratio can be acquired in real time. After normalizing the curve curvature, road surface adhesion coefficient, and drive wheel slip ratio, they are substituted into a nonlinear mapping model to calculate the brake pressure relief rate. In this way, a three-factor normalized mapping of curve curvature, road surface adhesion coefficient, and drive wheel slip ratio can be established, which can more accurately match the tire adhesion limit. On the other hand, after entering the curve, the mechanical braking pressure is controlled to release according to the brake pressure relief rate, while the electric motor braking is controlled. In this way, the pressure relief strategy of the mechanical braking pressure can be more accurately matched with the curve limit state, improving controllability. Mechanical braking and electric braking recovery are realized in the curve, thereby achieving coupled and gradual release, making the braking force of the electric vehicle's wheels change more smoothly and reducing the impact in the curve.

[0006] In some embodiments, the control method includes: If the slip ratio difference between the inner and outer wheels is determined to be greater than the calibration threshold, asymmetric braking pressure compensation is applied to the inner and outer wheels.

[0007] In this embodiment, during cornering, the inner wheel has a low load, and under the same braking pressure, the slip ratio of the inner wheel is usually significantly higher than that of the outer wheel. If the difference in slip ratio between the inner and outer wheels is greater than the calibration threshold, it indicates that the slip state of the inner and outer wheels is too different, and it is determined that the electric vehicle has a tendency to understeer or fishtail. By applying asymmetrical braking pressure compensation to the inner and outer wheels, that is, applying different amounts of compensation hydraulic pressure to the inner and outer wheels, the slip ratio of the inner wheel and the slip ratio of the outer wheel are brought back to a reasonable range, thus achieving pre-control before yaw attitude instability, which is different from the post-instability correction of ESP.

[0008] In some embodiments, the amount of compensation for the asymmetric braking pressure is proportional to the difference in slip ratio.

[0009] In this embodiment, the compensation amount is proportional to the difference in slip ratio, and the pressure adjustment range can be dynamically matched according to the magnitude of the slip deviation between the two wheels: the larger the difference in slip ratio, the more serious the imbalance between the inner and outer wheel grip and slip state, and the corresponding compensation amount will increase accordingly; conversely, it will be slightly adjusted. In this way, excessive slip of the inner wheel is suppressed, wheel lock-up is avoided, and the vehicle's cornering ability and driving posture are effectively maintained.

[0010] In some embodiments, the compensation amount is 0.5 to 1.2 times the slip ratio difference.

[0011] In this embodiment, the compensation amount is set to 0.5 to 1.2 times the slip ratio difference. The adjustment range of braking pressure can be limited according to the degree of slip deviation between the inner and outer wheels. When the slip ratio difference is small, a smaller compensation amount is used to achieve subtle and smooth pressure correction, avoiding vehicle vibration and driving jerking caused by large fluctuations in braking force. When the slip ratio difference is large, the upper limit compensation amount can offset the slip difference between the two wheels to a certain extent, stabilizing the vehicle posture and steering performance in corners. This balances adjustment sensitivity, control safety, and driving comfort.

[0012] In some embodiments, the calibration threshold is 2%-5%.

[0013] In this embodiment, the calibration threshold is 2%-5%, which can trigger asymmetric braking pressure compensation in a timely manner. This can avoid the decrease in driving smoothness caused by repeated fine-tuning of braking pressure due to an excessively low calibration threshold, and also prevent the problem of excessive accumulation of slip deviation, severe slippage or even lock-up of the inner wheel due to an excessively high calibration threshold. In this way, the wheel slip imbalance state under cornering braking can be accurately identified, and the braking force distribution can be corrected in advance to ensure the stability and steering ability of cornering driving.

[0014] In some embodiments, the curve curvature, the road surface adhesion coefficient, and the drive wheel slip ratio are normalized based on the Pacejka tire magic formula to construct the nonlinear mapping model.

[0015] In this embodiment, the longitudinal / lateral adhesion mechanism is derived based on the classic Pacejka tire magic formula; then, multiple regression fitting is completed through multiple sets of track calibration data of real vehicles, such as 300 sets or more, to obtain the nonlinear mapping model of this application; wherein, the larger the normalized curve curvature, the lower the normalized road surface adhesion coefficient, the higher the normalized drive wheel slip ratio, the lower the brake pressure relief rate, and the more it conforms to the tire's extreme grip characteristics.

[0016] In some embodiments, the electric vehicle is determined to meet a first condition, and then the electric vehicle is controlled to enter the track mode. The first condition includes: vehicle speed of 60km / h-180km / h, brake master cylinder pressure ≥90bar and pressure rise rate ≥20bar / 10ms, steering angle ≥3° and steering angular velocity ≥5° / 10ms, and road surface adhesion coefficient of 0.85-1.0.

[0017] In this embodiment, the electric vehicle is determined to meet the first condition, namely, the vehicle speed is 60km / h-180km / h, the brake master cylinder pressure is ≥90bar and the pressure rise rate is ≥20bar / 10ms, the steering angle is ≥3° and the steering angular velocity is ≥5° / 10ms, and the road surface adhesion coefficient is 0.85-1.0. This indicates that the electric vehicle is in extreme cornering conditions, the track mode is activated, the braking system of the electric vehicle is disengaged from the driver's pedal and enters active control. While making full use of the road surface adhesion to ensure braking performance, the vehicle body stability and driving safety during high-speed emergency steering and braking are greatly improved.

[0018] In some embodiments, controlling the mechanical braking pressure to release pressure according to the braking pressure relief rate and controlling the motor braking include: The mechanical braking pressure is controlled to release pressure according to the braking pressure release rate, and the motor braking torque is controlled to decrease in the reverse direction, so that the total deceleration of the electric vehicle is within the first range.

[0019] In this embodiment, mechanical braking and electric braking recovery are achieved in the curve, and the mechanical braking force and electric braking force are dynamically complementary, so that the total deceleration is maintained within the first range, achieving a roughly constant coupling and release, reducing impact and fluctuation, stabilizing the wheel slip state and vehicle posture, and improving driving comfort while ensuring driving safety.

[0020] In some embodiments, the first range is 0.6 to 0.8 times the gravitational acceleration.

[0021] In this embodiment, during the coupling release process, the mechanical braking pressure relief and the motor braking torque decay are executed synchronously and in reverse. The total deceleration of the electric vehicle is 0.6 to 0.8 times the gravitational acceleration, with basically no impact or fluctuation.

[0022] In some embodiments, the control method includes: The exit point is determined based on the continuous decrease in the curvature of the curve and the steering return signal.

[0023] In this embodiment, based on the continuous decrease in curvature of the curve and the advance prediction of the exit point of the steering return signal, the defect of misjudging the exit point of a single signal can be avoided, and the timing of the vehicle's exit from the curve can be captured more accurately. This makes it easier to gradually cancel the asymmetric braking compensation of the inner and outer wheels in advance, and to simultaneously and smoothly reduce the mechanical braking pressure and the motor feedback torque. This allows the braking force to transition smoothly with the exit condition, preventing abnormal braking force distribution after exiting the curve from causing the vehicle body to pull or veer, and optimizing the smoothness of acceleration and the overall driving stability of the vehicle.

[0024] In some embodiments, after determining the bend point, the control method includes: The mechanical braking pressure is controlled to drop to 0 bar within the first duration, and a torque feedforward command is sent to the MCU in advance within the second duration.

[0025] In this embodiment, the mechanical braking pressure is controlled to drop to 0 bar within the first time period, and a torque feedforward command is sent to the MCU in advance within the second time period. In this way, when exiting a curve, the exit point is predicted in advance, and a torque feedforward command is sent to the power system in advance, so as to achieve zero-delay connection between brake release and power output, thereby improving the acceleration capability when exiting a curve.

[0026] In some embodiments, the control method includes: If the electric vehicle meets one of the second conditions, it exits the track mode. The second conditions include: the curvature of the curve is not greater than 0.005, the pedal travel is not greater than 5mm, and the yaw rate deviation is not greater than 0.5rad / s.

[0027] In this embodiment, corner exit is characterized by three dimensions: corner curvature, pedal travel, and yaw rate deviation. If any one of the following conditions is met, the corner exit is determined as complete, and the corner curvature is no greater than 0.005, the pedal travel is no greater than 5mm, and the yaw rate deviation is no greater than 0.5rad / s.

[0028] This application provides an electric vehicle, which includes a memory and a processor. The memory stores computer programs or instructions, and when the computer programs or instructions are executed by the processor, they implement the control method described above.

[0029] This application provides a storage medium storing a computer program or instructions, which, when executed by a processor, implements any of the control methods described above.

[0030] This application provides a computer program product, which includes a computer program or instructions. When the computer program or instructions are executed by a processor, they implement the control method described in any of the above-mentioned embodiments. Attached Figure Description

[0031] Figure 1 This is a flowchart illustrating the control method in some embodiments of this application; Figure 2 This is a timing diagram showing the coupling and slow-release of mechanical braking pressure and motor braking torque during a bend in some embodiments of this application; Figure 3 This is an architectural block diagram of a portion of the structure of an electric vehicle in some embodiments of this application.

[0032] Explanation of reference numerals in the attached figures 10. IBCU; 20. MCU; 30. VCU; 40. Wheel speed sensor; 50. Steering angle sensor; 60. Brake pressure sensor; 70. IMU sensor; 100. Motor braking torque; 200. Mechanical braking pressure; 300. Total deceleration. Detailed Implementation

[0033] To make the objectives, technical solutions, and advantages of this application clearer, embodiments of the technical solutions of this application will be described in detail below with reference to the accompanying drawings. The following embodiments are only used to illustrate the technical solutions of this application more clearly, and are therefore merely examples and should not be used to limit the scope of protection of this application.

[0034] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs; the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit this application.

[0035] In the description of the embodiments of this application, the technical terms "first", "second", etc. are used only to distinguish different objects and should not be construed as indicating or implying relative importance or implicitly indicating the number, specific order or primary and secondary relationship of the indicated technical features.

[0036] In this document, the term "embodiment" means that a specific feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it a mutually exclusive, independent, or alternative embodiment. It will be explicitly and implicitly understood by those skilled in the art that combinations can be made in any suitable manner without contradiction; for example, different combinations of specific technical features / embodiments can form different implementations. To avoid unnecessary repetition, the various possible combinations of specific technical features / embodiments in this application will not be described separately.

[0037] Please see Figure 3 The electric vehicle provided in this application includes a vehicle body, four wheels, a motor, an IBCU10, an MCU20, an ESP, a VCU30, and a wheel speed sensor 40. The four wheels are rotatably mounted on the vehicle body. The IBCU10, MCU20, ESP, VCU30, and wheel speed sensor 40 are all mounted on the vehicle body. The wheel speed sensor 40 is used to detect the wheel speed. The electric vehicle also has an independent control interface and execution capability for the braking system to be independent of the driver's pedal.

[0038] Electric vehicles use electric motors to drive the wheels.

[0039] IBCU10, short for Integrated Brake Control Unit, is a highly integrated hydraulic brake-by-wire system. It achieves electronic, rapid, and intelligent control of braking commands through electrical signals. It can receive signals such as steering, throttle, wheel speed, and cornering attitude, calculate mechanical braking force requirements, and control hydraulic or pneumatic braking circuits to achieve functions such as anti-lock braking and brake force distribution. At the same time, it interacts with MCU20 to coordinate mechanical braking and electric braking.

[0040] MCU20, short for Motor Control Unit, controls the vehicle's drive motors. It can receive braking or driving commands from the upper level, control the motor's operating status, and monitor the motor's speed, torque, temperature, etc.

[0041] ESP, short for Electronic Stability Program, uses sensors to monitor vehicle dynamics and automatically brakes individual wheels and adjusts motor torque to stabilize the driving trajectory when there is a risk of loss of control such as understeer, oversteer, or slippage. ESP can be integrated into IBCU10.

[0042] VCU30, short for Vehicle Control Unit, is the central hub for coordinating the vehicle's powertrain and integrates MCU20, IBCU10, and other components.

[0043] Please see Figure 1 The control method provided in this application embodiment is used for cornering braking of an electric vehicle, including: S1. Control the electric vehicle to enter the track mode, wherein the track mode is the active braking control mode for the electric vehicle to enter a curve. S2. Obtain the curve curvature, road surface adhesion coefficient, and drive wheel slip ratio. Based on a nonlinear mapping model, calculate the brake depressurization rate. The nonlinear mapping model is as follows: ,in, Let represent the brake depressurization rate, K represent the normalized curvature, μ represent the normalized road adhesion coefficient, S represent the normalized drive wheel slip ratio, α represent the weighting coefficient of the normalized curvature, β represent the weighting coefficient of the normalized road adhesion coefficient, γ represent the weighting coefficient of the normalized drive wheel slip ratio, and C represent the base offset compensation constant, where α∈[0.3,0.45], β∈[0.15,0.25], γ∈[0.25,0.35], and C∈[0.05,0.1]. S3. Control the mechanical braking pressure to release pressure according to the braking pressure release rate, and control the motor braking.

[0044] When an electric vehicle enters a curve, it can be controlled to enter track mode. In track mode, the curve curvature, road surface adhesion coefficient, and drive wheel slip ratio can be acquired in real time. After normalizing the curve curvature, road surface adhesion coefficient, and drive wheel slip ratio, these parameters are substituted into a nonlinear mapping model to calculate the brake pressure relief rate. The brake pressure relief rate represents the decrease in mechanical braking pressure per unit time.

[0045] Curvature of a curve is the curvature of the instantaneous trajectory of an electric vehicle.

[0046] Curve curvature can be obtained in a known manner. For example, real-time curve curvature can be calculated by fusing steering angle, yaw rate, wheel speed of the four wheels, and lateral acceleration.

[0047] Please see Figure 3 Electric vehicles include a steering angle sensor 50 or an angular velocity sensor, which can be used to measure steering angular velocity.

[0048] The coefficient of friction (COP) is the ratio of the maximum ground adhesion a wheel can achieve to the vertical load on the wheel, representing the tire's ultimate grip capability on a given road surface. The COP can be obtained using well-known methods.

[0049] The drive wheel slip ratio is the percentage of the difference between the drive wheel speed and the vehicle speed, expressed as a percentage of the drive wheel speed. It characterizes the degree to which the drive wheel slips relative to the ground. The drive wheel slip ratio can be obtained using well-known methods.

[0050] For example, the wheel speed of the drive wheel can be obtained through a drive wheel encoder, and the vehicle speed can be obtained through a sensor.

[0051] The unit for brake depressurization rate is bar / 10ms.

[0052] Understandably, the curve curvature has been normalized, with the normalized curve curvature K ranging from 0 to 1. The road surface adhesion coefficient has been normalized, with the normalized road surface adhesion coefficient μ ranging from 0 to 1. The drive wheel slip ratio has been normalized, with the normalized drive wheel slip ratio S ranging from 0 to 1.

[0053] α, β, γ, and C are calibration coefficients, where α is between 0.3 and 0.45, β is between 0.15 and 0.25, γ is between 0.25 and 0.35, and C is between 0.05 and 0.1. C represents the basic offset compensation constant, which is the compensation amount for the fixed errors inherent in the hardware conditions of the entire braking system, motor, and sensors of the electric vehicle. In other words, C is a fixed value that is inherent to the electric vehicle at the factory and will not change due to changes in the operating conditions of the electric vehicle. Regardless of changes in cornering or slippage, C always exists and is used to smooth out the fixed deviations introduced by the system, so that the calculated brake pressure relief rate matches the actual needs of the vehicle.

[0054] α, β, γ, and C can be calibrated by testing actual vehicles under different conditions of curve curvature, road surface adhesion coefficient, and drive wheel slip ratio. The curve curvature can be 0.01-0.05, the road surface adhesion coefficient can be 0.85-1.0, and the drive wheel slip ratio can be 5%-12%. Then, the curve curvature, road surface adhesion coefficient, and drive wheel slip ratio are normalized.

[0055] Understandably, the industry standard for track dynamics calibration uses a curvature range of 0.01-0.05 for cornering. A curvature of 0.01, or a radius of 100m, represents gentle curves and high-speed mountain roads. A curvature of 0.05, or a radius of 20m, represents sharp curves, S-curves, and hairpin bends. Curvatures below 0.01, or a radius greater than 100m, indicate minimal lateral acceleration and no significant tire load transfer, thus not falling under the cornering braking conditions of this application and not requiring inclusion in model calibration. Curvatures above 0.05, or an R-radius less than 20m, indicate that production vehicles cannot stably pass through speeds between 60km / h and 180km / h, exceeding the speed range of this application and thus lacking practical vehicle calibration value. Therefore, a curvature range of 0.01-0.05 can fully cover all commonly used curve types (gentle curves, medium curves, and sharp hairpin bends) required by this application, and constitutes the full coverage sample range for cornering braking in this application. The road surface adhesion coefficient can range from 0.85 to 1.0, covering all dry professional racetrack surfaces. For high-adhesion extreme racetrack conditions, this represents the effective adhesion range for track braking. Wet roads and low-adhesion conditions such as ice and snow are not within the scope of this application and are not included in the calibration. A drive wheel slip ratio of 0%-5% indicates the tire's linear zone, with sufficient grip and no risk of slippage, requiring no active pressure relief correction and not falling within the range of this application. A drive wheel slip ratio of 5%-12% indicates the tire has entered the pseudo-linear transition zone (the core operating range of this application), where the difference in slip between the inner and outer wheels begins to appear, requiring dynamic adjustment of the brake pressure relief rate. A drive wheel slip ratio greater than 12% indicates the tire has entered a highly nonlinear zone, with rapid attenuation of lateral force, making it extremely prone to understeer / fishtailing. The control method of this application intervenes in advance to limit slippage to no more than 12%.

[0056] Curve curvature is used to characterize the sharpness of a curve, road surface adhesion coefficient is used to characterize road surface grip, and drive wheel slip ratio is used to characterize the degree of wheel slippage. Since curve curvature, road surface adhesion coefficient, and drive wheel slip ratio are completely different in magnitude and have different dimensions, their weights cannot be directly calculated. Otherwise, the road surface adhesion coefficient, which has a larger value, would dominate, causing the influence of curve curvature and drive wheel slip ratio to be masked, thus leading to inaccurate control logic. Therefore, curve curvature, road surface adhesion coefficient, and drive wheel slip ratio are normalized. This helps to unify the scale and eliminate the influence of dimensions.

[0057] The normalized curvature K refers to the curvature of the curve after normalization. The formula for normalization is: Normalized curvature K = (Current measured value of curvature - Minimum value of curvature) / (Maximum value of curvature - Minimum value of curvature). The current measured value of curvature refers to the curvature obtained from the current actual measurement.

[0058] For example, the curvature of the gentlest curve (i.e., the minimum curvature) is 0.01, the curvature of the sharpest curve (i.e., the maximum curvature) is 0.05, the current measured value of the curvature is 0.038, and the normalized curvature K = (0.038 - 0.01) ÷ (0.05 - 0.01) = 0.028 ÷ 0.04 = 0.70.

[0059] The normalized road adhesion coefficient μ refers to the road adhesion coefficient after normalization. The normalization calculation formula is: Normalized road adhesion coefficient μ = (Current measured value of road adhesion coefficient - Minimum value of road adhesion coefficient) / (Maximum value of road adhesion coefficient - Minimum value of road adhesion coefficient). The current measured value of road adhesion coefficient refers to the road adhesion coefficient obtained from the current actual measurement.

[0060] For example, the minimum value of the road surface adhesion coefficient is 0.85, the maximum value of the road surface adhesion coefficient is 1.0, the current measured value of the road surface adhesion coefficient is 0.95, and the normalized road surface adhesion coefficient μ = (0.95 - 0.85) ÷ (1.0 - 0.85) = 0.1 ÷ 0.15 ≈ 0.67.

[0061] The normalized drive wheel slip ratio S refers to the drive wheel slip ratio after normalization. The normalization calculation formula is: Normalized drive wheel slip ratio S = (Current measured value of drive wheel slip ratio - Minimum value of drive wheel slip ratio) / (Maximum value of drive wheel slip ratio - Minimum value of drive wheel slip ratio). The current measured value of drive wheel slip ratio refers to the drive wheel slip ratio obtained from the current actual measurement.

[0062] For example, the minimum drive wheel slip ratio is 5%, the maximum drive wheel slip ratio is 12%, the current measured value of the drive wheel slip ratio is 8%, and the normalized drive wheel slip ratio S = (0.08 - 0.05) ÷ (0.12 - 0.05) = 0.03 ÷ 0.07 ≈ 0.43.

[0063] α represents the weighting coefficient of the normalized corner curvature, β represents the weighting coefficient of the normalized road surface adhesion coefficient, γ represents the weighting coefficient of the normalized drive wheel slip ratio, and C represents the basic offset compensation constant. The physical mechanism is based on Pacejka's tire magic formula: corner curvature has the strongest impact on vehicle lateral load transfer and tire ultimate grip, followed by drive wheel slip ratio, and the road surface adhesion coefficient has the weakest impact; C is a fixed compensation term of the system and has the smallest impact.

[0064] For example, the process of calibrating α, β, γ, and C by testing actual vehicles under different curve curvature, road surface adhesion coefficient, and drive wheel slip ratio conditions includes: constructing a design matrix using normalized data of multiple sets of curve curvature, road surface adhesion coefficient, and drive wheel slip ratio. Then, define the objective of minimizing the residual, derive the normal equation to find the inverse matrix, and finally calculate α, β, γ and C.

[0065] Take the normalized data of the i-th group of curve curvature, road surface adhesion coefficient, and drive wheel slip ratio, and represent it as (K i ,μ i ,S i The measured brake depressurization rate is expressed as: Substitute into the model: In this formula, α, β, and γ are coefficients to be determined, and the constant C is always equal to 1 for matrix operations. This represents the residual of the i-th sample group, which includes random errors that cannot be completely eliminated, such as sensor noise, line deviation, and minor tire disturbances.

[0066] By uniformly adding 1 to the independent variables corresponding to the basic offset compensation constant C, a design matrix of 300 rows × 4 columns is constructed. : ; Output truth vector R real (300 rows x 1 column) is: .

[0067] Truth vector R real The true vector of the measured brake pressure relief rate is a 300-row × 1-column vector composed of the actual pressure relief rates of 300 sets of actual vehicle calibration samples.

[0068] The complete matrix model is as follows: ;in, Let be the parameter vector of the model to be determined, and be the parameter vector of all unknown parameters to be calibrated. The target vector that needs to be solved for this system of regression equations is, i.e. .

[0069] Single residual value = measured brake pressure relief rate The model calculates the pressure relief rate. The measured braking pressure relief rate is the actual pressure relief rate measured on a real vehicle, while the model-calculated pressure relief rate is the pressure relief rate calculated using the current α, β, γ, and C.

[0070] The difference between each set of data is squared and then summed to obtain a total error. Regardless of whether the error is large or small, it will become a positive number and will not cancel out. Moreover, the larger the error, the larger the value after squaring. The algorithm will prioritize avoiding serious biases and use the least squares method to square and sum the differences between all predicted values ​​and the true values ​​to find a set of parameters that minimizes the total error, ensuring that the model best fits the measured data.

[0071] Single residual value = measured brake pressure relief rate The decompression rate calculated by the model: The sum of squared residuals of all samples, i.e., the loss function J, is: ; The loss function J can be written in matrix form: For parameter vectors Find the gradient, set the gradient to 0 (the minimum point), and simplify to obtain the normal equation: ; To design the transpose of a matrix (4 rows × 300 columns), Invertible square matrix (the three input factors are linearly independent and there is no multicollinearity).

[0072] The following calculations can be performed automatically using calibration tools such as CANape / INCA, Python, and MATLAB: 1) Import 300 sets of normalized K i ,μ i ,S i Corresponding measured brake pressure relief rate ; 2) Automatically generate design matrix Output truth vector R real ; 3) Calculation Invertible matrix vector; 4) Find the inverse of a 4th order square matrix. ; 5) Matrix multiplication outputs four optimal baseline values ​​at once: α=0.4, β=0.2, γ=0.3, C=0.08 (α=0.4 is the largest: the curvature of the curve has the strongest impact on the pressure relief rate, which is consistent with the lateral load transfer mechanism of Pacejka tires; β=0.2 is the second largest: the slippage of the drive wheel directly determines the tire grip range, so its impact is secondary; γ=0.3 is relatively small: the road adhesion coefficient only corrects the basic grip redundancy, so its impact is the weakest; C=0.08, C is the basic offset compensation constant, used to offset the offset of fixed systems such as IBCU hydraulics and vehicle weight, and does not change with the working conditions.)

[0073] Mechanical braking pressure refers to the oil pressure in the hydraulic brake wheel cylinder. The pressure determines the clamping force of the brake pads, i.e., the magnitude of the mechanical braking force.

[0074] Controlling the mechanical braking pressure to release pressure according to the braking pressure relief rate allows for a more uniform and gradual reduction of wheel cylinder pressure until the pressure is released to the target pressure or completely released.

[0075] Mechanical braking can drive the brake lines through IBCU10, pushing the brake pads to clamp the brake disc, and relying on mechanical friction to consume the vehicle's kinetic energy to achieve deceleration.

[0076] Motor braking involves the wheels dragging the motor, converting the kinetic energy of the electric vehicle into electrical energy to recharge the power battery, while simultaneously generating reverse torque to achieve deceleration.

[0077] The control method provided in this application embodiment can control an electric vehicle to enter a track mode when it enters a curve. In track mode, the curve curvature, road surface adhesion coefficient, and drive wheel slip ratio can be acquired in real time. After normalizing the curve curvature, road surface adhesion coefficient, and drive wheel slip ratio, they are substituted into a nonlinear mapping model to calculate the brake pressure relief rate. In this way, a three-factor normalized mapping of curve curvature, road surface adhesion coefficient, and drive wheel slip ratio can be established, which can more accurately match the tire adhesion limit. On the other hand, after entering the curve, the mechanical braking pressure is controlled to release according to the brake pressure relief rate, while the electric motor braking is controlled. In this way, the pressure relief strategy of the mechanical braking pressure can be more accurately matched with the curve limit state, improving controllability. Mechanical braking and electric braking recovery are realized in the curve, thereby achieving coupled and gradual release, making the braking force of the electric vehicle's wheels change more smoothly and reducing the impact in the curve.

[0078] In some embodiments, the control method includes: S4. Determine that the slip ratio difference between the inner and outer wheels is greater than the calibration threshold, and apply asymmetric braking pressure compensation to the inner and outer wheels.

[0079] The inner wheel is the wheel that faces the center of the curve when an electric vehicle is turning.

[0080] The outer wheel is the wheel on the side furthest from the center of the curve during a turn in an electric vehicle.

[0081] Wheel slip ratio is a key indicator for assessing whether wheels are close to locking up. Slip ratio is the difference between vehicle speed and wheel speed, expressed as a percentage of vehicle speed. Wheel speed is the theoretical linear velocity of a wheel in pure rolling motion. A higher slip ratio indicates a greater component of wheel slippage, resulting in poorer grip and steering ability.

[0082] The slip ratio difference between the inner and outer wheels refers to the difference between the slip ratio of the inner wheel and the slip ratio of the outer wheel.

[0083] In related technologies, yaw adjustment relies on ESP for passive correction, only intervening after instability, and does not have the ability to pre-control slip ratio deviation.

[0084] In this embodiment, during cornering, the inner wheel has a low load, and under the same braking pressure, the slip ratio of the inner wheel is usually significantly higher than that of the outer wheel. If the difference in slip ratio between the inner and outer wheels is greater than the calibration threshold, it indicates that the slip state of the inner and outer wheels is too different, and it is determined that the electric vehicle has a tendency to understeer or fishtail. By applying asymmetrical braking pressure compensation to the inner and outer wheels, that is, applying different amounts of compensation hydraulic pressure to the inner and outer wheels, the slip ratio of the inner wheel and the slip ratio of the outer wheel are brought back to a reasonable range, thus achieving pre-control before yaw attitude instability, which is different from the post-instability correction of ESP.

[0085] In some embodiments, the amount of compensation for the asymmetric braking pressure is proportional to the difference in slip ratio.

[0086] In this embodiment, the compensation amount is proportional to the difference in slip ratio, and the pressure adjustment range can be dynamically matched according to the magnitude of the slip deviation between the two wheels: the larger the difference in slip ratio, the more serious the imbalance between the inner and outer wheel grip and slip state, and the corresponding compensation amount will increase accordingly; conversely, it will be slightly adjusted. In this way, excessive slip of the inner wheel is suppressed, wheel lock-up is avoided, and the vehicle's cornering ability and driving posture are effectively maintained.

[0087] In some embodiments, the compensation amount is 0.5 to 1.2 times the slip ratio difference.

[0088] For example, the compensation amount is any one of 0.5 times, 1.0 times, and 1.2 times the slip ratio difference, or a value between any two of them.

[0089] In this embodiment, the compensation amount is set to 0.5 to 1.2 times the slip ratio difference. The adjustment range of braking pressure can be limited according to the degree of slip deviation between the inner and outer wheels. When the slip ratio difference is small, a smaller compensation amount is used to achieve subtle and smooth pressure correction, avoiding vehicle vibration and driving jerking caused by large fluctuations in braking force. When the slip ratio difference is large, the upper limit compensation amount can offset the slip difference between the two wheels to a certain extent, stabilizing the vehicle posture and steering performance in corners. This balances adjustment sensitivity, control safety, and driving comfort.

[0090] In some embodiments, the calibration threshold is 2%-5%.

[0091] For example, the calibration threshold can be any value of 2%, 2.5%, 3%, 3.5%, 4%, 4.5%, and 5%, or a value between any two of them.

[0092] In this embodiment, the calibration threshold is 2%-5%, which can trigger asymmetric braking pressure compensation in a timely manner. This can avoid the decrease in driving smoothness caused by repeated fine-tuning of braking pressure due to an excessively low calibration threshold, and also prevent the problem of excessive accumulation of slip deviation, severe slippage or even lock-up of the inner wheel due to an excessively high calibration threshold. In this way, the wheel slip imbalance state under cornering braking can be accurately identified, and the braking force distribution can be corrected in advance to ensure the stability and steering ability of cornering driving.

[0093] In some embodiments, the curve curvature, the road surface adhesion coefficient, and the drive wheel slip ratio are normalized based on the Pacejka tire magic formula to construct the nonlinear mapping model.

[0094] Pacejka's Magic Formula for Tires is a semi-empirical tire mechanics model that fits the force and torque characteristics of a tire under different slip, lateral deviation, and load conditions using a unified trigonometric function form. It is a core tire model for vehicle dynamics, chassis control, and whole-vehicle simulation.

[0095] For example, the longitudinal / lateral adhesion mechanism is derived based on Pacejka's tire magic formula; and multiple regression fitting is performed using multiple sets of track calibration data of real vehicles, such as 300 sets or more, to obtain the nonlinear mapping model of this application; wherein, the calibration data includes: a curve curvature of 0.01-0.05, a road surface adhesion coefficient of 0.85-1.0, and a drive wheel slip rate of 5%-12%, and the curve curvature, road surface adhesion coefficient and drive wheel slip rate are normalized.

[0096] In this embodiment, the longitudinal / lateral adhesion mechanism is derived based on the classic Pacejka tire magic formula; then, multiple regression fitting is completed through multiple sets of track calibration data of real vehicles, such as 300 sets or more, to obtain the nonlinear mapping model of this application; wherein, the larger the normalized curve curvature, the lower the normalized road surface adhesion coefficient, the higher the normalized drive wheel slip ratio, the lower the brake pressure relief rate, and the more it conforms to the tire's extreme grip characteristics.

[0097] In some embodiments, the electric vehicle is determined to meet a first condition, and then the electric vehicle is controlled to enter the track mode. The first condition includes: vehicle speed of 60km / h-180km / h, brake master cylinder pressure ≥90bar and pressure rise rate ≥20bar / 10ms, steering angle ≥3° and steering angular velocity ≥5° / 10ms, and road surface adhesion coefficient of 0.85-1.0.

[0098] Vehicle speed is the speed at which an electric vehicle travels, and can be obtained, for example, by means of a sensor or by other known methods.

[0099] Brake master cylinder pressure can be obtained in a known manner; for example, please refer to [link to relevant documentation]. Figure 3 The electric vehicle includes a brake pressure sensor 60, which can be used to collect the brake master cylinder pressure.

[0100] The rate of increase of the brake master cylinder pressure can be obtained in a known manner; for example, it can be calculated using a differential algorithm based on the brake master cylinder pressure.

[0101] The steering angle can be obtained in a known manner, for example, by means of a steering angle sensor 50.

[0102] The steering angular velocity can be obtained in a known manner; for example, it can be calculated from the differential signal of the steering angle sensor 50.

[0103] Vehicle speed range: 60km / h~180km / h: covering medium and high speed driving range, including common speeds on urban expressways and highways.

[0104] Brake master cylinder pressure ≥90 bar: When the hydraulic pressure of the brake master cylinder reaches 90 bar or above, it indicates that the braking force output is under high load during high-intensity emergency braking.

[0105] Pressure rise rate ≥20 bar / 10 ms: The braking pressure rises by at least 20 bar every 10 milliseconds, indicating that the braking action is rapid, the pedal is pressed with great force and speed.

[0106] Steering angle ≥ 3°: The steering angle of the wheels is not less than 3°, indicating that the electric vehicle is in a steering or cornering state.

[0107] Steering angular velocity ≥5° / 10ms: The steering angle changes by no less than 5° every 10 milliseconds, indicating that the steering operation is rapid, the steering action is large and fast.

[0108] A road surface adhesion coefficient of 0.85-1.0 indicates a high-adhesion road surface (e.g., dry asphalt or cement road surface), which has strong road grip and allows the tires to achieve near-limit adhesion performance.

[0109] The unit "km / h" means kilometers per hour.

[0110] The unit "bar" is a pressure unit.

[0111] The unit "° / 10ms" represents degrees per ten milliseconds.

[0112] In this embodiment, the electric vehicle is determined to meet the first condition, namely, the vehicle speed is 60km / h-180km / h, the brake master cylinder pressure is ≥90bar and the pressure rise rate is ≥20bar / 10ms, the steering angle is ≥3° and the steering angular velocity is ≥5° / 10ms, and the road surface adhesion coefficient is 0.85-1.0. This indicates that the electric vehicle is in extreme cornering conditions, the track mode is activated, the braking system of the electric vehicle is disengaged from the driver's pedal and enters active control. While making full use of the road surface adhesion to ensure braking performance, the vehicle body stability and driving safety during high-speed emergency steering and braking are greatly improved.

[0113] In some embodiments, controlling the mechanical braking pressure to release pressure according to the braking pressure relief rate and controlling the motor braking include: The mechanical braking pressure is controlled to release pressure according to the braking pressure release rate, and the motor braking torque is controlled to decrease in the reverse direction, so that the total deceleration of the electric vehicle is within the first range.

[0114] IBCU10 can control the mechanical braking pressure to release pressure according to the braking pressure release rate, that is, reduce the hydraulic pressure of mechanical braking according to the braking pressure release rate, and gradually weaken the friction braking force; at the same time, MCU20 controls the torque of motor braking to decrease synchronously in the opposite direction.

[0115] Total deceleration is the combined deceleration effect produced by the combined action of mechanical friction braking and motor regenerative braking.

[0116] In related technologies, the mechanical braking and electric braking recovery decompression processes are independent of each other, making it difficult to ensure a constant overall vehicle deceleration. This can easily lead to impacts and vibrations, compromising stability during cornering. Moreover, the overall deceleration fluctuates significantly when switching between brake release and energy recovery. It is generally believed that the dynamic compensation of mechanical braking and electric braking is difficult to synchronize with high precision.

[0117] Please see Figure 2 , Figure 2This is a timing diagram showing the coupling and release of mechanical braking pressure and motor braking torque in a curve in some embodiments of this application. Figure 2 The left vertical axis represents torque, the right vertical axis represents gravitational acceleration, the horizontal axis represents time, 200 represents mechanical braking pressure, 100 represents motor braking torque, and 300 represents total deceleration. Among them, the mechanical braking pressure 200 is depressurized according to the braking pressure relief rate, and the motor braking torque 100 decays in the opposite direction, so that the total deceleration 300 remains constant.

[0118] In this embodiment, mechanical braking and electric braking recovery are achieved in the curve, and the mechanical braking force and electric braking force are dynamically complementary, so that the total deceleration is maintained within the first range, achieving a roughly constant coupling and release, reducing impact and fluctuation, stabilizing the wheel slip state and vehicle posture, and improving driving comfort while ensuring driving safety.

[0119] In some embodiments, the first range is 0.6 to 0.8 times the gravitational acceleration.

[0120] In this embodiment, during the coupling release process, the mechanical braking pressure relief and the motor braking torque decay are executed synchronously and in reverse. The total deceleration of the electric vehicle is 0.6 to 0.8 times the gravitational acceleration, with basically no impact or fluctuation.

[0121] In some embodiments, the control method includes: S5. Based on the continuous decrease in the curvature of the curve and the steering return signal, determine the exit point of the curve.

[0122] There are no restrictions on how the steering return signal is acquired. For example, the steering return signal generated by the steering wheel angle can be acquired in real time through the IBCU10.

[0123] When the curvature of the curve is detected to be decreasing continuously and a steering return signal is present, it is determined that the electric vehicle has entered the exit phase of the curve and the exit point is locked.

[0124] In this embodiment, based on the continuous decrease in curvature of the curve and the advance prediction of the exit point of the steering return signal, the defect of misjudging the exit point of a single signal can be avoided, and the timing of the vehicle's exit from the curve can be captured more accurately. This makes it easier to gradually cancel the asymmetric braking compensation of the inner and outer wheels in advance, and to simultaneously and smoothly reduce the mechanical braking pressure and the motor feedback torque. This allows the braking force to transition smoothly with the exit condition, preventing abnormal braking force distribution after exiting the curve from causing the vehicle body to pull or veer, and optimizing the smoothness of acceleration and the overall driving stability of the vehicle.

[0125] In some embodiments, after determining the bend point, the control method includes: S6. Control the mechanical braking pressure to drop to 0 bar within the first duration, and send a torque feedforward command to the MCU in advance for the second duration.

[0126] For example, after determining that the vehicle has reached the exit point of the curve, the IBCU controls the hydraulic system to smoothly release the mechanical braking pressure to 0 bar within the first time period according to the calibrated pressure release rate, and gradually cancel the friction braking force; compared with the moment when the mechanical braking pressure reaches zero, a torque feedforward command is sent to the MCU in advance for a second time period to achieve zero-delay power output.

[0127] In related technologies, the exit-of-turn power is triggered after the turn, resulting in a delay of hundreds of milliseconds.

[0128] In this embodiment, the mechanical braking pressure is controlled to drop to 0 bar within the first duration, and a torque feedforward command is sent to the MCU20 in advance within the second duration. In this way, when exiting a curve, the exit point is predicted in advance, and a torque feedforward command is sent to the power system in advance, so as to achieve zero-delay connection between brake release and power output, thereby improving the acceleration capability when exiting a curve.

[0129] The first duration can be set according to requirements. For example, the first duration can be between 50ms and 100ms. For instance, the first duration can be any value among 50ms, 60ms, 80ms, and 100ms, or any value between two of them.

[0130] The second duration can be set according to requirements. For example, the second duration can be between 50ms and 100ms. For instance, the second duration can be any value among 50ms, 60ms, 80ms, and 100ms, or any value between two of them.

[0131] The unit "ms" stands for millisecond.

[0132] In some embodiments, the control method includes: S7. Determine that the electric vehicle meets one of the second conditions and exit the track mode. The second conditions include: the curvature of the curve is not greater than 0.005, the pedal travel is not greater than 5mm, and the yaw rate deviation is not greater than 0.5rad / s.

[0133] A curve curvature ≤ 0.005 indicates that the curve curvature is extremely small and the curve tends to be a straight line.

[0134] Pedal travel refers to the amount of displacement of the pedal when it is pressed down.

[0135] A pedal travel of ≤5mm indicates that the driver has almost released the pedal, has no intention of continuing to brake, and the braking demand has basically disappeared.

[0136] Yaw rate refers to the angular velocity at which an electric vehicle deflects around its vertical axis, representing the speed at which the vehicle body actually yaws during steering.

[0137] Yaw rate deviation is the difference between the measured yaw rate and the theoretical target yaw rate calculated based on the steering angle and vehicle speed. A larger yaw rate deviation indicates that the vehicle is either fishtailing or understeer. The smaller the yaw rate deviation, the closer the actual posture of the vehicle is to the ideal driving trajectory, and the lower the risk of lateral instability.

[0138] In some embodiments, a yaw rate deviation greater than 0.5 rad / s indicates that ESP does not intervene deeply and the electric vehicle enters track mode.

[0139] For example, please refer to Figure 3 Electric vehicles include an IMU (Inertial Measurement Unit) sensor, and the IMU sensor 70 can be used to collect yaw rate.

[0140] A yaw rate deviation of ≤0.5rad / s indicates that the vehicle body has no excessive yaw and its attitude is stable, the risk of lateral instability can be eliminated, and ESP intervenes deeply.

[0141] The unit "mm" stands for millimeter.

[0142] The unit "rad / s" is radians per second.

[0143] In this embodiment, corner exit is characterized by three dimensions: corner curvature, pedal travel, and yaw rate deviation. If any one of the following conditions is met, the corner exit is determined as complete, and the corner curvature is no greater than 0.005, the pedal travel is no greater than 5mm, and the yaw rate deviation is no greater than 0.5rad / s.

[0144] The control method of this application is further described below with an embodiment. The test vehicle is a mass-produced high-performance pure electric four-wheel drive model (the vehicle has a mass of 1980 kg, a maximum power of 350 kW, and is equipped with IBCU10 brake-by-wire and dual MCU20, which have the ability to independently control the braking system without the pedal).

[0145] Control methods include: S1: Determine that the electric vehicle meets the first condition, and control the electric vehicle to enter the track mode. The first condition includes: vehicle speed of 60km / h-180km / h, brake master cylinder pressure ≥90bar and pressure rise rate ≥20bar / 10ms, steering angle ≥3° and steering angular velocity ≥5° / 10ms, and road surface adhesion coefficient of 0.85-1.0.

[0146] The vehicle speed is 92 km / h; the brake master cylinder pressure is 115 bar, the pressure rise rate is 25 bar / 10 ms; the steering angular velocity is 6° / 10 ms; and the road surface adhesion coefficient is 0.95.

[0147] S2: Obtain the curvature of the curve, the road surface adhesion coefficient, and the slip ratio of the drive wheel. Calculate the brake depressurization rate based on a nonlinear mapping model.

[0148] The obtained curve curvature was 0.038, and the normalized curve curvature was 0.76; the obtained drive wheel slip ratio was 8%, and the normalized drive wheel slip ratio was 0.43; the normalized road adhesion coefficient was 0.67. After normalizing K, μ, and S, the values ​​were substituted into the nonlinear mapping model to calculate the target pressure relief rate. The calibration coefficients are set to α=0.4, β=0.2, γ=0.3, and C=0.08. The calculated values ​​are... =0.76×0.4+0.67×0.2+0.43×0.3+0.08=0.304+0.134+0.129+0.08=0.647bar / 10ms.

[0149] S3: Control the mechanical braking pressure to release pressure according to the braking pressure release rate, and control the motor braking torque to decrease in the reverse direction, so that the total deceleration of the electric vehicle is within the first range, wherein the braking pressure release rate is 0.647 bar / 10 ms, and the total deceleration is maintained at 0.6 times the gravitational acceleration.

[0150] S4: If the slip ratio difference between the inner and outer wheels is greater than the calibration threshold, apply asymmetric braking pressure compensation to the inner and outer wheels.

[0151] Among them, the slip ratio difference ΔS is 4%, the calibration threshold is 3%; the compensation amount is 0.8 times the slip ratio difference, then the compensation amount = 4% × 0.8 = 3.2 bar, which actively suppresses the push-head trend.

[0152] S5: Determine the exit point of the curve based on the continuous decrease in the curvature of the curve and the steering return signal.

[0153] Among them, the curvature of the curve continuously decreased to 0.01 (0.2 after normalization) and the steering returned to straight, thus identifying the curve point in advance.

[0154] S6: Control the mechanical braking pressure to drop to 0 bar within the first duration, and send a torque feedforward command to MCU20 in advance for the second duration.

[0155] The first duration is 50ms, and the second duration is 100ms.

[0156] The mechanical braking pressure drops to 0 bar within 100ms, and a torque feedforward command is sent 100ms in advance to achieve zero-delay acceleration out of the curve.

[0157] S7: Determine that the curvature of the curve is no greater than 0.005, then exit the track mode.

[0158] The curve curvature returns to 0.003 (straight-line level), the electric vehicle completely exits the curve, and exits track mode.

[0159] For example, a comparative experiment was conducted between the control method described above in this application and the traditional ESP mode, wherein, The same mass-produced high-performance pure electric four-wheel drive vehicle was used as the test vehicle. The test site was a closed professional asphalt track with a dry surface and a road adhesion coefficient of 0.85 to 0.95. The tire specifications (Michelin PS4S), tire pressure, and ambient temperature (25℃) were uniform. The entry point, braking point, and driving line were fixed. Equipped with a Kistler high-precision wheel speed sensor 40 (sampling accuracy ±0.1km / h), Vector 1000Hz high-speed CAN acquisition system, Dspace real-time simulation system, and IMU inertial navigation synchronous recording; Each working condition was tested 10 times, and the arithmetic mean was taken to reduce noise. The only variable is the braking control strategy: the traditional ESP mode and the control method of this application.

[0160] Please refer to Table 1 below for the test results. Table 1 shows the comparison results: Table 1

[0161] As shown in Table 1, the control method of this application improves the maximum speed at the entry point of a corner, the fluctuation of the yaw rate during a corner, the speed at the end of the exit of a corner, and the lap time on a multi-corner track.

[0162] This application provides an electric vehicle, which includes a memory and a processor. The memory stores computer programs or instructions, and when the computer programs or instructions are executed by the processor, they implement the control method in any embodiment of this application.

[0163] Memory includes, but is not limited to, Random Access Memory (RAM), Read Only Memory (ROM), and Programmable Read Only Memory (ROM). Erasable Programmable Read-Only Memory (PROM) Electrically Erasable Programmable Read-Only Memory (EPROM) Only memory (EEPROM), etc.

[0164] A processor can be an integrated circuit chip with signal processing capabilities. The processor can be a general-purpose processor, including a central processing unit (CPU) and a network processor (NP), etc. The general-purpose processor can be a microprocessor or any conventional processor, and can implement or execute the various control methods, steps, and logic block diagrams disclosed in the embodiments of this application.

[0165] This application provides a storage medium, namely a computer-readable storage medium, on which a computer program or instructions are stored. When the computer program or instructions are executed by a processor, they implement the control method in any of the embodiments of this application. The computer-readable storage medium can be transient or non-transient.

[0166] This application provides a computer program product, which includes a computer program or instructions. When the computer program or instructions are executed by a processor, they implement the control method in any of the embodiments of this application. The computer program product can be implemented specifically through hardware, software, or a combination thereof. In one optional embodiment, the computer program product is specifically embodied as a computer storage medium; in another optional embodiment, the computer program product is specifically embodied as a software product, such as a software development kit (SDK), etc.

[0167] It should be noted that, in the embodiments of this application, if the above-described control method is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiments of this application, or the part that contributes to related technologies, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause an electric vehicle to execute all or part of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, a portable hard drive, a read-only memory (ROM), a magnetic disk, or an optical disk. Thus, the embodiments of this application are not limited to any specific hardware and software combination.

[0168] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit it. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features. These modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application. In particular, as long as there is no structural conflict, the various technical features mentioned in each embodiment can be combined in any way.

Claims

1. A control method for cornering braking of an electric vehicle, characterized in that, include: Control the electric vehicle to enter track mode, wherein the track mode is the active braking control mode for the electric vehicle to enter a curve. By obtaining the curve curvature, road surface adhesion coefficient, and drive wheel slip ratio, the brake depressurization rate is calculated based on a nonlinear mapping model. The nonlinear mapping model is as follows: ,in, Let represent the brake depressurization rate, K represent the normalized curvature, μ represent the normalized road adhesion coefficient, S represent the normalized drive wheel slip ratio, α represent the weighting coefficient of the normalized curvature, β represent the weighting coefficient of the normalized road adhesion coefficient, γ represent the weighting coefficient of the normalized drive wheel slip ratio, and C represent the base offset compensation constant, where α∈[0.3,0.45], β∈[0.15,0.25], γ∈[0.25,0.35], and C∈[0.05,0.1]. Control the mechanical braking pressure to release pressure according to the braking pressure release rate, and control the motor braking.

2. The control method according to claim 1, characterized in that, The control method includes: If the slip ratio difference between the inner and outer wheels is determined to be greater than the calibration threshold, asymmetric braking pressure compensation is applied to the inner and outer wheels.

3. The control method according to claim 2, characterized in that, The amount of compensation for the asymmetric braking pressure is proportional to the difference in slip ratio.

4. The control method according to claim 3, characterized in that, The compensation amount is 0.5 to 1.2 times the difference in slip ratio.

5. The control method according to claim 2, characterized in that, The calibration threshold is 2%-5%.

6. The control method according to claim 1, characterized in that, Based on Pacejka's tire magic formula, the curve curvature, the road surface adhesion coefficient, and the drive wheel slip ratio are normalized to construct the nonlinear mapping model.

7. The control method according to claim 1, characterized in that, If the electric vehicle meets the first condition, control the electric vehicle to enter the track mode. The first condition includes: vehicle speed of 60km / h-180km / h, brake master cylinder pressure ≥90bar and pressure rise rate ≥20bar / 10ms, steering angle ≥3° and steering angular velocity ≥5° / 10ms, and road surface adhesion coefficient of 0.85-1.

0.

8. The control method according to claim 1, characterized in that, Controlling the mechanical braking pressure to release pressure according to the stated braking pressure relief rate, and controlling the motor braking, includes: The mechanical braking pressure is controlled to release pressure according to the braking pressure release rate, and the motor braking torque is controlled to decrease in the reverse direction, so that the total deceleration of the electric vehicle is within the first range.

9. The control method according to claim 8, characterized in that, The first range is 0.6 to 0.8 times the gravitational acceleration.

10. The control method according to claim 1, characterized in that, The control method includes: The exit point is determined based on the continuous decrease in the curvature of the curve and the steering return signal.

11. The control method according to claim 10, characterized in that, After determining the exit point of the bend, the control method includes: The mechanical braking pressure is controlled to drop to 0 bar within the first duration, and a torque feedforward command is sent to the MCU in advance within the second duration.

12. The control method according to claim 1, characterized in that, The control method includes: If the electric vehicle meets one of the second conditions, it exits the track mode. The second conditions include: the curvature of the curve is not greater than 0.005, the pedal travel is not greater than 5mm, and the yaw rate deviation is not greater than 0.5rad / s.

13. An electric vehicle, characterized in that, The electric vehicle includes a memory and a processor. The memory stores computer programs or instructions, which, when executed by the processor, implement the control method according to any one of claims 1 to 12.

14. A storage medium, characterized in that, The storage medium stores a computer program or instructions, which, when executed by a processor, implement the control method according to any one of claims 1 to 12.

15. A computer program product, characterized in that, The computer program product includes a computer program or instructions, which, when executed by a processor, implement the control method according to any one of claims 1 to 12.

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