Control method and system for non-inductive braking of electric motorcycle

Abstract

The invention discloses a control method and system for non-inductive braking of an electric motorcycle, and the method achieves the precise estimation of a vehicle state through multi-sensor data fusion and adaptive Kalman filtering, and employs a dual-path torque filtering architecture and a cooperative control strategy. The problems of pause feeling, low energy recovery efficiency and insufficient steering stability of a traditional braking system are solved. Through multi-stage filtering and closed-loop control, the braking smoothness, the energy recovery efficiency and the steering stability are remarkably improved, the requirements of complex working conditions are met, and the non-pause and high-energy-efficiency braking experience is provided for the electric motorcycle.

Description

Technical Field

[0001] The present invention belongs to the technical field of electric motorcycle control, and in particular relates to a control method and system for a sensorless braking of an electric motorcycle. Background Art

[0002] The core goal of sensorless braking in electric motorcycles is to eliminate the jerkiness associated with traditional braking through smooth braking force distribution and energy recovery, while maximizing energy recovery efficiency. Kalman filtering and torque filtering are two key algorithms that optimize state estimation and torque control, respectively, to achieve sensorless braking.

[0003] Existing electric motorcycles have the following technical defects in braking and steering control:

[0004] 1) Regenerative braking torque abrupt changes: Traditional regenerative braking systems directly recover energy through the motor's back electromotive force, but the torque response is abrupt, resulting in a noticeable jerking sensation when the vehicle brakes, affecting riding comfort.

[0005] 2) Insufficient steering stability: During high-speed steering, the dynamic change in body roll angle can easily cause vehicle instability. Traditional electronic stability systems (ESC) have a delayed response and are unable to compensate for steering torque in real time.

[0006] 3) Low energy recovery efficiency: Braking energy recovery and steering stability control lack coordination, and a single control strategy cannot achieve both maximum energy recovery and vehicle dynamic balance.

[0007] 4) Sensor noise interference: Sensor data such as wheel speed and acceleration are affected by road bumps. Traditional filtering algorithms, such as sliding average filtering, cannot dynamically adapt to complex working conditions.

[0008] In the existing technology, although some solutions use Kalman filtering for state estimation, they are not deeply coupled with dual-path torque filtering and lack a dynamic parameter adjustment mechanism. Summary of the Invention

[0009] The present invention aims to provide a control method and system for sensorless braking of an electric motorcycle, which systematically solves the above-mentioned problems through a multi-stage filtering architecture and closed-loop feedback.

[0010] In order to achieve the purpose of the present invention, on the one hand, the present invention provides a control system for a sensorless braking of an electric motorcycle, comprising the following modules:

[0011] The sensor module includes a wheel speed sensor, an inertial measurement unit, and a slope sensor, which is used to collect wheel speed, roll angle, and road slope data in real time;

[0012] a Kalman filter module, comprising a Kalman filter for receiving Kalman filter parameters applicable to the Kalman filter, and further for receiving feedback signals of wheel deceleration, motor current, and battery voltage to correct vehicle state estimation;

[0013] The execution control module includes an actuator, wherein the actuator includes a motor actuator and a mechanical brake actuator, and is used to coordinate the working range of each actuator using the received torque command, obtain the final torque command and send it to the motor actuator.

[0014] On the other hand, the present invention also provides a method for realizing a control system for a sensorless braking of an electric motorcycle, comprising the following steps:

[0015] Step 1: acquiring multi-source sensor data in real time through the wheel speed sensor, inertial measurement unit, and slope sensor of the electric motorcycle, which includes wheel speed, roll angle, and road slope data;

[0016] Step 2: Using the multi-source sensor data, the state equation matrix and the noise covariance matrix are estimated online by the least squares method to determine the adaptive Kalman filter parameters;

[0017] Step 3: Calculating the Kalman filter parameters through a PI controller to obtain a regenerative braking torque; using the roll angle and road slope data to obtain a steering compensation torque, and then performing a first-order filter on the regenerative braking torque and the steering compensation torque to enhance stability;

[0018] Step 4: Convert the filtered braking torque and steering compensation torque into a unified dimension, define priorities based on vehicle status and driving mode, and then obtain the total torque command through weighted synthesis;

[0019] Step 5: The total torque command is coordinated with the working range of the actuators through an arbitration mechanism. The actuators include motor actuators and mechanical brake actuators, and the final torque command is obtained and sent to the motor actuators to ensure smooth transition of the command and avoid sudden changes.

[0020] Step 6: After the motor actuator executes the final torque command, it monitors the feedback signals of the wheel deceleration, motor current, and battery voltage in real time; inputs the feedback signals into a Kalman filter including the Kalman filter parameters, corrects the vehicle state estimation, and dynamically adjusts the noise covariance matrix according to the operating state to complete closed-loop control.

[0021] Compared with the existing technology, the significant progress of the present invention lies in: (1) Feel-free braking experience: the present invention suppresses noise through Kalman filtering and smoothes the output through torque filtering, eliminating the sense of frustration during braking and improving the driving experience; (2) Improved energy recovery efficiency: the present invention optimizes the utilization of regenerative braking force, maximizes the recovery of braking energy, and extends the vehicle's cruising range; (3) Enhanced steering stability: the present invention improves the stability and handling performance during steering through rotation compensation and torque filtering; (4) Enhanced system stability: the present invention reduces the risk of loss of control due to sensor noise or torque mutation through the coordinated work of Kalman filtering and torque filtering; (5) Strong adaptability: the present invention can adapt to various complex conditions and driving needs through adaptive Kalman filtering and dynamic torque distribution strategy.

[0022] In order to more clearly illustrate the functional characteristics and structural parameters of the present invention, further description is given below with reference to the accompanying drawings and specific embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0024] Figure 1 It is a flow chart of the steps of the present invention. DETAILED DESCRIPTION

[0025] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments; based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0026] The present invention provides a control system for a sensorless braking of an electric motorcycle, which includes the following modules:

[0027] The sensor module includes a wheel speed sensor, an inertial measurement unit (IMU), and a slope sensor, which is used to collect real-time data on wheel speed, roll angle, and road slope;

[0028] a Kalman filter module, comprising a Kalman filter for receiving Kalman filter parameters applicable to the Kalman filter, and further for receiving feedback signals of wheel deceleration, motor current, and battery voltage to correct vehicle state estimation;

[0029] The execution control module includes an actuator, wherein the actuator includes a motor actuator and a mechanical brake actuator, and is used to coordinate the working range of each actuator using the received torque command, obtain the final torque command and send it to the motor actuator.

[0030] The present invention is a method for realizing the control system of the electric motorcycle without sensory braking according to claim 1, combined with Figure 1 , including the following steps:

[0031] Step 1: acquiring multi-source sensor data in real time through the wheel speed sensor, inertial measurement unit, and slope sensor of the electric motorcycle, which includes wheel speed, roll angle, and road slope data;

[0032] Step 2: Using the multi-source sensor data, the state equation matrix and the noise covariance matrix are estimated online by the least squares method to determine the adaptive Kalman filter parameters;

[0033] Step 3: The Kalman filter parameters are calculated using a PI controller (Proportional Integral Controller, which regulates the system through proportional (P) and integral (I) control) to obtain a regenerative braking torque; the roll angle and road slope data are used to obtain a steering compensation torque, and the regenerative braking torque and the steering compensation torque are then first-order filtered to enhance stability;

[0034] Step 4: Convert the filtered braking torque and steering compensation torque into a unified dimension, define priorities based on vehicle status and driving mode, and then obtain the total torque command through weighted synthesis;

[0035] Step 5: The total torque command is used to coordinate the working range of each actuator through an arbitration mechanism. The actuators include motor actuators and mechanical brake actuators, and the final torque command is obtained and sent to the motor actuators to ensure smooth transition of the command and avoid sudden changes.

[0036] Step 6: After the motor actuator executes the final torque command, it monitors the feedback signals of the wheel deceleration, motor current, and battery voltage in real time; inputs the feedback signals into the Kalman filter including the Kalman filter parameters, corrects the vehicle state estimation, such as longitudinal acceleration and roll angular velocity, and dynamically adjusts the noise covariance matrix Q according to the actual operating state to complete closed-loop control.

[0037] The step 1 comprises the following steps:

[0038] Step 1-1: The wheel speed sensor generates a wheel speed dataset by measuring wheel rotation speed and using a wheel speed-to-vehicle speed conversion algorithm. The inertial measurement unit (IMU) fuses the three-axis accelerometer and gyroscope to obtain a vehicle body roll angle dataset. The slope sensor fuses GPS elevation data with the IMU longitudinal acceleration to obtain a real-time road slope dataset, completing the collection of multi-source sensor data.

[0039] Step 1-2: Using the collected multi-source sensor data, the longitudinal acceleration and the roll acceleration of the vehicle are estimated in real time according to the dynamic model to provide state feedback for determining the adaptive Kalman filter parameters.

[0040] The kinetic model estimation method of steps 1-2 is specifically shown in the following formula:

[0041] Determining air resistance :

[0042] ;

[0043] in, is the drag coefficient, is the influence value of air resistance, is the air density, is the longitudinal speed;

[0044] Determining gyroscopic torque :

[0045] ;

[0046] in, is the wheel speed, is the steering angle, is the steering angular velocity, is the wheel moment of inertia;

[0047] Finally, the longitudinal acceleration of the vehicle is obtained and the vehicle's roll acceleration :

[0048] ;

[0049] in, is the vehicle mass, is the regenerative braking torque, To compensate for the steering torque, is the rolling resistance, is the moment of inertia.

[0050] The step 2 specifically includes the following steps:

[0051] Step 2-1: Measure the longitudinal vehicle speed using the wheel speed dataset , the vehicle body roll angle data set obtains the roll angle , the road slope data set and estimation method obtain the road slope , the longitudinal acceleration of the vehicle and the vehicle's roll acceleration , to obtain the accurate vehicle state vector :

[0052] ;

[0053] Step 2-2: Describe the vehicle state vector by discretizing the state equation As time evolves, the discretized state equation is shown as follows:

[0054] ;

[0055] in, To utilize The state of the moment and The control input at the moment is thus predicted Estimated state value at the moment; for The actual state of the moment; is the first matrix, describing the relationship between the internal states of the system; It is the control input of steering torque and braking torque. The second matrix describes the influence of the control input on the system state, which is used to convert the control input Mapping to state space; for Control input at all times, used to actively adjust the system state; is the process noise and obeys distributed; is discrete time;

[0056] Step 2.3: Determine the driving mode by the rate of change of the vehicle state vector and dynamically adjust the process noise covariance matrix according to the driving state , which is used for state prediction and estimation of the Kalman filter and determination of the adaptive Kalman filter parameters. The formula is shown below:

[0057] ;

[0058] When in emergency braking mode, increase the longitudinal speed and longitudinal acceleration The noise term reflects the increase in model uncertainty during severe braking; keeping the roll-related noise small ensures the accuracy of steering stability estimation;

[0059] When in stable driving mode, the noise covariance is fully reduced, and the maximum reduction of the present invention is , improve the signal-to-noise ratio of state estimation; especially reduce the road slope noise, optimizing the long-term stability of the slope estimate.

[0060] The first matrix As shown below:

[0061] ;

[0062] in, is the sampling time, c is the rotation damping coefficient, m is the vehicle mass, R is the turning radius, g is the acceleration of gravity, is the longitudinal speed;

[0063] The second matrix As shown in the following formula:

[0064] ;

[0065] in, is the acceleration generated by the unit braking torque, which is affected by the vehicle mass modulation; is the roll acceleration generated by the unit steering torque, which is affected by the moment of inertia modulation.

[0066] The step 3 specifically includes the following steps:

[0067] Step 3-1: The Kalman filter parameters are used to calculate the desired regenerative braking torque through the PI controller. This torque directly acts on the vehicle power system, converting the vehicle kinetic energy into electrical energy storage through motor reversal, thereby achieving brake energy recovery; the regenerative braking torque output by the PI controller The calculation formula is as follows:

[0068] ;

[0069] in, is a proportional term, according to the current vehicle speed error ( ) directly adjusts the braking torque, It is an integral term, eliminating steady-state errors and ensuring that the vehicle speed accurately tracks the target value. ; is the proportional coefficient that determines the transient response of the vehicle speed error. To eliminate the integral coefficient of steady-state error, is the control target vehicle speed of the braking energy recovery system; t is time;

[0070] Step 3-2: The regenerative braking torque output by the PI controller is The braking regenerative torque is obtained by first-order low-pass filtering and smoothing. To prevent the impact caused by torque mutation, the processing process is shown as follows:

[0071] ;

[0072] in, is the braking regenerative torque at time t, is the exponential kernel, and the historical torque is weighted averaged by the exponential kernel; is the time constant, which controls the degree of smoothness. The larger the value, the stronger the smoothing effect, but the response delay increases; ΔT is the rate of change of braking torque;

[0073] Dynamically adjust the smoothing intensity according to the braking torque change rate ΔT to achieve adaptive control, as shown in the following formula:

[0074] ;

[0075] in, Map the braking torque change rate ΔT to the (0,1) interval to achieve a smooth transition;

[0076] Step 3-3, the roll angle and road slope The steering torque compensation formula can be used to accurately calculate the steering compensation torque required to maintain stable steering of the vehicle. , the compensation formula is as follows:

[0077] ;

[0078] in, Due to the body roll angle Steering torque compensation required for the resulting roll moment; is the road slope change rate And vehicle motion state: vehicle speed , turning radius , the jointly generated additional steering torque compensation;

[0079] Step 3-4: Perform first-order filtering on the steering torque compensation to obtain filtered steering compensation torque. , the processing process is as follows:

[0080] ;

[0081] in, is the filter coefficient, is the steering torque filter time constant, is at time t.

[0082] The step 4 specifically includes the following steps:

[0083] Step 4-1, converting the filtered steering compensation torque and brake regeneration torque through an actuator efficiency model to obtain a torque request normalization;

[0084] The filtered steering compensation torque and the brake regenerative torque Convert to a unified torque dimension of N·m and consider the efficiency of the actuator:

[0085] ;

[0086] in, is the steering actuator efficiency (usually 0.8~0.95), is the braking energy recovery efficiency (usually 0.6~0.8), is the normalized filtered steering compensation torque, is the normalized braking regenerative torque;

[0087] Step 4-2: defining a priority for torque distribution according to the vehicle state and driving mode of step 2;

[0088] The priorities specifically include the following:

[0089] Priority 1: Vehicle stability control (such as anti-rollover, ESP intervention);

[0090] Priority 2: Braking energy recovery (maximizing energy recovery while ensuring safety);

[0091] Priority 3: Steering stability compensation (priority is increased at low speed or in sharp turns);

[0092] Step 4-3: The normalized braking regenerative torque and the normalized filtered steering compensation torque are weighted and synthesized to obtain the total torque command. :

[0093] ;

[0094] in, is the weighted coefficient of the braking regenerative torque, is the weighting coefficient of the steering compensation torque, The maximum output torque of the motor or engine.

[0095] The activation conditions of the mechanical brake actuator are:

[0096] when When , the mechanical brake actuator is activated;

[0097] in, is the normalized braking regenerative torque (without considering steering compensation);

[0098] When the weighted combined total torque is less than 70% of the normalized regenerative braking torque, the mechanical braking torque Supplement braking force.

[0099] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0100] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A control system for a sensorless brake of an electric motorcycle, characterized in that: Includes the following modules: The sensor module includes a wheel speed sensor, an inertial measurement unit, and a slope sensor, which is used to collect wheel speed, roll angle, and road slope data in real time; a Kalman filter module, comprising a Kalman filter for receiving Kalman filter parameters applicable to the Kalman filter, and further for receiving feedback signals of wheel deceleration, motor current, and battery voltage to correct vehicle state estimation; The execution control module includes an actuator, wherein the actuator includes a motor actuator and a mechanical brake actuator, and is used to coordinate the working range of each actuator using the received torque command, obtain the final torque command and send it to the motor actuator.

2. A method for realizing the control system of the electric motorcycle without sensory braking according to claim 1, characterized in that: The following steps are involved: Step 1: acquiring multi-source sensor data in real time through the wheel speed sensor, inertial measurement unit, and slope sensor of the electric motorcycle, which includes wheel speed, roll angle, and road slope data; Step 2: Using the multi-source sensor data, the state equation matrix and the noise covariance matrix are estimated online by the least squares method to determine the adaptive Kalman filter parameters; Step 3: Calculating the Kalman filter parameters through a PI controller to obtain a regenerative braking torque; using the roll angle and road slope data to obtain a steering compensation torque, and then performing a first-order filter on the regenerative braking torque and the steering compensation torque to enhance stability; Step 4: Convert the filtered braking torque and steering compensation torque into a unified dimension, define priorities based on vehicle status and driving mode, and then obtain the total torque command through weighted synthesis; Step 5: The total torque command is coordinated with the working range of the actuators through an arbitration mechanism. The actuators include motor actuators and mechanical brake actuators, and the final torque command is obtained and sent to the motor actuators to ensure smooth transition of the command and avoid sudden changes. Step 6: After the motor actuator executes the final torque command, it monitors the feedback signals of the wheel deceleration, motor current, and battery voltage in real time; inputs the feedback signals into a Kalman filter including the Kalman filter parameters, corrects the vehicle state estimation, and dynamically adjusts the noise covariance matrix according to the operating state to complete closed-loop control.

3. A control system method for achieving a sensorless braking of an electric motorcycle according to claim 2, characterized in that: The step 1 comprises the following steps: Step 1-1: The wheel speed sensor generates a wheel speed dataset by measuring wheel rotation speed and using a wheel speed-to-vehicle speed conversion algorithm. The inertial measurement unit (IMU) fuses the three-axis accelerometer and gyroscope to obtain a vehicle body roll angle dataset. The slope sensor fuses GPS elevation data with the IMU longitudinal acceleration to obtain a real-time road slope dataset, completing the collection of multi-source sensor data. Step 1-2: Using the collected multi-source sensor data, the longitudinal acceleration and the roll acceleration of the vehicle are estimated in real time according to the dynamic model to provide state feedback for determining the adaptive Kalman filter parameters.

4. A control system method for achieving a sensorless braking of an electric motorcycle according to claim 3, characterized in that: The kinetic model estimation method of steps 1-2 is specifically shown in the following formula: Determining air resistance : ； in, is the drag coefficient, is the influence value of air resistance, is the air density, is the longitudinal speed; Determining gyroscopic torque : ； in, is the wheel speed, is the steering angle, is the steering angular velocity, is the wheel moment of inertia; Finally, the longitudinal acceleration of the vehicle is obtained and the vehicle's roll acceleration : ； in, is the vehicle mass, is the regenerative braking torque, To compensate for the steering torque, is the rolling resistance, is the moment of inertia.

5. A control system method for achieving a sensorless braking of an electric motorcycle according to claim 4, characterized in that: The step 2 specifically includes the following steps: Step 2-1: Measure the longitudinal vehicle speed using the wheel speed dataset , the vehicle body roll angle data set obtains the roll angle , the road slope data set and estimation method obtain the road slope , the longitudinal acceleration of the vehicle and the vehicle's roll acceleration , to obtain the accurate vehicle state vector : ； Step 2-2: Describe the vehicle state vector by discretizing the state equation As time evolves, the discretized state equation is shown as follows: ； in, To utilize The state of the moment and The control input at the moment is thus predicted Estimated state value at the moment; for The actual state of the moment; is the first matrix, describing the relationship between the internal states of the system; It is the control input of steering torque and braking torque. The second matrix describes the influence of the control input on the system state, which is used to convert the control input Mapping to state space; for Control input at all times, used to actively adjust the system state; is the process noise and obeys distributed; is discrete time; Step 2.3: Determine the driving mode by the rate of change of the vehicle state vector and dynamically adjust the process noise covariance matrix according to the driving state , determine the adaptive Kalman filter parameters, the formula is shown below: ； When in emergency braking mode, increase the longitudinal speed and longitudinal acceleration The noise term reflects the increase in model uncertainty during severe braking; keeping the roll-related noise small ensures the accuracy of steering stability estimation; When in stable driving mode, the noise covariance is fully reduced and the signal-to-noise ratio of the state estimation is improved; in particular, the road slope is reduced. noise, optimizing the long-term stability of the slope estimate.

6. A control system method for achieving a sensorless braking of an electric motorcycle according to claim 5, characterized in that: The first matrix As shown below: ； in, is the sampling time, c is the rotation damping coefficient, m is the vehicle mass, R is the turning radius, g is the acceleration of gravity, is the longitudinal speed; The second matrix As shown in the following formula: ； in, is the acceleration generated by the unit braking torque, which is affected by the vehicle mass modulation; is the roll acceleration generated by the unit steering torque, which is affected by the moment of inertia modulation.

7. A control system method for achieving a sensorless braking of an electric motorcycle according to claim 6, characterized in that: The step 3 specifically includes the following steps: Step 3-1: The Kalman filter parameters are used to calculate the desired regenerative braking torque through the PI controller. This torque directly acts on the vehicle power system, converting the vehicle kinetic energy into electrical energy storage through motor reversal, thereby achieving brake energy recovery; the regenerative braking torque output by the PI controller The calculation formula is as follows: ； in, is a proportional term, according to the current vehicle speed error ( ) directly adjusts the braking torque, It is an integral term, eliminating steady-state errors and ensuring that the vehicle speed accurately tracks the target value. ; is the proportional coefficient that determines the transient response of the vehicle speed error. To eliminate the integral coefficient of steady-state error, is the control target vehicle speed of the braking energy recovery system; t is time; Step 3-2: The regenerative braking torque output by the PI controller is The braking regenerative torque is obtained by first-order low-pass filtering and smoothing. , the processing process is shown as follows: ； in, is the braking regenerative torque at time t, is the exponential kernel, and the historical torque is weighted averaged by the exponential kernel; is the time constant, which controls the degree of smoothness. The larger the value, the stronger the smoothing effect, but the response delay increases; ΔT is the rate of change of braking torque; Dynamically adjust the smoothing intensity according to the braking torque change rate ΔT to achieve adaptive control, as shown in the following formula: ； in, Map the braking torque change rate ΔT to the (0,1) interval to achieve a smooth transition; Step 3-3, the roll angle and road slope The steering torque compensation formula can be used to accurately calculate the steering compensation torque required to maintain stable steering of the vehicle. , the compensation formula is as follows: ； in, Due to the body roll angle Steering torque compensation required for the resulting roll moment; is the road slope change rate And vehicle motion state: vehicle speed , turning radius , the jointly generated additional steering torque compensation; Step 3-4: Perform first-order filtering on the steering torque compensation to obtain filtered steering compensation torque. , the processing process is as follows: ； in, is the filter coefficient, is the steering torque filter time constant, is at time t.

8. A control system method for achieving a sensorless braking of an electric motorcycle according to claim 7, characterized in that: The step 4 specifically includes the following steps: Step 4-1, converting the filtered steering compensation torque and brake regeneration torque through an actuator efficiency model to obtain a torque request normalization; The filtered steering compensation torque and the brake regenerative torque Convert to a uniform torque dimension and take into account the efficiency of the actuator: ； in, To turn to actuator efficiency, For the braking energy recovery efficiency, is the normalized filtered steering compensation torque, is the normalized braking regenerative torque; Step 4-2: defining a priority for torque distribution according to the vehicle state and driving mode of step 2; Step 4-3: The normalized braking regenerative torque and the normalized filtered steering compensation torque are weighted and synthesized to obtain the total torque command. : ； in, is the weighted coefficient of the braking regenerative torque, is the weighting coefficient of the steering compensation torque, The maximum output torque of the motor or engine.

9. A control system method for achieving sensorless braking of an electric motorcycle according to claim 8, characterized in that: The activation conditions of the mechanical brake actuator are: when When , the mechanical brake actuator is activated; in, is the normalized braking regenerative torque; When the weighted combined total torque is less than 70% of the normalized regenerative braking torque, the mechanical braking torque Supplement braking force.

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