Stability control method and system for electric automobile

Abstract

The invention relates to the technical field of electric vehicle control, and discloses a stability control method and system for an electric vehicle, and the method comprises the steps: collecting vehicle driving data in real time, and dynamically estimating the state parameters of the vehicle through an unscented Kalman filtering algorithm; according to the state parameters of the vehicle, combined with the kinematic model and the foresight trajectory, the expected yawing moment in a short time in the future is calculated, and the longitudinal traction requirement of the vehicle is obtained; and according to the state parameters of the vehicle and the longitudinal traction requirement of the vehicle, the yawing moment and the traction force are decoupled and distributed to the four electric driving wheel ends, and stable control over the electric vehicle is achieved. Compared with a traditional stable control system only based on closed-loop feedback, the stable control system has the advantages that the control response is faster, the yawing intervention is more accurate, and better control performance and vehicle safety are shown under the low-adhesion road surface and high-load working conditions.

Description

Technical Field

[0001] The present invention relates to the technical field of electric vehicle control, and particularly to a stability control method and system for an electric vehicle. Background Art

[0002] With the development of new energy vehicle technology, four-wheel independent drive structures are becoming increasingly popular in electric vehicles, enabling independent control of the drive torque of each wheel and providing the technical basis for implementing advanced vehicle dynamic control. However, most current vehicles still adopt traditional stability control strategies, relying on mechanical braking systems or only intervening passively after instability occurs, lacking a forward-looking control logic based on road conditions and state perception. This approach has a lagging response in high-risk driving scenarios and limited control effects.

[0003] On the other hand, the electric drive system is limited by the heat capacity and cooling capacity of the motor. Prolonged loading is likely to cause the temperature rise to exceed the threshold, thereby triggering a power protection mechanism and restricting the drive ability. In the current control system, the thermal state of the motor is usually ignored. Especially in the stability control stage, the motors of each wheel may be forced to distribute torque under high load conditions, resulting in overheating and derating of some motors, ultimately affecting the stability control effect of the vehicle and even posing a safety risk.

[0004] The present invention fundamentally improves the electric vehicle's ability to perceive the risk of yaw instability and the control response efficiency, integrating three dimensions of yaw moment prediction, adhesion coefficient estimation, and motor thermal state constraint optimization, and realizing a stability control method that truly combines forward-looking judgment and multi-objective coordination, meeting the higher requirements for safety and dynamic control capabilities of future intelligent electric drive platforms. Summary of the Invention

[0005] In view of the above existing problems, the present invention is proposed.

[0006] Therefore, the technical problems solved by the present invention are: the existing electric vehicle control technology has problems of yaw instability and neglecting the influence of the motor thermal state on torque distribution.

[0007] To solve the above technical problems, the present invention provides the following technical solution: a stability control method for an electric vehicle, including: collecting vehicle driving data in real time, and dynamically estimating the state parameters of the vehicle by using the unscented Kalman filter algorithm; according to the state parameters of the vehicle, combining the kinematic model and the forward-looking trajectory, calculating the expected yaw moment in the short term in the future, and obtaining the vehicle longitudinal traction demand; according to the state parameters of the vehicle and the vehicle longitudinal traction demand, decoupling and distributing the yaw moment and the traction force to the four electric drive wheel ends to achieve the stable control of the electric vehicle.

[0008] As a preferred solution of a stability control method for an electric vehicle according to the present invention, wherein: the vehicle driving data includes vehicle speed, steering wheel angle, yaw rate, lateral acceleration, and wheel speed.

[0009] As a preferred solution of a stability control method for an electric vehicle according to the present invention, wherein: the estimated state parameters of the vehicle include constructing an extended state vector based on the vehicle driving data and calculating the instantaneous slip ratio of each wheel; Using a filter to generate a set of points, and dynamically adjusting the process noise covariance matrix according to the current maximum slip ratio; Introducing a saturation value jointly determined by the adhesion coefficient and the vertical load, using the hyperbolic tangent function to envelope the cornering stiffness model, constructing a lateral tire force saturation model, embedding the lateral tire force saturation model into the state propagation function, and performing nonlinear prediction of points; According to the predicted values of points, calculating the weighted mean and covariance, obtaining the prior state estimation result at the current moment, and calculating the innovation term. Completing the posterior estimation update based on the unscented Kalman gain to obtain the state parameters of the vehicle. The state parameters of the vehicle include vehicle speed, sideslip angle, yaw rate, and lateral adhesion coefficient.

[0010] As a preferred solution of a stability control method for an electric vehicle according to the present invention, wherein: the forward-looking trajectory is a sequence of target path points within a certain time window forward with the current moment of the vehicle as a reference, including multiple spatial position points and the curvature information corresponding to the spatial position points.

[0011] As a preferred solution of a stability control method for an electric vehicle according to the present invention, wherein: calculating the desired yaw moment within a short future time includes setting a fixed forward-looking time window , calculating the forward-looking distance within according to the vehicle speed, performing cubic spline fitting on the forward-looking trajectory within the forward-looking distance, calculating the average curvature, and obtaining the desired yaw rate; Calculating the yaw rate error and the sideslip angle suppression error, using the state parameters of the vehicle to linearly approximate the bicycle model in real time, generating the state matrix and the input matrix, and constructing an adaptive linearized vehicle model; Setting the prediction step , additionally introducing the sideslip angle as the state estimation output, setting the sideslip angle error as the target quantity, constructing the objective function and the constraint conditions, and the objective function is expressed as: ; wherein, represents the controller objective function value; represents the prediction step; Indicate moment; Indicate the weight of the yaw rate error; Indicate the moment of the yaw rate error; Indicate the weight of the sideslip angle error; Indicate the moment of the sideslip angle error; Indicate the weight of the torque change rate; Indicate the moment of the torque change rate; Indicate the control input, the moment of the yaw moment; Compile the adaptive linearized vehicle model, the objective function and the constraint conditions into a QP problem together, use a real-time QP solver to obtain the optimal yaw moment sequence within a single cycle, and take the solution at the first moment as the desired yaw moment within.

[0012] As a preferred scheme of a stability control method for an electric vehicle according to the present invention, wherein: the longitudinal traction demand of the vehicle is the target traction force required by the vehicle according to the driver input, and is calculated and obtained by the vehicle control unit VCU.

[0013] As a preferred scheme of a stability control method for an electric vehicle according to the present invention, wherein: the decoupling and distribution of the yaw moment and the traction force to the four electric drive wheel ends includes establishing an equality constraint of the four-wheel torque on the longitudinal traction force and the yaw moment of the whole vehicle according to the state parameters and physical relationships of the vehicle, the four-wheel resultant force satisfies the longitudinal traction demand, and the left and right distribution of the four wheels satisfies the desired yaw moment; Set dynamic constraint conditions for each wheel electric drive motor, including torque upper limit constraint, adhesion limit constraint, power limit constraint and torque change rate limit. The adhesion limit constraint estimates the maximum traction force through the state parameters of the vehicle and the current vertical load of each wheel, and is expressed as: ; Wherein, Indicates the motor torque output of the th wheel; Indicates the effective radius of the wheel; Indicates the lateral adhesion coefficient at the moment Indicates the th wheel vertical load; On the premise of satisfying the equality constraint and the dynamic constraint conditions, construct a quadratic programming optimization problem with the yaw moment error, the torque change rate and the power loss as the cost function, solve and calculate the optimal torque distribution result, and send it to each drive motor for each wheel electric drive motor to execute.

[0014] A stability control system for an electric vehicle adopting any of the methods of the present invention, wherein: a state estimation module collects vehicle driving data in real time and dynamically estimates the state parameters of the vehicle by using the unscented Kalman filtering algorithm; A yaw moment prediction module calculates the expected yaw moment within a short future time according to the state parameters of the vehicle, in combination with a kinematic model and a forward-looking trajectory, and obtains the vehicle longitudinal traction demand; A motor torque distribution module decouples and distributes the yaw moment and traction force to the four electric drive wheel ends according to the state parameters of the vehicle and the vehicle longitudinal traction demand, to achieve stable control of the electric vehicle.

[0015] Advantages of the present invention: By constructing a predictive control model based on road condition perception and vehicle body state estimation, the present invention can predict and calculate the expected yaw moment before yaw instability occurs, so as to dynamically distribute the motor torque in advance and achieve the forward-looking stable control of the vehicle. At the same time, a motor thermal constraint model is introduced in the method to avoid power derating caused by motor overheating on the premise of ensuring stability and improve the long-term reliability of the system. Compared with the traditional stability control system based only on closed-loop feedback, the control response of the present invention is faster, the yaw intervention is more accurate, and it shows better control performance and vehicle safety under low adhesion road surfaces and high load conditions. Description of the Drawings

[0016] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts; Figure 1 It is the overall flowchart of a stability control method for an electric vehicle provided by an embodiment of the present invention. Detailed Embodiments

[0017] To make the above objects, features and advantages of the present invention more obvious and understandable, the specific embodiments of the present invention will be described in detail below with reference to the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0018] Embodiment 1, referring to Figure 1 , which is an embodiment of the present invention, provides a stability control method for an electric vehicle, including: S1: Collect vehicle driving data in real time and dynamically estimate the state parameters of the vehicle by using the unscented Kalman filtering algorithm.

[0019] Vehicle driving data closely related to the dynamic behavior of the vehicle is collected in real time through a sensor network. The vehicle driving data includes vehicle speed, steering wheel angle, yaw angular velocity, lateral acceleration, rotational speed of each wheel, etc.

[0020] Through continuous collection and processing of the above vehicle driving data, a dynamic state data structure of the vehicle is constructed in real time, and accurate and low-latency data support is provided for subsequent yaw angular velocity control, adhesion coefficient estimation, and motor torque distribution algorithms. All sensor acquisition modules can be uniformly connected to the controller through the vehicle CAN bus or high-speed Ethernet interface, and support an update frequency of more than 100Hz to ensure the ability to quickly respond to and predict sudden working conditions.

[0021] Furthermore, to achieve real-time estimation of the key dynamic states of electric vehicles and improve the responsiveness and environmental adaptability of the yaw stability control system, based on the unscented Kalman filter (UKF), important state variables that are difficult to directly measure, such as vehicle sideslip angle and lateral adhesion coefficient, are synchronously estimated.

[0022] Obtain the current vehicle driving data, including longitudinal vehicle speed, steering wheel angle, yaw angular velocity, lateral acceleration, and rotational speed of each wheel, and construct an extended state vector , where represents the time, represents the sideslip angle, represents the yaw angular velocity, represents the lateral adhesion coefficient. On this basis, the system automatically calculates the slip ratio of the four tires at the current moment and extracts the maximum slip ratio value as an indicator variable for sudden changes in working conditions.

[0023] Use the filter to generate a set of points, and dynamically adjust the process noise covariance matrix according to the current maximum slip ratio. When sudden states such as forced skidding, low adhesion, or rapid acceleration and deceleration occur in the vehicle, the process noise automatically amplifies, thereby improving the dynamic tracking ability of state propagation and enhancing the estimation robustness.

[0024] According to the vehicle nonlinear lateral dynamics model (single-track model), perform state propagation on each point. Since the traditional linear tire force model is approximately valid under small sideslip angles and high adhesion conditions, it will predict excessive lateral force when the road surface adhesion coefficient decreases or the sideslip angle increases, resulting in distorted state estimation. The present invention introduces a saturation value jointly determined by the adhesion coefficient and the vertical load, uses the hyperbolic tangent function to envelope the side slip stiffness model, and constructs a lateral tire force saturation model with adjustable adhesion, expressed as: ; where Represents the lateral tire force generated by the current tire; Represents the current vertical load on the wheel; Represents the hyperbolic tangent function; Represents the cornering stiffness coefficient of the tire; Represents the cornering angle of the current wheel; This model can scale the output amplitude in real time according to the road surface adhesion coefficient under different road conditions to ensure avoiding overshoot of force estimation in high cornering or low adhesion scenarios.

[0025] After completing the state propagation, according to The predicted values of the points are used to calculate the weighted mean and covariance to obtain the prior state estimation result at the current moment. The state vector is mapped to the observation space through the measurement function and compared with the measured yaw rate, lateral acceleration, and wheel speed data to calculate the innovation term. The posterior estimation update is completed based on the unscented Kalman gain to obtain the state parameters of the vehicle. The state parameters of the vehicle include vehicle speed, cornering angle, yaw rate, and lateral adhesion coefficient.

[0026] It should be noted that the fixed covariance assumption makes the filter converge too slowly in high-slip scenarios and even diverge due to modeling errors. Taking the maximum tire slip rate as the "uncertainty indicator", the slip rates of the four wheels are calculated every cycle and the maximum value is taken, multiplied by an empirical coefficient to form incremental noise, which is added to the reference matrix to update the adaptive process noise covariance: ; Among them, Represents the adaptive process noise covariance; Represents the initial process noise covariance; Represents the empirical coefficient; Represents the Maximum tire slip rate at the moment; Represents the identity matrix; When the slip rate increases, the process noise is linearly amplified, allowing the filter to make faster corrections to the model error.

[0027] S2: According to the state parameters of the vehicle, combined with the kinematic model and the forward-looking trajectory, calculate the desired yaw moment in the short term in the future and obtain the vehicle's longitudinal traction demand; Further, the forward-looking trajectory refers to the spatial expression of the expected driving path within a certain period of time or distance forward based on the data provided by the driver input or the autonomous driving path planner under the current driving state of the vehicle, and is used to predict the future yaw behavior of the vehicle. The forward-looking trajectory includes the forward-looking distance or forward-looking time period, the sequence of trajectory points, and the reference of the target yaw behavior. The path points can be derived from the driver's steering input or provided by the path planning module of the autonomous driving system.

[0028] Furthermore, set a fixed forward-looking time window , and calculate according to the vehicle speed The forward view distance within, within the forward view distance, perform cubic spline fitting on the forward view trajectory, calculate the average curvature, and obtain the desired yaw rate.

[0029] Calculate the yaw rate error and the sideslip angle suppression error. The yaw rate error is the difference between the desired yaw rate and the actual yaw rate, and the sideslip angle suppression error is expressed as the negative value of the sideslip angle. Use the vehicle's state parameters to perform real-time linearization on the bicycle model, generate the state matrix and the input matrix to replace the fixed parameter matrix, and construct an adaptive linearized vehicle model: ; Among them, represents the state vector at time ; represents the state vector at time ; represents the discrete state matrix; represents the input matrix; represents the control input, the yaw moment at time ; and Change according to the real-time .

[0030] Set the prediction step , additionally introduce the sideslip angle as the state estimation output, set the sideslip angle error as the target quantity, and construct the objective function and the constraint conditions. The objective function is expressed as: ; Constraint conditions: ; Among them, represents the controller objective function value; represents the prediction step; represents time; represents the weight of the yaw rate error; represents the yaw rate error at time ; represents the weight of the sideslip angle error; represents the sideslip angle error at time ; represents the weight of the yaw moment change rate; represents the yaw moment change rate at time , obtained by calculating the difference in the yaw moment between the current moment and the previous moment; represents the control input, the yaw moment at time ; represents the maximum yaw moment; represents the maximum yaw moment change rate. Calculated from the current adhesion coefficient estimation result to ensure that the required torque is within the limit of the tire lateral force.

[0031] The adaptive linearized vehicle model, objective function, and constraint conditions are jointly compiled into a QP problem. A real-time QP solver is used to obtain the optimal yaw torque sequence within a single cycle, and the solution at the first moment is taken as the desired yaw torque within.

[0032] Different from the traditional ESP (Electronic Stability Program) that only controls the yaw rate, the present invention introduces a combined objective function of the sideslip angle error and the yaw rate error to achieve multi-objective coordinated control of the vehicle attitude, avoid the problem of side slip expansion caused by single yaw rate control, and improve the comprehensive control performance.

[0033] Furthermore, the vehicle longitudinal traction demand is the target traction force required by the vehicle according to the driver's input, and is calculated and obtained by the vehicle control unit VCU.

[0034] Specifically, through the existing pedal sensors or upper control modules of the vehicle, the acceleration / deceleration demands issued by the driver's intention or the autonomous driving system are obtained. The VCU has built-in vehicle longitudinal control strategies (such as starting power limit, regenerative braking, drive distribution, etc.), and will calculate the total driving force demand or the total drive axle torque at the current moment. Calculating the total traction demand by the vehicle control unit (VCU) is the basic configuration of all current electric vehicles, and this system only reads the output result of the VCU as a lower-level execution module.

[0035] S3: According to the vehicle state parameters and the vehicle longitudinal traction demand, decouple and distribute the yaw torque and the traction force to the four electric drive wheel ends to achieve the stable control of the electric vehicle.

[0036] Based on the requirements of actual driving, the motor torque distribution module needs to distribute the desired yaw torque and the longitudinal traction demand to the four-wheel end motor torques within a 1-2 ms control cycle, taking into account the tire adhesion limit, drive power, temperature rise constraint, and ride comfort. Specifically, it includes the following steps: Receive the control inputs from the upper layer in real time, including: the desired yaw torque (output by the yaw prediction module), the longitudinal traction demand (calculated from the vehicle control unit or the driver's input), the current motor temperature of each wheel and the torque output value of the previous cycle, the lateral adhesion coefficient, and the vertical load (provided by the state estimation module).

[0037] According to the vehicle state parameters and physical relationships, establish an equality constraint of the four-wheel torques on the vehicle longitudinal traction force and the yaw torque. The resultant force of the four wheels satisfies the longitudinal traction demand, and the left-right distribution of the four wheels satisfies the desired yaw torque.

[0038] Set dynamic constraint conditions for each wheel electric drive motor, including torque upper limit constraint (including thermal derating): ; Among them, represents the minimum torque; represents the th target output torque of the wheel; represents the maximum available torque related to temperature; when the motor winding temperature is relatively high, the torque upper limit decreases automatically with temperature; The adhesion limit constraint estimates the maximum traction force through the vehicle's state parameters and the current vertical load of each wheel, expressed as:

[0039] Among them, represents the th motor torque output of the wheel; represents the effective radius of the wheel; represents the moment lateral adhesion coefficient; represents the th vertical load of the wheel; the adhesion limit constraint is used to ensure that the torque at each wheel end does not exceed the maximum traction force allowed by the road surface, avoiding wheel slip; Power limit constraint: ; Among them, represents the wheel angular velocity; represents the th maximum allowable electric power at the wheel end. The power limit constraint prevents the drive, battery, or inverter system from being overloaded; Torque change rate limit: ; Among them, represents the torque output of this wheel in the previous control cycle; represents the maximum allowable torque change rate; represents the control cycle time; the torque change rate limit is used to prevent large mutations in the motor torque command, avoiding vehicle dynamic instability or discomfort in ride.

[0040] On the premise of satisfying the equality constraints and dynamic constraints, a quadratic programming optimization problem with the yaw moment error, torque change rate, and power loss as the cost function is constructed, expressed as: ; Among them, , , represent the weight factors in the control objective function, which can be adaptively set according to the working conditions; represents the four-wheel motor torque vector; Represents the theoretical equilibrium torque under the average distribution of traction force. The objective function Consists of three parts. The first item penalizes torque mutation, the second item penalizes motor power loss, and the third item penalizes deviation of traction distribution, ensuring that while meeting yaw control, the system energy consumption efficiency and control smoothness are taken into account.

[0041] Use a structured sparse QP solver (such as OSQP) to solve and calculate the optimal torque distribution result. After filtering and limiting processing, it is sent to each drive motor at each wheel for execution. Record the output result for the change rate constraint in the next cycle. If it is found that any wheel end output touches the saturation or constraint boundary, control risk signals such as insufficient adhesion or execution force can be fed back to the upper-level system.

[0042] Through the above method, the present invention not only realizes the dynamic coordinated control of the torque of the four electric drive wheels, but also ensures the stability, safety and driving efficiency of the vehicle under different road conditions, loads and thermal environments through the joint constraints on the adhesion state and thermal state. This distribution mechanism can be compatible with mainstream electric vehicle platforms and has good engineering adaptability and popularization value.

[0043] Embodiment 2, in an exemplary embodiment, a stability control system for an electric vehicle is further provided, including a state estimation module, a yaw moment prediction module, and a motor torque distribution module.

[0044] Among them, the state estimation module is used to obtain and process the driving data of the vehicle in real time, including basic information such as vehicle speed, steering wheel angle, yaw angular velocity, lateral acceleration, and rotational speed of each wheel. Based on the unscented Kalman filter algorithm, this module dynamically estimates key state parameters that cannot be directly measured, such as road surface adhesion coefficient, vehicle centroid sideslip angle, vehicle body mass, and vertical load distribution. The estimation result provides an accurate system state basis for subsequent yaw moment prediction and torque distribution.

[0045] The yaw moment prediction module receives the current vehicle state parameters from the state estimation module, and combines the forward-looking trajectory data and the kinematic model of the vehicle to predict the dynamic trend of the vehicle in a short time and calculate the required desired yaw moment. This module also determines the longitudinal traction demand of the vehicle according to the current driving instruction or the autonomous driving path of the vehicle. The yaw moment and the traction demand jointly constitute the target guidance for the lower-level execution system.

[0046] The motor torque distribution module is used to intelligently distribute the total torque to the four independent drive wheel ends on the premise of meeting the desired yaw moment and longitudinal traction demand, in combination with the current adhesion state, motor capabilities and load conditions of the vehicle. Through the fine control of the torque, both the stable adjustment of the vehicle attitude can be realized, and the driving efficiency and motor safety can be guaranteed, thereby improving the driving stability and response ability of the whole vehicle under variable working conditions.

[0047] A top-down data and instruction transfer chain is formed among the modules: the state estimation module provides basic state support, the yaw moment prediction module formulates control objectives, and the motor torque distribution module completes instruction execution. The overall system has high integration and dynamic adaptability, can respond to changes in the vehicle driving state in real time, and realizes the stability control of high-performance electric drive vehicles under various road and operating conditions.

[0048] If the above functions are implemented in the form of software function units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs, etc., which can store program codes.

[0049] The logic and / or steps represented in the flowchart or described in other ways herein, for example, can be considered as a definite sequence list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch instructions from the instruction execution system, apparatus, or device and execute the instructions), or used in combination with these instruction execution systems, apparatuses, or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.

[0050] More specific examples (nonexhaustive list) of computer-readable media include the following: an electrical connection part with one or more wirings (electronic device), a portable computer disk cartridge (magnetic device), random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber device, and portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other media, then editing, interpreting, or processing it in other suitable ways as necessary, and then storing it in the computer memory.

[0051] It should be understood that each part of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, any one or a combination of the following techniques well known in the art can be used: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application specific integrated circuits having suitable combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), and the like.

[0052] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered by the scope of the claims of the present invention.

Claims

1. A stability control method for an electric vehicle, characterized in that, Including: Collect vehicle driving data in real time and dynamically estimate the state parameters of the vehicle using the unscented Kalman filter algorithm; According to the state parameters of the vehicle, combine the kinematic model and the forward-looking trajectory to calculate the expected yaw moment in the short term in the future and obtain the vehicle longitudinal traction demand; According to the state parameters of the vehicle and the vehicle longitudinal traction demand, decouple and distribute the yaw moment and traction force to the four electric drive wheel ends to achieve stable control of the electric vehicle.

2. The stability control method for an electric vehicle according to claim 1, characterized in that: The vehicle driving data includes vehicle speed, steering wheel angle, yaw rate, lateral acceleration, and wheel speed.

3. The stability control method of an electric vehicle according to claim 2, characterized in that: The estimating of the state parameters of the vehicle includes constructing an extended state vector based on the vehicle driving data and calculating the instantaneous slip ratio of each wheel; Generate a set using a filter points, and dynamically adjust the process noise covariance matrix according to the current maximum slip rate; Introduce the saturation value jointly determined by the adhesion coefficient and the vertical load, use the hyperbolic tangent function to envelope the cornering stiffness model, construct the lateral tire force saturation model, embed the lateral tire force saturation model into the state propagation function, and perform non-linear prediction on the points; According to Calculate the weighted mean and covariance based on the predicted values of the points to obtain the prior state estimation result at the current moment, calculate the innovation term, complete the posterior estimation update based on the unscented Kalman gain, and obtain the state parameters of the vehicle. The state parameters of the vehicle include vehicle speed, sideslip angle, yaw rate, and lateral adhesion coefficient.

4. The stability control method for an electric vehicle according to claim 3, wherein: The forward-looking trajectory is a sequence of target path points within a certain time window forward with the current moment of the vehicle as a reference, including multiple spatial position points and the curvature information corresponding to the spatial position points.

5. The stability control method for an electric vehicle according to claim 4, characterized in that: The calculation of the expected yaw moment in the short term in the future includes setting a fixed forward-looking time window , calculating the forward-looking distance within, performing cubic spline fitting on the forward-looking trajectory within the forward-looking distance, calculating the average curvature, and obtaining the expected yaw angular velocity; Calculate the yaw rate error and the sideslip angle suppression error, linearly linearize the bicycle model in real time using the state parameters of the vehicle, generate the state matrix and the input matrix, and construct an adaptive linearized vehicle model; Set the prediction step length , additionally introduce the sideslip angle as the state estimation output, set the sideslip angle error as the target quantity, and construct the objective function and constraint conditions. The objective function is expressed as: ； Among them, represents the controller objective function value; represents the prediction step length; represents time; represents the weight of the yaw rate error; represents the time yaw rate error at; represents the weight of the sideslip angle error; represents the time sideslip angle error at; represents the weight of the torque change rate; represents the time torque change rate at; represents the control input, the yaw moment at time ; Compile the adaptive linearized vehicle model, objective function, and constraints together into a QP problem, and use a real-time QP solver to obtain the optimal yaw moment sequence within a single period. Take the solution at the first moment as the desired yaw moment within.

6. The stability control method for an electric vehicle according to claim 5, characterized in that: The vehicle longitudinal traction demand is the target traction force required by the vehicle according to the driver's input, which is calculated and obtained by the vehicle control unit VCU.

7. The stability control method for an electric vehicle according to claim 6, characterized in that: The decoupling and distributing the yaw moment and traction force to the four electric drive wheel ends includes establishing an equality constraint of the four-wheel torque on the vehicle longitudinal traction force and the yaw moment according to the state parameters and physical relationships of the vehicle, the resultant force of the four wheels satisfies the longitudinal traction demand, and the left-right distribution of the four wheels satisfies the expected yaw moment; Set dynamic constraint conditions for each wheel electric drive motor, including torque upper limit constraint, adhesion limit constraint, power limit constraint, and torque change rate limit. The adhesion limit constraint estimates the maximum traction force through the state parameters of the vehicle and the current vertical load of each wheel, expressed as: ； Among them, represents the motor torque output of the th wheel; represents the effective radius of the wheel; represents the lateral adhesion coefficient at time ; represents the vertical load of the th wheel; On the premise of satisfying the equality constraint and the dynamic constraint conditions, construct a quadratic programming optimization problem with the yaw moment error, torque change rate, and power loss as the cost function, solve and calculate the optimal torque distribution result, and send it to each drive motor for each wheel electric drive motor to execute.

8. A stability control system for an electric vehicle, which is applied to the stability control method for an electric vehicle according to any one of claims 1 to 7, characterized in that, Including, A state estimation module that collects vehicle driving data in real time and dynamically estimates the state parameters of the vehicle using the unscented Kalman filter algorithm; A yaw moment prediction module that calculates the expected yaw moment in the short term in the future according to the state parameters of the vehicle, combines the kinematic model and the forward-looking trajectory, and obtains the vehicle longitudinal traction demand; A motor torque distribution module that decouples and distributes the yaw moment and traction force to the four electric drive wheel ends according to the state parameters of the vehicle and the vehicle longitudinal traction demand to achieve stable control of the electric vehicle.

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