A method, device and medium for dynamic compensation of brake deviation without clamping force sensor

Abstract

This invention relates to a method, device, and medium for dynamic compensation of brake drift without clamping force sensors, comprising: acquiring multi-dimensional sensor data and obtaining vehicle state parameters through filtering and fusion; calculating a reference yaw rate and a reference lateral acceleration based on the front wheel steering angle, longitudinal vehicle speed, and tire-road peak adhesion coefficient; using the difference between the actual yaw rate and the reference yaw rate as the yaw rate deviation, and the difference between the actual lateral acceleration and the reference lateral acceleration as the lateral acceleration deviation; when the brake pedal travel is not zero and any deviation exceeds the corresponding threshold, calculating an additional yaw moment based on the yaw rate deviation, and then calculating the braking force compensation amount for the corresponding wheel; converting the braking force compensation amount into an execution command and outputting the target torque of each wheel's brake motor. Compared with the prior art, this invention has advantages such as high accuracy of multi-source information fusion, strong adaptive dynamic condition capability, and strong robustness.

Description

Technical Field

[0001] This invention relates to the field of automotive braking control technology, and in particular to a method, device and medium for dynamic compensation of brake deviation without clamping force sensor. Background Technology

[0002] With the rapid development of automotive intelligence and drive-by-wire technologies, electromechanical braking systems (EMB) have gradually become the core development direction for next-generation automotive braking systems due to their advantages such as fast response, high control precision, and ease of integration with active safety functions, as they eliminate traditional hydraulic lines. However, in actual braking scenarios, due to limitations such as cost and space constraints, most EMB systems are not equipped with clamping force sensors, making it impossible to directly obtain the actual braking force value. When the four-wheel EMB system is subjected to external interference, it is prone to vehicle braking deviation, seriously threatening driving safety.

[0003] To address brake pull issues, existing compensation technologies are mostly based on traditional hydraulic braking systems, indirectly intervening through anti-lock braking systems (ABS) and electronic stability control (ESP). However, these methods suffer from response lag and limited compensation accuracy. For electromechanical braking systems without clamping force sensors, existing compensation methods have the following shortcomings: First, they rely heavily on single sensor data for feedback compensation, failing to fully integrate multi-dimensional vehicle state information, resulting in insufficient accuracy in compensation decisions; second, they lack effective modeling of nonlinear factors during braking, leading to poor robustness of the compensation algorithms; and third, compensation strategies are mostly static rule-based designs, unable to adaptively adjust according to vehicle dynamic conditions, and difficult to cover complex driving scenarios. Therefore, how to fully utilize multi-source vehicle state information to adaptively address complex dynamic conditions and improve robustness and compensation accuracy for brake pull compensation without relying on clamping force sensors is a technical problem that needs to be solved. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of the existing technology by providing a method, device and medium for dynamic compensation of brake drift without clamping force sensor. After obtaining vehicle state parameters through multi-source information fusion, a PID control strategy is adopted to calculate the additional yaw moment with the yaw rate deviation approaching zero. The braking force compensation is then distributed to the most effective wheel by combining the steering direction and the sign of the additional yaw moment, thereby achieving dynamic and accurate suppression of brake drift under the condition of no clamping force sensor.

[0005] The objective of this invention can be achieved through the following technical solutions: According to one aspect of the present invention, a method for dynamic compensation of brake deviation without a clamping force sensor is provided, the specific steps of which include: S1. Collect multi-dimensional sensor data during vehicle braking process, and obtain vehicle state parameters through filtering and fusion processing; S2. Calculate the reference yaw rate and reference lateral acceleration based on the front wheel steering angle, longitudinal vehicle speed and tire-road peak adhesion coefficient in the vehicle state parameters; S3. The difference between the actual yaw rate and the reference yaw rate is taken as the yaw rate deviation, and the difference between the actual lateral acceleration and the reference lateral acceleration is taken as the lateral acceleration deviation. When the brake pedal travel is not zero and the yaw rate deviation or the lateral acceleration deviation exceeds the corresponding threshold, an additional yaw moment is calculated based on the yaw rate deviation, and the braking force compensation of the corresponding wheel is calculated based on the additional yaw moment. S4. Convert the braking force compensation amount into an execution command for the electromechanical brake actuator and output the target torque of each wheel brake motor.

[0006] Furthermore, the multi-dimensional sensor data in S1 includes wheel speed sensor data, front wheel steering angle sensor data, brake pedal travel sensor data, yaw rate sensor data, lateral acceleration sensor data, longitudinal acceleration sensor data, and tire-road peak adhesion coefficient sensor data. The filtering and fusion processing uses a Kalman filter algorithm for noise reduction, and then outputs vehicle state parameters through a weighted fusion strategy. The vehicle state parameters include longitudinal vehicle speed, wheel speed of each wheel, front wheel steering angle, brake pedal travel, vehicle yaw rate, lateral acceleration, longitudinal acceleration, and tire-road peak adhesion coefficient.

[0007] Further, in S2, the reference yaw rate is calculated as follows: based on the front wheel steering angle, longitudinal vehicle speed, tire-road peak adhesion coefficient, vehicle mass, distance from center of gravity to front axle, distance from center of gravity to rear axle, wheelbase, front axle equivalent lateral stiffness, and rear axle equivalent lateral stiffness; the value of the reference yaw rate is limited to a preset multiple of the product of tire-road peak adhesion coefficient and gravitational acceleration; the reference lateral acceleration is calculated based on the product of longitudinal vehicle speed and the reference yaw rate.

[0008] Furthermore, in S3, the calculation of the additional yaw moment adopts a PID control strategy, with the yaw rate deviation approaching zero as the control objective, and is calculated based on the proportional coefficient, integral coefficient, and time function of the yaw rate deviation.

[0009] Furthermore, in S3, the braking force compensation amount is calculated as follows: the basic compensation amount is obtained based on the ratio of the additional yaw moment to the vehicle wheel track, and then the basic compensation amount is compared with the limit value determined by the total braking force request and the actual braking deceleration based on the brake pedal travel analysis, and the smaller value is taken as the final braking force compensation amount.

[0010] Furthermore, the method for determining the corresponding wheel in S3 is as follows: the target of action is determined based on the combination of the sign of the additional yaw moment and the vehicle's steering direction. When the vehicle turns left and the additional yaw moment is positive, it acts on the left rear wheel; when the vehicle turns right and the additional yaw moment is positive, it acts on the left front wheel; when the vehicle turns left and the additional yaw moment is negative, it acts on the right front wheel; and when the vehicle turns right and the additional yaw moment is negative, it acts on the right rear wheel.

[0011] Furthermore, the execution command is sent to the brake actuators of each wheel via the CAN bus to adjust the braking clamping force of the left and right wheels and to dynamically redistribute the braking force.

[0012] Furthermore, the method also includes compensation correction, which monitors the changing trends of the yaw rate deviation and the lateral acceleration deviation in real time during the compensation execution process. When both deviations return to the corresponding threshold range, the current redistribution mechanism is maintained. When the deviations do not improve or worsen, the braking force compensation amount is allocated to the electromechanical brake actuators of the same side wheel for online correction, and the vehicle state parameters after braking execution are fed back to the filtering and fusion processing in real time.

[0013] According to a second aspect of the present invention, an electronic device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the program to implement the method described thereon.

[0014] According to a third aspect of the present invention, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the method described thereon.

[0015] Compared with the prior art, the present invention has the following beneficial effects: (1) By using multi-source sensor data fusion and filtering, the perception accuracy of vehicle state parameters is improved. This invention uses the Kalman filter algorithm to denoise multi-dimensional sensor data such as wheel speed sensor, yaw rate sensor, and lateral acceleration sensor. After removing singular values, the key state parameters such as longitudinal vehicle speed, yaw rate, lateral acceleration and tire-road peak adhesion coefficient are output through a weighted fusion strategy. The multi-source information fusion method effectively avoids the problem of large noise and easy interference of single sensor data, and can provide high-precision and high-reliability data support for deviation identification. Thus, under the condition of no clamping force sensor, it ensures that the subsequent compensation calculation is based on the accurate real-time vehicle state, thereby improving the overall perception capability and decision credibility of the braking deviation compensation system.

[0016] (2) A PID closed-loop control strategy based on yaw rate deviation is adopted to achieve rapid response and accurate calculation of additional yaw torque. The deviation between the actual yaw rate and the reference yaw rate is used as the control target. A PID control strategy is designed to calculate the additional yaw torque. By configuring the parameters of proportional coefficient and integral coefficient, the yaw rate deviation is made to quickly approach zero. The closed-loop feedback mechanism used can dynamically adjust the compensation torque output according to the real-time deviation. Compared with static rules or open-loop control, the calculation response speed and adjustment accuracy of additional yaw torque are improved, so that an effective correction torque is quickly generated when braking deviation occurs, and the lateral deviation trend of the vehicle is suppressed.

[0017] (3) Combining the optimal wheel distribution mechanism with the steering direction and the sign of the additional yaw moment, the braking force compensation is accurately applied and has online correction capability. Based on the combination of the positive and negative signs of the additional yaw moment and the vehicle steering direction, the braking force compensation is distributed to the most effective wheel, such as turning left and the positive moment acting on the left rear wheel, turning right and the positive moment acting on the left front wheel, etc. At the same time, the wheel track, total braking force request and actual braking deceleration are used to limit the compensation amount. During the compensation execution process, the trend of the deviation characteristic index is monitored in real time. When the deviation is not improved or worsens, the compensation amount is distributed to the same side wheel for online correction. This ensures that the braking force compensation is accurately applied to the wheel that can produce the most deviation correction effect and has adaptive adjustment capability. Thus, without the need for clamping force sensors, the deviation phenomenon caused by braking imbalance is effectively suppressed, and the braking safety and driving stability of the vehicle under different working conditions are ultimately improved. Attached Figure Description

[0018] Figure 1 A flowchart of a dynamic compensation method for brake deviation without a clamping force sensor; Figure 2 This is a data flow diagram for a dynamic compensation method for brake deviation without a clamping force sensor. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0020] In the field of vehicle braking control technology, electromechanical braking systems offer advantages such as fast response and high control precision due to the elimination of traditional hydraulic lines. However, limited by cost and layout space, most systems lack clamping force sensors, making it impossible to directly obtain actual braking force values. This results in brake drift becoming a significant safety hazard. Existing compensation methods have several shortcomings: for example, they often rely on data from a single sensor, failing to fully integrate multi-dimensional vehicle status information, leading to insufficient accuracy in compensation decisions; compensation strategies are mostly static rule designs, unable to adaptively adjust according to dynamic conditions such as vehicle speed, load, and road surface conditions, making it difficult to cover complex driving scenarios; and the algorithms have poor robustness, making them susceptible to parameter disturbances or external interference in practical applications. Therefore, this embodiment proposes a braking deviation method, which improves the accuracy of vehicle state parameters through Kalman filtering and weighted fusion; calculates yaw rate deviation and lateral acceleration deviation based on a reference model to accurately determine deviation; uses PID control to calculate additional yaw moment and distribute it to the most effective wheel; and achieves adaptive compensation through closed-loop feedback and online correction, effectively solving the problems of insufficient accuracy of compensation decision and poor adaptability to working conditions in the prior art, and realizing dynamic and accurate suppression of braking deviation under the condition of no clamping force sensor.

[0021] like Figure 1 As shown, this embodiment provides a dynamic compensation method for brake deviation without a clamping force sensor. The specific steps include: S1. Collect multi-dimensional sensor data during vehicle braking process, and obtain vehicle state parameters through filtering and fusion processing; S2. Calculate the reference yaw rate and reference lateral acceleration based on the front wheel steering angle, longitudinal vehicle speed and tire-road peak adhesion coefficient in the vehicle state parameters; S3. The difference between the actual yaw rate and the reference yaw rate is taken as the yaw rate deviation, and the difference between the actual lateral acceleration and the reference lateral acceleration is taken as the lateral acceleration deviation. When the brake pedal travel is not zero and the yaw rate deviation or lateral acceleration deviation exceeds the corresponding threshold, the additional yaw moment is calculated based on the yaw rate deviation, and the braking force compensation of the corresponding wheel is calculated based on the additional yaw moment. S4. Convert the braking force compensation amount into the execution command of the electromechanical brake actuator and output the target torque of each wheel brake motor.

[0022] like Figure 2 The diagram shown illustrates the data flow of the dynamic compensation method for brake pull. In this embodiment, the dynamic compensation method for brake pull is implemented by constructing a multi-source information filtering and fusion module, a vehicle reference model module, a deviation calculation module, a brake pull criterion module, a braking force redistribution module, and a braking force compensation command conversion module.

[0023] The vehicle reference model module calculates reference yaw rate and reference lateral acceleration based on the front wheel steering angle, longitudinal vehicle speed, and tire-road peak adhesion coefficient output by the multi-source information filtering and fusion module. The deviation calculation module subtracts the reference yaw rate and reference lateral acceleration output by the vehicle reference model module from the actual yaw rate and actual lateral acceleration output by the multi-source information filtering and fusion module, outputting the yaw rate deviation and lateral acceleration deviation to the brake pullback criterion module and the brake force redistribution module. The brake pullback criterion module generates an algorithm intervention flag based on the received yaw rate deviation and lateral acceleration deviation, combined with the brake pedal travel signal. The brake force redistribution module calculates the braking force compensation for each wheel based on the vehicle state parameters output by the multi-source information filtering and fusion module, the yaw rate deviation output by the deviation calculation module, and the algorithm intervention flag output by the brake pullback criterion module. The brake force compensation command conversion module converts the braking force compensation calculated by the brake force redistribution module into the target torque of each wheel's brake motor and outputs it.

[0024] In S1, multi-dimensional sensor data includes wheel speed sensor data, front wheel steering angle sensor data, brake pedal travel sensor data, yaw rate sensor data, lateral acceleration sensor data, longitudinal acceleration sensor data, and tire-road peak adhesion coefficient sensor data. After noise reduction using the Kalman filter algorithm, the vehicle state parameters are output through a weighted fusion strategy. The vehicle state parameters include longitudinal vehicle speed, wheel speed of each wheel, front wheel steering angle, brake pedal travel, vehicle yaw rate, lateral acceleration, longitudinal acceleration, and tire-road peak adhesion coefficient.

[0025] In S2, the reference yaw rate is calculated as follows: based on the front wheel steering angle, longitudinal vehicle speed, tire-road peak adhesion coefficient, vehicle mass, distance from center of gravity to front axle, distance from center of gravity to rear axle, wheelbase, front axle equivalent lateral stiffness, and rear axle equivalent lateral stiffness; the value of the reference yaw rate is limited to a preset multiple of the product of tire-road peak adhesion coefficient and gravitational acceleration; the reference lateral acceleration is calculated based on the product of longitudinal vehicle speed and reference yaw rate.

[0026] Specifically, by combining the front wheel steering angle, longitudinal vehicle speed, and tire-road peak adhesion coefficient, the ideal reference yaw rate of the vehicle is calculated. and reference lateral acceleration The expression is: , , in, For the overall vehicle quality, This is the distance from the center of gravity to the front axle. This is the distance from the center of mass to the rear axle. Wheelbase For the front axle equivalent lateral stiffness, For the equivalent lateral stiffness of the rear axle, For longitudinal vehicle speed, For the front wheel steering angle, This refers to the peak coefficient of adhesion between the tire and the road surface. This is the acceleration due to gravity.

[0027] The braking pull characteristic indicators are calculated, including yaw rate deviation and lateral acceleration deviation. The yaw rate deviation is the difference between the actual yaw rate and the reference yaw rate, and the lateral acceleration deviation is the difference between the actual lateral acceleration and the reference lateral acceleration. When the brake pedal travel is not zero and the yaw rate deviation or lateral acceleration deviation exceeds the corresponding threshold, the vehicle is determined to be in a braking pull state. The yaw rate deviation threshold is set at 0.03 rad / s, and the lateral acceleration deviation threshold is set at 0.3 m / s². 2 In S3, the calculation of the additional yaw moment adopts a PID control strategy, with the control objective being to make the yaw rate deviation approach zero. It is calculated based on the proportional coefficient, integral coefficient, and time function of the yaw rate deviation.

[0028] The braking force compensation amount is calculated as follows: the basic compensation amount is obtained based on the ratio of the additional yaw moment to the vehicle wheel track. Then, the basic compensation amount is compared with the limit value determined by the total braking force request and the actual braking deceleration based on the brake pedal travel analysis, and the smaller value is taken as the final braking force compensation amount.

[0029] The method for determining the corresponding wheel is as follows: the target is determined by the combination of the sign of the additional yaw moment and the vehicle's steering direction. When the vehicle turns left and the additional yaw moment is positive, it acts on the left rear wheel; when the vehicle turns right and the additional yaw moment is positive, it acts on the left front wheel; when the vehicle turns left and the additional yaw moment is negative, it acts on the right front wheel; and when the vehicle turns right and the additional yaw moment is negative, it acts on the right rear wheel.

[0030] Specifically, based on the yaw rate deviation calculated by S2, a PID control strategy is adopted, with the yaw rate deviation approaching zero as the control objective. A deviation compensation control law is designed, and the additional yaw torque is calculated. The expression is: , in, This is the proportionality coefficient. The integral coefficient is... To reference the deviation between the yaw rate and the actual yaw rate; in this embodiment, Take 1200, Take 40.

[0031] Calculate the braking force compensation amount for the corresponding wheels after redistribution. The expression is: , in, The wheelbase of the vehicle. The total braking force request is derived from the brake pedal travel. m For vehicle curb weight This refers to the actual braking deceleration of the vehicle.

[0032] Additional yaw moment is distributed to the most effective wheels for deviation compensation. The target of the action is determined by the sign of the additional yaw moment and the steering direction. The direction in which the additional yaw moment causes the vehicle to tend to turn to the left is defined as positive, and the opposite is negative. The wheel braking force redistribution strategy is as follows: when the steering direction is left and the additional yaw moment is positive, the target is the left rear wheel; when the steering direction is right and the additional yaw moment is positive, the target is the left front wheel; when the steering direction is left and the additional yaw moment is negative, the target is the right front wheel; when the steering direction is right and the additional yaw moment is negative, the target is the right rear wheel.

[0033] Finally, the execution command is sent to the brake actuators of each wheel via the CAN bus to adjust the braking clamping force of the left and right wheels and to dynamically redistribute the braking force.

[0034] Specifically, based on the constructed wheel braking force redistribution strategy, the calculated braking force compensation is added to the corresponding wheels; the braking force compensation is converted into execution commands for the electromechanical braking system, which include the target torque of each wheel's brake motor; the execution commands are sent to the brake actuators of each wheel of the electromechanical braking system via the CAN bus to adjust the braking clamping force of the left and right wheels, thereby realizing the dynamic redistribution of braking force; the vehicle state parameters after braking execution are collected in real time and fed back to the filtering and fusion processing of S1 to form a closed-loop compensation control.

[0035] The method in this embodiment also includes compensation correction. During the compensation execution process, the changing trends of yaw rate deviation and lateral acceleration deviation are monitored in real time. When both deviations return to the corresponding threshold range, the current redistribution mechanism is maintained. When the deviation does not improve or worsens, the braking force compensation is allocated to the electromechanical brake actuators of the same side wheels for online correction until the deviation is suppressed. The vehicle state parameters after braking execution are fed back to the filtering and fusion processing in real time.

[0036] To illustrate the braking deviation compensation method and system control performance of this embodiment, a simulation was used to implement the compensation control method provided in this embodiment. A typical braking deviation scenario was set up, and comparative experiments were conducted using the no-control method and the method of this invention. The experimental results are shown in Table 1.

[0037] Table 1 Simulation results of this embodiment Experimental results show that the maximum deviation of yaw rate and the maximum deviation of lateral acceleration are significantly reduced by the method of the present invention, and the compensation effect is better.

[0038] Therefore, the method provided in this embodiment improves the accuracy of state perception through multi-source information fusion. By integrating multi-dimensional sensor data such as wheel speed sensor data, yaw rate sensor data, lateral acceleration sensor data, and tire-road peak adhesion coefficient sensor data, and combining Kalman filtering noise reduction and weighted fusion strategies, it effectively improves the measurement accuracy of vehicle state parameters such as longitudinal vehicle speed, vehicle yaw rate, lateral acceleration, and tire-road peak adhesion coefficient, providing reliable data support for brake drift identification and compensation.

[0039] In addition, this embodiment adopts a PID control strategy, calculates the additional yaw moment with the yaw rate deviation approaching zero as the control target, and distributes the braking force compensation to the most effective wheel by combining the sign of the additional yaw moment and the vehicle steering direction, effectively improving the response speed in the braking deviation compensation process and enhancing the robustness and dynamic performance of the compensation control. Meanwhile, by feeding back the vehicle state parameters after braking to the filtering and fusion processing in real time, a closed-loop compensation control is formed. During the compensation process, the changing trends of yaw rate deviation and lateral acceleration deviation are monitored in real time. When the deviation does not improve or worsens, the braking force compensation is distributed to the electromechanical brake actuators of the same side wheel for online correction. This ensures that the compensation command can track the changes in the deviation state in real time, quickly suppress braking deviation, and improve vehicle braking safety and driving stability.

[0040] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the described module can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0041] The electronic device of this invention includes a central processing unit (CPU), which can perform various appropriate actions and processes according to computer program instructions stored in read-only memory (ROM) or loaded from a storage unit into random access memory (RAM). The RAM may also store various programs and data required for device operation. The CPU, ROM, and RAM are interconnected via a bus. Input / output (I / O) interfaces are also connected to the bus.

[0042] Multiple components in the device are connected to an I / O interface, including: input units such as a keyboard, mouse, etc.; output units such as various types of displays, speakers, etc.; storage units such as disks, optical disks, etc.; and communication units such as network interface cards, modems, wireless transceivers, etc. The communication unit allows the device to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks. The processing unit performs the various methods and processes described above, such as the method of the present invention. For example, in some embodiments, the method of the present invention may be implemented as a computer software program tangibly contained in a machine-readable medium, such as a storage unit. In some embodiments, part or all of the computer program may be loaded and / or installed on the device via ROM and / or the communication unit. When the computer program is loaded into RAM and executed by the CPU, one or more steps of the method of the present invention described above may be performed. Alternatively, in other embodiments, the CPU may be configured to execute the method of the present invention by any other suitable means (e.g., by means of firmware).

[0043] The functions described above in this document can be performed, at least in part, by one or more hardware logic components. For example, exemplary types of hardware logic components that can be used, without limitation, include: Field Programmable Gate Arrays (FPGAs), Application-Specific Integrated Circuits (ASICs), Application Standard Products (ASSPs), System-on-Chip (SoCs), Complex Programmable Logic Devices (CPLDs), and so on.

[0044] The program code used to implement the methods of the present invention can be written in any combination of one or more programming languages. This program code can be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing device, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code can be executed entirely on the machine, partially on the machine, as a standalone software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0045] In the context of this invention, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. Machine-readable media can include, but are not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0046] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and these modifications or substitutions should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for dynamic compensation of brake deviation without clamping force sensor, characterized in that, The specific steps include: S1. Collect multi-dimensional sensor data during vehicle braking process, and obtain vehicle state parameters through filtering and fusion processing; S2. Calculate the reference yaw rate and reference lateral acceleration based on the front wheel steering angle, longitudinal vehicle speed and tire-road peak adhesion coefficient in the vehicle state parameters; S3. The difference between the actual yaw rate and the reference yaw rate is taken as the yaw rate deviation, and the difference between the actual lateral acceleration and the reference lateral acceleration is taken as the lateral acceleration deviation. When the brake pedal travel is not zero and the yaw rate deviation or the lateral acceleration deviation exceeds the corresponding threshold, an additional yaw moment is calculated based on the yaw rate deviation, and the braking force compensation of the corresponding wheel is calculated based on the additional yaw moment. S4. Convert the braking force compensation amount into an execution command for the electromechanical brake actuator and output the target torque of each wheel brake motor.

2. The method for dynamic compensation of brake deviation without clamping force sensor according to claim 1, characterized in that, The multi-dimensional sensor data in S1 includes wheel speed sensor data, front wheel steering angle sensor data, brake pedal travel sensor data, yaw rate sensor data, lateral acceleration sensor data, longitudinal acceleration sensor data, and tire-road peak adhesion coefficient sensor data. The filtering and fusion process uses the Kalman filter algorithm for noise reduction, and then outputs vehicle state parameters through a weighted fusion strategy. The vehicle state parameters include longitudinal vehicle speed, wheel speed of each wheel, front wheel steering angle, brake pedal travel, vehicle yaw rate, lateral acceleration, longitudinal acceleration, and tire-road peak adhesion coefficient.

3. The method for dynamic compensation of brake deviation without clamping force sensor according to claim 1, characterized in that, In S2, the reference yaw rate is calculated as follows: based on the front wheel steering angle, longitudinal vehicle speed, tire-road peak adhesion coefficient, vehicle mass, distance from center of gravity to front axle, distance from center of gravity to rear axle, wheelbase, front axle equivalent lateral stiffness, and rear axle equivalent lateral stiffness; the value of the reference yaw rate is limited to a preset multiple of the product of tire-road peak adhesion coefficient and gravitational acceleration; the reference lateral acceleration is calculated based on the product of longitudinal vehicle speed and the reference yaw rate.

4. The method for dynamic compensation of brake deviation without clamping force sensor according to claim 1, characterized in that, In S3, the calculation of the additional yaw moment adopts a PID control strategy, with the yaw rate deviation approaching zero as the control objective, and is calculated based on the proportional coefficient, integral coefficient, and time function of the yaw rate deviation.

5. The method for dynamic compensation of brake deviation without clamping force sensor according to claim 1, characterized in that, In S3, the braking force compensation amount is calculated as follows: the basic compensation amount is obtained based on the ratio of the additional yaw moment to the vehicle wheel track. Then, the basic compensation amount is compared with the limit value determined by the total braking force request and the actual braking deceleration based on the brake pedal travel analysis, and the smaller value is taken as the final braking force compensation amount.

6. The method for dynamic compensation of brake deviation without clamping force sensor according to claim 1, characterized in that, The method for determining the corresponding wheel in S3 is as follows: the target of the action is determined based on the combination of the sign of the additional yaw moment and the vehicle's steering direction. When the vehicle turns left and the additional yaw moment is positive, it acts on the left rear wheel; when the vehicle turns right and the additional yaw moment is positive, it acts on the left front wheel; when the vehicle turns left and the additional yaw moment is negative, it acts on the right front wheel; and when the vehicle turns right and the additional yaw moment is negative, it acts on the right rear wheel.

7. The method for dynamic compensation of brake deviation without clamping force sensor according to claim 1, characterized in that, The execution command is sent to the brake actuators of each wheel via the CAN bus to adjust the braking clamping force of the left and right wheels and to dynamically redistribute the braking force.

8. The method for dynamic compensation of brake deviation without clamping force sensor according to claim 1, characterized in that, The method also includes compensation correction, which monitors the changing trends of the yaw rate deviation and the lateral acceleration deviation in real time during the compensation process, and maintains the current redistribution mechanism when both deviations return to the corresponding threshold range. When the deviation is not improved or worsens, the braking force compensation is distributed to the electromechanical brake actuators on the same side of the wheel for online correction, and the vehicle status parameters after braking are fed back to the filtering and fusion processing in real time.

9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the program, it implements the method as described in any one of claims 1 to 8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the method as described in any one of claims 1 to 8.

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