Prejudgment type braking force dynamic distribution method based on multi-source sensing fusion

Abstract

The invention discloses a pre-judgment type braking force dynamic distribution method based on multi-source sensing fusion, and belongs to the field of vehicle braking, and the method comprises the following steps: S1, multi-source sensing data acquisition and preprocessing: synchronously acquiring vehicle state data and road conditions; s2, on the basis of the vehicle state data and the road condition collected in the step S1, the braking force requirement is recognized in advance through feed-forward logic, and feed-forward braking torque is determined; s3, based on the real-time braking state, feedback braking torque is determined through a PID algorithm; and S4, the feedforward braking torque and the feedback braking torque are fused through dynamic weight distribution, and a four-wheel unified pre-judgment type synthetic braking force torque is generated. By adopting the pre-judgment type braking force dynamic distribution method based on the multi-source sensing fusion, a real-time sensing layer is constructed through the multi-source sensing fusion, a feedforward-feedback double-loop controller and a curve pre-execution mechanism are designed, efficient and accurate dynamic distribution of braking force is achieved, and the braking performance and stability of a vehicle are remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of vehicle braking technology, and in particular to a predictive dynamic distribution method for braking force based on multi-source sensor fusion. Background Technology

[0002] Electronic Brakeforce Distribution (EBD), a core technology in modern vehicle active safety systems, typically works in conjunction with Anti-lock Braking System (ABS). Its core function is to dynamically adjust the braking force distribution ratio between the front and rear axles and between the left and right wheels by monitoring key parameters such as wheel slip and vehicle load distribution in real time. This technology effectively avoids the risk of vehicle skidding and instability caused by premature wheel lock-up due to excessive braking force, while maximizing the road grip potential of each tire. It significantly shortens braking distances in complex scenarios such as wet and slippery roads and low-friction surfaces, making it one of the key technologies for ensuring vehicle braking safety and efficiency.

[0003] However, in the field of dynamic braking force distribution, the current mainstream hydraulic EBD system still has significant technical shortcomings, specifically as follows: 1. Inherent Delay Issues in Hydraulic Systems: Traditional hydraulic EBD systems rely on solenoid valves to control brake fluid pressure regulation. From the moment the wheel speed sensor detects wheel slippage to the moment the solenoid valve activates and completes the brake force reduction action, there is an inherent delay of 80-120ms (the delay duration is affected by environmental factors such as solenoid valve response speed, brake fluid viscosity, and temperature). Within this delay window, the tire slip ratio is prone to exceeding the optimal stable range (the conventional optimal slip ratio is 15%-20%), thus triggering premature intervention of the ABS to reduce pressure. This not only interrupts the continuous transmission of braking force but also prevents the braking system from continuously maintaining maximum effective braking force, ultimately resulting in a decrease in overall braking efficiency.

[0004] 2. Mechanical limitations of fixed proportional valves: Hydraulic EBD systems generally use mechanical proportional valves with a preset fixed front-to-rear axle braking force distribution ratio (the conventional preset value is approximately 60:40, i.e., the front axle bears 60% of the braking force and the rear axle bears 40%). This ratio cannot be dynamically adjusted according to real-time changes in vehicle load (such as switching between full-load and empty vehicles, uneven distribution of passengers or cargo, etc.). This fixed distribution mode leads to two problems: First, when empty, the rear axle load is low, and the fixed braking force ratio can easily cause the rear wheels to lock up; second, when fully loaded, the rear axle load increases, and the fixed ratio cannot fully utilize the rear axle adhesion, resulting in a waste of rear wheel braking force potential and ultimately increasing the vehicle's braking distance.

[0005] 3. Lack of risk prediction capability: The control logic of existing hydraulic EBD systems is based on real-time feedback signals from wheel speed sensors, which is a lagging control mode of "correcting after detecting deviation". It cannot predict braking risks by combining vehicle dynamics (such as steering angle, lateral acceleration, and body roll tendency). Taking the cornering braking scenario as an example: When the vehicle is turning, the inner wheel's contact adhesion decreases significantly due to the transfer of vehicle load. At this time, the braking force of the inner wheel needs to be reduced in advance to avoid lock-up; however, traditional systems can only initiate the decompression action after detecting the slip signal of the inner wheel. The lagging response can easily cause the inner wheel to lock up briefly, resulting in understeer or deviation of the braking trajectory, which seriously affects the safety of cornering braking.

[0006] Compared to hydraulic EBD systems, electromechanical braking (EMB) systems, by directly driving the brake actuator via an electric motor, eliminate hydraulic lines and brake fluid components, fundamentally eliminating hydraulic delay issues and possessing a natural technological advantage in terms of rapid braking force response and dynamic distribution. However, current EMB systems still have the following key shortcomings in their braking force distribution schemes:

[0007] 1. Limited sensing dimension: The existing EMB system's braking force distribution relies solely on basic signals such as wheel speed and vehicle deceleration for calculation, resulting in the braking force distribution being unable to accurately match the vehicle's dynamic operating conditions and prone to distribution deviations.

[0008] 2. Failure to fully leverage the advantages of electromechanical technology: The current EMB system still uses the "post-event correction" control logic of the hydraulic EBD system, that is, it only makes adjustments after detecting a deviation in braking force distribution, failing to achieve a "predictive and proactive distribution" control mode based on the vehicle's dynamic state. This means that although the EMB system has a millisecond-level response speed, it cannot translate its rapid response capability into predictive safety enhancement, and fails to fully unleash its electromechanical integration technology potential. Summary of the Invention

[0009] The purpose of this invention is to provide a predictive braking force dynamic allocation method based on multi-source sensor fusion to solve the above-mentioned technical problems.

[0010] To achieve the above objectives, this invention provides a predictive braking force dynamic allocation method based on multi-source sensor fusion, comprising the following steps: S1. Multi-source sensor data acquisition and preprocessing: Simultaneously acquire vehicle status data and road conditions; S2. Based on the vehicle status data and road conditions collected in step S1, the braking force demand is identified in advance through feedforward logic, and the feedforward braking torque is determined. S3. Determine the feedback braking torque based on the real-time braking status using a PID algorithm; S4. By dynamically weighting and integrating feedforward braking torque and feedback braking torque, a unified predictive synthetic braking torque is generated for all four wheels.

[0011] Preferably, the vehicle status data mentioned in step S1 includes suspension height. angular acceleration pedal travel and vehicle acceleration ; Road surface condition includes road surface adhesion coefficient .

[0012] Preferably, step S2 specifically includes the following steps: S21, Based on steering angle acceleration and pedal travel Determine whether the feedforward activation condition is met. If so, proceed to step S22; otherwise, do nothing. and These are the pedal travel threshold and the steering angle acceleration threshold, respectively. , ; S22, Based on suspension height and vehicle acceleration Calculate the load transfer rate

[0013] ; In the formula, , , and These represent the load transfer rates of the left front wheel, left rear wheel, right front wheel, and right rear wheel, respectively. Indicates time; Indicates the effective mass carried by the vehicle; Indicates the vehicle's wheelbase; Indicates the longitudinal acceleration of the vehicle; and These represent the distances from the center of mass to the front and rear axles, respectively. Indicates the wheelbase of the vehicle; S23. Calculate feedforward braking torque based on load transfer rate. : ; Constraints: ; In the formula, and These represent pavement abrupt change sensitivity and load migration compensation intensity, respectively. Represents a linear mapping function; Indicates the total torque demand; .

[0014] Preferably, step S3 specifically includes the following steps: S31. Calculate the target value of slip ratio. and actual slip ratio Deviation between : ; in, ; ; In the formula, Indicates the lateral acceleration of the vehicle; Indicates the speed of the entire vehicle; Indicates wheel speed; Indicates the rolling radius of the wheel; S32, Based on deviation Calculate feedback braking torque : ; In the formula, Indicates the proportional gain coefficient; Indicates the integral gain coefficient; This represents the differential gain coefficient.

[0015] Preferably, the predictive synthetic braking torque described in step S4 The calculation formula is as follows: ; In the formula, This represents the stage-specific weighting coefficient; and .

[0016] Preferably, in step S4, when At the same time, compensate for and correct the torque of the inner wheel and the torque of the outer wheel: ; ; in, ; ; In the formula, and These represent the inner wheel torque and outer wheel torque after compensation and correction, respectively; and These represent the inner wheel torque and outer wheel torque before compensation correction, respectively. .

[0017] Therefore, the present invention employs the above-mentioned predictive braking force dynamic allocation method based on multi-source sensor fusion, which has the following beneficial effects: 1. Shorten braking distance and improve braking efficiency: Adopting a predictive braking force dynamic distribution logic, relying on the EMB system to eliminate hydraulic delay, and combining multi-source sensor fusion (suspension height change rate, millimeter-wave radar road surface adhesion data) to adapt to load changes in real time, without the mechanical limitations of a fixed proportional valve, it can maximize the use of tire adhesion, enabling the vehicle to quickly reach the maximum deceleration, avoiding the problem of insufficient utilization of rear wheel braking force potential, and effectively shortening the braking distance; 2. Improve cornering braking stability and avoid trajectory deviation: Predict the loss of adhesion of the inner wheel in the corner by using steering angle acceleration, and implement the reduction of braking force of the inner wheel and the pressure increase compensation of the outer wheel in advance. Adaptively relax the cornering slip tolerance, avoid ABS intervention too early, and prevent the vehicle from deviating from its trajectory when braking in a corner. 3. Improve braking accuracy and system robustness: The system adopts a load-adaptive dual-loop (feedforward and feedback) collaborative control. The feedforward loop predicts the demand based on the road surface adhesion gradient and load mobility and outputs the feedforward braking torque. The feedback loop outputs the feedback braking torque based on the actual slip ratio deviation using a PID algorithm. The dual-loop collaboration accurately realizes the dynamic distribution of braking force, which can adapt to different road surface adhesion conditions and load changes, and improve the system's anti-interference ability and robustness.

[0018] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0019] Figure 1 This is a flowchart of a predictive braking force dynamic allocation method based on multi-source sensor fusion according to the present invention. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are merely illustrative of the embodiments of the present invention and are not intended to limit the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of this application. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout.

[0021] It should be noted that the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion, such as a process, method, system, product, or server that includes a series of steps or units, not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such process, method, product, or device.

[0022] The embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0023] like Figure 1 As shown, a predictive braking force dynamic allocation method based on multi-source sensor fusion includes the following steps: S1. Multi-source sensor data acquisition and preprocessing: Simultaneously acquire vehicle status data and road conditions; The vehicle status data mentioned in step S1 includes suspension height. angular acceleration pedal travel and vehicle acceleration ; Road surface condition includes road surface adhesion coefficient .

[0024] S2. Based on the vehicle status data and road conditions collected in step S1, the braking force demand is identified in advance through feedforward logic, and the feedforward braking torque is determined. Step S2 specifically includes the following steps: S21, Based on steering angle acceleration and pedal travel Determine whether the feedforward activation condition is met. If so, proceed to step S22; otherwise, do nothing. and These are the pedal travel threshold and the steering angle acceleration threshold, respectively. , ; S22, Based on suspension height and vehicle acceleration Calculate the load transfer rate

[0025] ; In the formula, , , and These represent the load transfer rates of the left front wheel, left rear wheel, right front wheel, and right rear wheel, respectively. Indicates time; Indicates the effective mass carried by the vehicle; Indicates the vehicle's wheelbase; Indicates the longitudinal acceleration of the vehicle; and These represent the distances from the center of mass to the front and rear axles, respectively. Indicates the wheelbase of the vehicle; S23. Calculate feedforward braking torque based on load transfer rate. : ; Constraints: ; In the formula, and These represent pavement abrupt change sensitivity and load migration compensation intensity, respectively. Represents a linear mapping function; Indicates the total torque demand; , , ,or .

[0026] S3. Determine the feedback braking torque based on the real-time braking status using a PID algorithm; Step S3 specifically includes the following steps: S31. Calculate the target value of slip ratio. and actual slip ratio Deviation between : ; in, ; ; In the formula, Indicates the lateral acceleration of the vehicle; Indicates the speed of the entire vehicle; Indicates wheel speed; Indicates the rolling radius of the wheel; S32, Based on deviation Calculate feedback braking torque : ; In the formula, Indicates the proportional gain coefficient; Indicates the integral gain coefficient; This represents the differential gain coefficient.

[0027] S4. By dynamically weighting and integrating feedforward braking torque and feedback braking torque, a unified predictive synthetic braking torque is generated for all four wheels.

[0028] The predictive synthetic braking torque described in step S4 The calculation formula is as follows: ; In the formula, This represents the stage-specific weighting coefficient; and .

[0029] Preferably, in step S4, when At the same time, compensate for and correct the torque of the inner wheel and the torque of the outer wheel: ; ; in, ; ; In the formula, and These represent the inner wheel torque and outer wheel torque after compensation and correction, respectively; and These represent the inner wheel torque and outer wheel torque before compensation correction, respectively. .

[0030] Finally, 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 preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the technical solutions of the present invention, and these modifications or equivalent substitutions cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A predictive braking force dynamic allocation method based on multi-source sensor fusion, characterized in that: Includes the following steps: S1. Multi-source sensor data acquisition and preprocessing: Simultaneously acquire vehicle status data and road conditions; S2. Based on the vehicle status data and road conditions collected in step S1, the braking force demand is identified in advance through feedforward logic, and the feedforward braking torque is determined. S3. Determine the feedback braking torque based on the real-time braking status using a PID algorithm; S4. By dynamically weighting and integrating feedforward braking torque and feedback braking torque, a unified predictive synthetic braking torque is generated for all four wheels.

2. The predictive braking force dynamic allocation method based on multi-source sensor fusion according to claim 1, characterized in that: The vehicle status data mentioned in step S1 includes suspension height. angular acceleration pedal travel and vehicle acceleration ; Road surface conditions include road surface adhesion coefficient .

3. The predictive braking force dynamic allocation method based on multi-source sensor fusion according to claim 2, characterized in that: Step S2 specifically includes the following steps: S21, Based on steering angle acceleration and pedal travel Determine whether the feedforward activation condition is met. If so, proceed to step S22; otherwise, do nothing. and These are the pedal travel threshold and the steering angle acceleration threshold, respectively. , ; S22, Based on suspension height and vehicle acceleration Calculate the load transfer rate: ； In the formula, , , and These represent the load transfer rates of the left front wheel, left rear wheel, right front wheel, and right rear wheel, respectively. Indicates time; Indicates the effective mass carried by the vehicle; Indicates the vehicle's wheelbase; Indicates the longitudinal acceleration of the vehicle; and These represent the distances from the center of mass to the front and rear axles, respectively. Indicates the wheelbase of the vehicle; S23. Calculate feedforward braking torque based on load transfer rate. : ； Constraints: ; In the formula, and These represent pavement abrupt change sensitivity and load migration compensation intensity, respectively. Represents a linear mapping function; Indicates the total torque demand; .

4. The predictive braking force dynamic allocation method based on multi-source sensor fusion according to claim 3, characterized in that: Step S3 specifically includes the following steps: S31. Calculate the target value of slip ratio. and actual slip ratio Deviation between : ； in, ； ； In the formula, Indicates the lateral acceleration of the vehicle; Indicates the speed of the entire vehicle; Indicates wheel speed; Indicates the rolling radius of the wheel; S32, Based on deviation Calculate feedback braking torque : ； In the formula, Indicates the proportional gain coefficient; Indicates the integral gain coefficient; This represents the differential gain coefficient.

5. The predictive braking force dynamic allocation method based on multi-source sensor fusion according to claim 4, characterized in that: The predictive synthetic braking torque described in step S4 The calculation formula is as follows: ； In the formula, This represents the stage-specific weighting coefficient; and .

6. The predictive braking force dynamic allocation method based on multi-source sensor fusion according to claim 5, characterized in that: In step S4, when At the same time, compensate for and correct the torque of the inner wheel and the torque of the outer wheel: ； ； in, ； ； In the formula, and These represent the inner wheel torque and outer wheel torque after compensation and correction, respectively; and These represent the inner wheel torque and outer wheel torque before compensation correction, respectively. .

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