A pre-judgment type brake force dynamic distribution method based on multi-source sensing fusion

Abstract

The application discloses a kind of pre-judgment type brake force dynamic distribution methods based on multi-source sensing fusion, belong to vehicle braking field, comprising the following steps: S1, multi-source sensing data acquisition and pre-processing: synchronous acquisition vehicle state data and road condition;S2, based on the vehicle state data and road condition of step S1 acquisition, brake force demand is identified in advance by feedforward logic, determine feedforward braking torque;S3, feedback braking torque is determined using PID algorithm based on real-time braking state;S4, by dynamic weight distribution fusion feedforward braking torque and feedback braking torque, generates four-wheel unified pre-judgment type combined braking force torque.The above-mentioned one kind is based on multi-source sensing fusion pre-judgment type brake force dynamic distribution method, and real-time perception layer is constructed by multi-source sensing fusion, and feedforward-feedback double-loop controller and bend pre-execution mechanism are designed, efficient and accurate dynamic distribution of brake force is realized, and vehicle braking performance and stability are significantly improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of vehicle braking technology, and in particular to a pre-judgment type brake force dynamic distribution method based on multi-source sensing fusion. BACKGROUND

[0002] Electronic brake force distribution (EBD) as the core technology of modern vehicle active safety system, usually works with anti-lock braking system (ABS), its core function is to dynamically adjust the brake force distribution ratio of front and rear axle and left and right wheel by monitoring the wheel slip state, vehicle load distribution and other key parameters in real time. Using the above technology can not only effectively avoid the risk of vehicle spinout and instability caused by excessive brake force of rear wheel, but also maximize the potential of ground adhesion of each tire, significantly shorten the braking distance in complex scenarios such as wet road and low adhesion coefficient road, which is one of the key technologies to ensure the safety and efficiency of vehicle braking.

[0003] However, in the field of brake force dynamic distribution, the current mainstream hydraulic EBD system still has significant technical defects, which are as follows:

[0004] 1. Inherent delay problem of hydraulic system: The traditional hydraulic EBD system relies on electromagnetic valve to control brake hydraulic pressure regulation. From the detection of wheel slip signal by wheel speed sensor to the start and completion of brake force decompression action by electromagnetic valve, there is an inherent delay of 80-120 ms (the delay time is affected by the response speed of electromagnetic valve, brake fluid viscosity, temperature and other environmental factors). In this delay window, the tire slip rate is easy to break through the best stable interval (the conventional best slip rate is 15%-20%), and then trigger ABS to intervene for decompression adjustment too early - this not only interrupts the continuous transmission process of brake force, but also causes the brake system to be unable to continuously maintain the maximum effective brake force, ultimately resulting in a decrease in overall braking efficiency.

[0005] 2. Mechanical limitations of fixed ratio valve: Hydraulic EBD systems generally use mechanical proportional valves to preset a fixed front-rear axle brake force distribution ratio (the conventional preset value is about 60:40, i.e. the front axle bears 60% of the brake force and the rear axle bears 40%). This ratio cannot be dynamically adjusted according to the real-time load changes of the vehicle (such as vehicle full load / empty load switching, uneven distribution of passengers or cargo, etc.). This fixed distribution mode will cause two problems: one is that the low load of the rear axle will cause the fixed ratio brake force to easily cause the rear wheel to lock; the other is that the high load of the rear axle will cause the fixed ratio to be unable to fully utilize the adhesion of the rear axle, resulting in waste of the potential of the rear wheel brake force, and ultimately extending the braking distance of the vehicle.

[0006] 3. Lack of risk prediction ability: The control logic of the existing hydraulic EBD system is based on the real-time feedback signal of the wheel speed sensor, which belongs to the lag control mode of "correcting after detecting deviation", and cannot predict braking risks in combination with the dynamic state of the vehicle (such as steering angle, lateral acceleration, and body roll tendency). For example, in a curve braking scenario: when the vehicle is turning, the ground adhesion of the inner wheel decreases significantly due to the load transfer of the vehicle body, and the braking force of the inner wheel needs to be reduced in advance to avoid locking; but the traditional system can only start the pressure reduction action after detecting the inner wheel slip signal, and the lag response can cause the inner wheel to lock temporarily, causing the vehicle to turn insufficiently or the braking trajectory to deviate, seriously affecting the safety of curve braking.

[0007] Compared with the hydraulic EBD system, the electronic mechanical brake (EMB) system directly drives the brake actuator through the motor, eliminates the hydraulic pipeline, brake fluid and other components, and fundamentally eliminates the hydraulic delay problem, having a natural technical advantage in the field of brake force fast response and dynamic distribution. However, the current brake force distribution scheme of the EMB system still has the following key deficiencies:

[0008] 1. Single sensing dimension: The brake force distribution of the existing EMB system only relies on basic signals such as wheel speed and vehicle deceleration for calculation, resulting in inaccurate brake force distribution that can easily deviate from the vehicle dynamic condition.

[0009] 2. Not fully utilizing the advantages of electromechanical technology: The current EMB system still uses the "post-correction" control logic of the hydraulic EBD system, i.e. only adjusting after detecting the deviation of brake force distribution, and not realizing the "advance prediction, active distribution" control mode based on the dynamic state of the vehicle. This results in the EMB system having a response speed of milliseconds, but failing to convert the fast response capability into predictive safety improvement, and not fully utilizing the technical potential of electromechanical integration. SUMMARY

[0010] The purpose of the present application is to provide a multi-source sensor fusion-based predictive brake force dynamic distribution method to solve the above technical problems.

[0011] To achieve the above purpose, the present application provides a multi-source sensor fusion-based predictive brake force dynamic distribution method, comprising the following steps:

[0012] S1, multi-source sensor data acquisition and preprocessing: synchronously acquiring vehicle state data and road conditions;

[0013] S2, based on the vehicle state data and road conditions collected in step S1, the brake force demand is identified in advance through feedforward logic, and the feedforward brake torque is determined;

[0014] S3, the feedback brake torque is determined based on the real-time braking state using the PID algorithm;

[0015] 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.

[0016] Preferably, the vehicle status data mentioned in step S1 includes suspension height. angular acceleration pedal travel and vehicle acceleration ;

[0017] Road surface condition includes road surface adhesion coefficient .

[0018] Preferably, step S2 specifically includes the following steps:

[0019] 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. , ;

[0020] S22, Based on suspension height and vehicle acceleration Calculate the load transfer rate

[0021] ;

[0022] 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;

[0023] S23. Calculate feedforward braking torque based on load transfer rate. :

[0024] ;

[0025] Constraints: ;

[0026] 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; .

[0027] Preferably, step S3 specifically includes the following steps:

[0028] S31. Calculate the target value of slip ratio. and actual slip ratio Deviation between :

[0029] ;

[0030] in,

[0031] ;

[0032] ;

[0033] 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;

[0034] S32, Based on deviation Calculate feedback braking torque :

[0035] ;

[0036] In the formula, Indicates the proportional gain coefficient; Indicates the integral gain coefficient; This represents the differential gain coefficient.

[0037] Preferably, the predictive synthetic braking torque described in step S4 The calculation formula is as follows:

[0038] ;

[0039] In the formula, This represents the stage-specific weighting coefficient; and .

[0040] 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:

[0041] ;

[0042] ;

[0043] in,

[0044] ;

[0045] ;

[0046] 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. .

[0047] 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:

[0048] 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;

[0049] 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.

[0050] 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.

[0051] 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

[0052] 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

[0053] 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.

[0054] 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.

[0055] The embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0056] like Figure 1 As shown, a predictive braking force dynamic allocation method based on multi-source sensor fusion includes the following steps:

[0057] S1. Multi-source sensor data acquisition and preprocessing: Simultaneously acquire vehicle status data and road conditions;

[0058] The vehicle status data mentioned in step S1 includes suspension height. angular acceleration pedal travel and vehicle acceleration ;

[0059] Road surface condition includes road surface adhesion coefficient .

[0060] 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.

[0061] Step S2 specifically includes the following steps:

[0062] 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. , ;

[0063] S22, Based on suspension height and vehicle acceleration Calculate the load transfer rate

[0064] ;

[0065] 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;

[0066] S23. Calculate feedforward braking torque based on load transfer rate. :

[0067] ;

[0068] Constraints: ;

[0069] 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 .

[0070] S3. Determine the feedback braking torque based on the real-time braking status using a PID algorithm;

[0071] Step S3 specifically includes the following steps:

[0072] S31. Calculate the target value of slip ratio. and actual slip ratio Deviation between :

[0073] ;

[0074] in,

[0075] ;

[0076] ;

[0077] 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;

[0078] S32, Based on deviation Calculate feedback braking torque :

[0079] ;

[0080] In the formula, Indicates the proportional gain coefficient; Indicates the integral gain coefficient; This represents the differential gain coefficient.

[0081] 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.

[0082] The predictive synthetic braking torque described in step S4 The calculation formula is as follows:

[0083] ;

[0084] In the formula, This represents the stage-specific weighting coefficient; and .

[0085] 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:

[0086] ;

[0087] ;

[0088] in,

[0089] ;

[0090] ;

[0091] 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. .

[0092] 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 pre-judgment braking force dynamic distribution method based on multi-source sensing fusion, characterized in that: The method comprises the following steps: S1, multi-source sensing data acquisition and preprocessing: synchronously acquiring vehicle state data and road conditions; S2, based on the vehicle state 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, the feedback braking torque is determined by using a PID algorithm based on the real-time braking state; S4, the feedforward braking torque and the feedback braking torque are fused by dynamic weight distribution to generate a four-wheel unified predictive synthesis braking force torque; Step S2 specifically comprises the following steps: S21, based on the steering angle acceleration and the pedal stroke , determine whether the feedforward activation condition is satisfied , if yes, execute step S22, otherwise, do nothing; wherein, and are a pedal stroke threshold value and a steering angle acceleration threshold value, respectively, and , ; S22, based on suspension height and vehicle acceleration Calculate load transfer rate: ； wherein, , , and represent the load transfer rates of the front left wheel, the rear left wheel, the front right wheel, and the rear right wheel, respectively; represents time; represents the effective mass carried by the vehicle; represents the wheelbase of the vehicle; represents the longitudinal acceleration of the vehicle; and represent the distances from the center of mass to the front axle and the rear axle, respectively; represents the track of the vehicle; S23, calculate the feedforward braking torque based on the load transfer rate : ； Constraints: ; In the formula, and respectively represent the road surface abruptness sensitivity and the load transfer compensation strength; represents a linear mapping function; represents the total demand torque; .

2. The pre-judgment brake force dynamic distribution method based on multi-source sensing fusion according to claim 1, characterized in that: The vehicle state data described in step S1 includes suspension height , steering angle acceleration , pedal stroke , and vehicle acceleration ; The road surface condition includes a road surface adhesion coefficient .

3. The pre-anticipatory brake force dynamic distribution method based on multi-source sensing fusion according to claim 2, characterized in that: Step S3 specifically comprises the following steps: S31, calculate a target value of the slip ratio and a deviation between the target value of the slip ratio and an actual value of the slip ratio :​ ； Wherein, ； ； In the formula, represents the vehicle lateral acceleration; represents the vehicle speed; represents the wheel rotational speed; represents the wheel rolling radius; S32、based on the deviation calculating the feedback braking torque : ； wherein represents a proportional gain coefficient; represents an integral gain coefficient; represents a derivative gain coefficient.

4. The pre-estimation braking force dynamic distribution method based on multi-source sensing fusion according to claim 3, characterized in that: The pre-judgment synthetic braking force torque described in step S4 The calculation formula is as follows: ； In the formula, denotes a phase weight coefficient; and .

5. The pre-anticipatory brake force dynamic distribution method based on multi-source sensing fusion according to claim 4, characterized in that: In step S4, when the inside wheel torque and the outside wheel torque are compensated and corrected: ； ； Wherein, ； ； wherein and respectively represent the compensated corrected inside wheel torque and outside wheel torque; and respectively represent the compensated uncorrected inside wheel torque and outside wheel torque, and .

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