A multi-working-condition-oriented vehicle ABS parameter optimization system and method

Abstract

The application discloses a kind of multi-working-condition-oriented vehicle ABS parameter optimization system and method, the method of the application includes obtaining vehicle and road condition information using fusion algorithm and each sensor information;ABS parameter and double-agent strategy network are initialized;According to vehicle and road condition information, road surface type is identified;Double-agent outputs the influence factor of wheel target slip ratio and the parameter of slip ratio tracking controller, slip ratio tracking controller calculates and issues target braking force, and executor executes control instruction;The reward of double-agent action is calculated;According to reward and the real-time data obtained, double-agent strategy network is optimized.The application can adaptively adjust ABS control parameters according to multiple working conditions, improve braking performance, shorten braking distance, and effectively avoid vehicle out of control or wheel lock phenomenon.

Description

Technical Field

[0001] The present invention relates to the field of vehicle braking technology, and in particular to a vehicle ABS parameter optimization system and method for multiple working conditions. Background Art

[0002] Vehicle anti-lock braking systems (ABS) prevent wheel lock by controlling slip, ensuring braking stability and directional control. However, existing ABS controller parameters are manually calibrated and then recalibrated, failing to fully consider the impact of varying road surface types and braking conditions on system performance. This approach suffers from the following drawbacks:

[0003] (1) The target slip rate cannot change with the road surface type: The road adhesion coefficient and wheel state under different road surface types (such as dry road surface, wet road surface, and icy and snowy road surface) vary significantly. The optimal target slip rate should change dynamically with the change of road surface type.

[0004] (2) Controller parameters cannot adapt to changes in operating conditions: Under different operating conditions, the response speed of the controller with fixed parameters to the slip rate varies. The response lag under certain conditions may lead to increased braking distance or vehicle loss of control.

[0005] (3) Manual calibration is time-consuming: Traditional ABS controller parameters require a large amount of experimental data for parameter calibration, which results in a long debugging cycle and poor adaptability.

[0006] Therefore, there is an urgent need to develop a technology that can dynamically optimize ABS parameters according to different working conditions, so that the ABS system has adaptive capabilities and performs well under different working conditions. Summary of the Invention

[0007] The present invention aims to solve one of the technical problems in the related art at least to a certain extent.

[0008] To this end, the present invention proposes a vehicle ABS parameter optimization system for multiple working conditions, which can dynamically optimize system parameters according to the working conditions and improve the braking performance of the vehicle under different road conditions.

[0009] Another object of the present invention is to provide a vehicle ABS parameter optimization method for multiple working conditions.

[0010] To achieve the above-mentioned purpose, the present invention proposes a vehicle ABS parameter optimization system for multiple working conditions, including an information acquisition module, a road type recognition module, a slip rate tracking module, a parameter optimization module and an execution module; wherein,

[0011] The information acquisition module is used to process the measured signal and send it to the road type identification module, the slip rate tracking module and the parameter optimization module;

[0012] The road surface type identification module is used to identify the road surface type information based on the combined sensing information sent by the information acquisition module and send it to the parameter optimization module;

[0013] The parameter optimization module is used to calculate the braking distance and slip rate tracking error based on the road type information sent by the road type recognition module and the combined sensor information sent by the information acquisition module, and use them as feedback on the vehicle's braking performance, and calculate the target slip rate influencing factor and controller parameters;

[0014] The slip rate tracking module is used to calculate the actual slip rate based on the combined sensor information sent by the information acquisition module, and calculate the required target braking force based on the target slip rate influencing factor and controller parameters sent by the parameter optimization module, and send it to the execution module;

[0015] The execution module is configured to receive and execute the target braking force command sent by the slip rate tracking module;

[0016] The parameter optimization module is further used to obtain the optimal values ​​of the target slip rate influencing factor and the controller parameters through iterative training.

[0017] The multi-operating-condition vehicle ABS parameter optimization system according to the embodiment of the present invention may also have the following additional technical features:

[0018] In one embodiment of the present invention, the information acquisition module includes multiple types of cameras, radars, inertial measurement units, wheel speed sensors, wheel steering angle sensors, tire noise sensors, and tire pressure sensors.

[0019] To achieve the above-mentioned object, the present invention further proposes a vehicle ABS parameter optimization method for multiple working conditions, comprising:

[0020] The combined sensor information is filtered and fused to obtain vehicle dynamic parameters, and the actual longitudinal speed, slip rate and utilized adhesion coefficient of each wheel are calculated based on the vehicle dynamic parameters;

[0021] Use fusion algorithms and sensor information to obtain vehicle and road condition information;

[0022] Initialize ABS parameters and dual-agent strategy network;

[0023] Identify road surface type based on vehicle and road condition information;

[0024] Based on the road surface type, the dual agents output the influencing factors of the wheel target slip rate and the parameters of the slip rate tracking controller. The slip rate tracking controller calculates and issues the target braking force, and the actuator executes the control command.

[0025] Calculate rewards for dual-agent actions;

[0026] Optimize the two-agent policy network based on rewards and acquired real-time data.

[0027] The multi-operating-condition vehicle ABS parameter optimization system and method of the embodiment of the present invention utilizes operating-condition identification and artificial intelligence algorithms to adjust the target slip ratio and optimize the ABS controller parameters.

[0028] Additional aspects and advantages of the present invention will be set forth in part in the description which follows and, in part, will be obvious from the description which follows, or may be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0030] Figure 1 2. It is a schematic structural diagram of a vehicle ABS parameter optimization system for multiple working conditions according to an embodiment of the present invention;

[0031] Figure 2 4 is a flow chart of a vehicle ABS parameter optimization method for multiple working conditions according to an embodiment of the present invention. DETAILED DESCRIPTION

[0032] It should be noted that, in the absence of conflict, the embodiments and features of the embodiments of the present invention can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0033] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0034] The following describes a vehicle ABS parameter optimization system and method for multiple working conditions proposed in accordance with an embodiment of the present invention with reference to the accompanying drawings.

[0035] Figure 1 A vehicle ABS parameter optimization system for multiple working conditions according to an embodiment of the present invention is provided. Figure 1 As shown, it includes an information acquisition module, a road type identification module, a slip rate tracking module, a parameter optimization module and an execution module; wherein,

[0036] The information acquisition module includes a camera, radar, inertial measurement unit, wheel speed sensor, wheel steering angle sensor, tire noise sensor, tire pressure sensor, etc. The measured signals are processed and then transmitted to the road type recognition module, slip rate tracking module and parameter optimization module.

[0037] The road surface type identification module receives the combined sensing information transmitted by the information acquisition module, identifies the road surface type, and transmits it to the parameter optimization module.

[0038] The parameter optimization module receives road surface type information transmitted by the road surface type recognition module and the combined sensor information transmitted by the information acquisition module, calculates the braking distance and slip rate tracking error, and uses them as feedback on the vehicle braking performance. Through iterative training, it obtains the optimal values ​​of the target slip rate influencing factor and controller parameters, and transmits them to the slip rate tracking module.

[0039] The slip rate tracking module receives the combined sensor information transmitted by the information acquisition module, calculates the actual slip rate, receives the target slip rate influencing factor and controller parameters transmitted by the parameter optimization module, calculates the required target braking force, and transmits it to the execution module.

[0040] The execution module is a braking system that receives and executes the target braking force command issued by the slip rate tracking module.

[0041] The multi-operating-condition vehicle ABS parameter optimization system according to an embodiment of the present invention utilizes operating-condition identification and artificial intelligence algorithms to adjust the target slip ratio and optimize ABS controller parameters. This method adaptively adjusts ABS control parameters based on various operating conditions, improving braking performance, shortening braking distance, and effectively preventing vehicle loss of control or wheel locking.

[0042] In order to implement the above embodiment, Figure 2 As shown, this embodiment also provides a vehicle ABS parameter optimization method for multiple working conditions, including:

[0043] S1, obtains vehicle and road condition information using fusion algorithms and sensor information;

[0044] S2, initialize ABS parameters and dual-agent strategy network;

[0045] S3, identifying the road type based on vehicle and road condition information;

[0046] S4, based on the road surface type, the dual agent outputs the influencing factor of the wheel target slip rate and the parameters of the slip rate tracking controller, the slip rate tracking controller calculates and issues the target braking force, and the actuator executes the control instruction;

[0047] S5, calculates the rewards of the dual-agent actions;

[0048] S6, optimizes the dual-agent policy network based on rewards and acquired real-time data.

[0049] In one embodiment of the present invention, the fusion algorithm and the information from each sensor are used to obtain vehicle and road condition information, including vehicle speed v, wheel speed n, wheel steering angle δ, vehicle tire noise characteristic Δ, tire pressure p, wheel road adhesion coefficient Etc. And calculate the wheel slip rate s.

[0050] s=(v-nr) / v

[0051] Where r is the wheel radius.

[0052] In one embodiment of the present invention, initial values ​​of the factors affecting the target wheel slip rate, such as the wheel longitudinal speed, steering angle, road adhesion coefficient, and road type, are randomly given. The initial value of the target wheel slip rate is calculated according to the following formula and is limited to between 0.1 and 0.3.

[0053]

[0054] Where o is the target wheel slip rate; λ, ε, and η are the factors affecting the target wheel slip rate, namely the wheel longitudinal speed, steering angle, and road adhesion coefficient; ξ j is the influence factor of road surface type on the target wheel slip rate, and j is the code of the road surface type.

[0055] The PID algorithm is used as the slip tracking controller, and the controller parameter k is randomly given. P 、k I 、k D The initial value of .

[0056] Randomly initialize the policy networks of the first and second agents.

[0057] Among them, the first intelligent agent is used to optimize the influencing factor of the target slip rate.

[0058] The second agent is used to optimize the parameters of the slip tracking controller.

[0059] Among them, the state space of the first agent is defined as The action space is defined as {λ,ε,η,ξ j}, the reward function is designed as {-a1L-a2[(o>0.5)|(o<0)]}. Among them, v0 is the initial braking velocity; L is the braking distance; a1 is the braking distance penalty function; and a2 is the penalty function for the target slip rate exceeding the normal range.

[0060] Among them, the state space of the second agent is defined as The action space is defined as {k P ,k I ,k D}, the reward function is designed as {-b1|Δd|-b2ΔF b Where d is the slip tracking error; Δd is the rate of change of the slip tracking error; F b is the wheel braking force; ΔF b is the rate of change of the wheel braking force; b1 is the tracking error penalty function; b2 is the frequent adjustment penalty coefficient.

[0061] Among them, the first agent and the second agent both include but are not limited to the Deep Deterministic Policy Gradient (DDPG) algorithm, the Twin Delayed DDPG (TD3) algorithm, and the Soft Actor-Critic (SAC) algorithm.

[0062] In one embodiment of the present invention, when the vehicle speed and tire pressure are constant, the tire noise characteristics of the vehicle when traveling on different road surfaces are different. The road type is identified based on the vehicle speed, tire pressure and the tire noise characteristics.

[0063] The vehicle tire noise characteristics include but are not limited to main sound frequency and decibels.

[0064] Among them, road surface types include but are not limited to dry roads, wet roads, icy and snowy roads, and gravel roads.

[0065] In one embodiment of the present invention, the dual agent selects an action (i.e., outputs the influencing factor of the target wheel slip rate and the parameters of the slip rate tracking controller), the slip rate tracking controller calculates and issues the target braking force, and the actuator executes the control instruction, including:

[0066] Real-time acquisition of the road type, initial braking speed, braking distance, slip tracking error d, slip tracking error change rate Δd, and wheel braking force change rate ΔF during the current braking process b And other data.

[0067] The first agent is based on the current state Select action {λ,ε,η,ξ j}(i.e. adjust and output λ, ε, η, ξ j ), and then calculate the target wheel slip rate.

[0068] The second agent is based on the current state Select action {k P ,k I ,k D}(i.e. adjust and output k P、k I 、k D ).

[0069] The slip rate tracking controller calculates and issues the target braking force based on the target wheel slip rate, controller parameters and the current actual slip rate. The actuator executes the control command and the vehicle enters the braking process.

[0070] In one embodiment of the present invention, the reward of an action is calculated:

[0071] Calculate the reward for the first agent’s action {-a1L-a2[(o>0.5)|(o<0)]}.

[0072] Calculate the reward for the second agent's action {-b1|Δd|-b2ΔF b}.

[0073] In one embodiment of the present invention, the policy network is updated based on the rewards and acquired real-time data to optimize the action selection strategy.

[0074] In one embodiment of the present invention, steps S3 through S6 are repeated until the reward function gradually converges, at which point training is terminated and the optimal values ​​of the wheel target slip rate influencing factor and the slip rate tracking controller parameters are obtained. When the vehicle enters a new operating condition, or when braking performance becomes unstable, or when ABS parameters have not been updated for a certain period of time, retraining is performed, repeating steps S3 through S6 until braking performance stabilizes, and the optimal values ​​of the wheel target slip rate influencing factor and the slip rate tracking controller parameters are updated.

[0075] In summary, in recent years, vehicles have been rapidly developing towards electrification, intelligence, and unmanned driving. With the rapid advancement of drive-by-wire chassis and autonomous driving technologies, the uncertainty of vehicle driving conditions has greatly increased. As the carrier of higher-level technologies such as autonomous driving, the vehicle chassis's adaptability to dynamic environments is becoming increasingly important, especially for braking safety under complex road conditions. Implementing this multi-condition ABS parameter optimization method can significantly improve vehicle braking performance under various conditions, providing crucial safety protection for both occupants and the vehicle itself, especially in extreme conditions such as emergency braking and on slippery, icy, or snowy roads.

[0076] The multi-operating-condition vehicle ABS parameter optimization method according to an embodiment of the present invention optimizes the parameters of an intelligent vehicle anti-lock braking system (ABS) for various road types, including icy, slippery, and dry roads, and for multiple driving conditions, including forward, reverse, and steering. This method utilizes operating-condition identification and artificial intelligence algorithms to adjust the target slip ratio and optimize ABS controller parameters. This method adaptively adjusts ABS control parameters based on various operating conditions, improving braking performance, shortening braking distance, and effectively preventing vehicle loss of control or wheel locking.

[0077] In the description of this specification, the reference terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" mean that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art can combine and combine different embodiments or examples described in this specification and features of different embodiments or examples without contradiction.

[0078] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of the technical features being referred to. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present invention, "plurality" means at least two, such as two, three, etc., unless otherwise specifically defined.

Claims

1. A vehicle ABS parameter optimization system for multiple working conditions, characterized by: It includes information collection module, road type identification module, slip rate tracking module, parameter optimization module and execution module; among them, The information acquisition module is used to process the measured signal and send it to the road type identification module, the slip rate tracking module and the parameter optimization module; The road surface type identification module is used to identify the road surface type information based on the combined sensing information sent by the information acquisition module and send it to the parameter optimization module; The parameter optimization module is used to calculate the braking distance and slip rate tracking error based on the road type information sent by the road type recognition module and the combined sensor information sent by the information acquisition module, and use them as feedback on the vehicle's braking performance, and calculate the target slip rate influencing factor and controller parameters; The slip rate tracking module is used to calculate the actual slip rate based on the combined sensor information sent by the information acquisition module, and calculate the required target braking force based on the target slip rate influencing factor and controller parameters sent by the parameter optimization module, and send it to the execution module; The execution module is configured to receive and execute the target braking force command sent by the slip rate tracking module; The parameter optimization module is further used to obtain the optimal values ​​of the target slip rate influencing factor and the controller parameters through iterative training.

2. The system according to claim 1, wherein: The information acquisition module includes multiple types of cameras, radars, inertial measurement units, wheel speed sensors, wheel steering angle sensors, tire noise sensors, and tire pressure sensors.

3. A vehicle ABS parameter optimization method for multiple working conditions applied to the system according to any one of claims 1 and 2, characterized in that: include: Use fusion algorithms and sensor information to obtain vehicle and road condition information; Initialize ABS parameters and dual-agent strategy network; Identify road surface type based on vehicle and road condition information; Based on the road surface type, the dual agents output the influencing factors of the wheel target slip rate and the parameters of the slip rate tracking controller. The slip rate tracking controller calculates and issues the target braking force, and the actuator executes the control command. Calculate rewards for dual-agent actions; Optimize the two-agent policy network based on rewards and acquired real-time data.

4. The method according to claim 3, characterized in that Vehicle and road condition information, including: vehicle speed v , wheel speed n , wheel steering angle δ , vehicle tire noise characteristics Δ, tire pressure p , wheel-road adhesion coefficient φ ; And calculate the wheel slip rate s : Where, r is the wheel radius.

5. The method according to claim 4, characterized in that Initialize ABS parameters and the dual-agent policy network, including: Randomly give the initial values ​​of the factors affecting the wheel target slip rate, such as wheel longitudinal speed, steering angle, road adhesion coefficient, and road type, and calculate the initial value of the wheel target slip rate according to the following formula: Where, o is the target slip rate of the wheel; λ 、 ε 、 η are the factors affecting the target wheel slip rate, namely the wheel longitudinal speed, steering angle and road adhesion coefficient; ξ j is the influence factor of road surface type on the target wheel slip rate, j is the code for the road surface type; The PID algorithm is used as the slip tracking controller, and the controller parameters are randomly given. k P 、 k I 、 k D The initial value of The policy networks of the first agent and the second agent are randomly initialized; wherein the first agent is used to optimize the influencing factor of the target slip rate; and the second agent is used to optimize the parameters of the slip rate tracking controller.

6. The method according to claim 5, characterized in that The state space of the first agent is defined as { v , δ , φ , j , v 0, o }, the action space is defined as { λ , ε , η, ξ j }, the reward function is designed as {- a 1 L - a 2[( o >0.5)|( o <0)]}; where v 0 is the initial braking speed; L is the braking distance; a 1 is the braking distance penalty function; a 2 is the penalty function when the target slip rate exceeds the normal range.

7. The method according to claim 5, characterized in that The state space of the second agent is defined as { v , δ , φ , j , d, Δ d , F b }, the action space is defined as { k P , k I , k D }, the reward function is designed as {- b 1|Δ d |- b 2Δ F b }; in, d is the slip rate tracking error; Δ d is the rate of change of slip tracking error; F b is the wheel braking force; Δ F b is the rate of change of wheel braking force; b 1 is the tracking error penalty function; b 2 means frequent adjustment of penalty coefficient.

8. The method according to claim 7, characterized in that The dual agents output the influencing factors of the target wheel slip rate and the parameters of the slip rate tracking controller. The slip rate tracking controller calculates and issues the target braking force, and the actuator executes the control instructions, including: Real-time acquisition of the road type, initial braking speed, braking distance, and slip rate tracking error during the current braking process d , the rate of change of slip tracking error Δ d , the rate of change of wheel braking force Δ F b ; The first agent is based on the current state { v , δ , φ , j , v 0, o }, select action { λ , ε , η, ξ j }, and calculate the target wheel slip rate; The second agent is based on the current state { v , δ , φ , j , d, Δ d , F b }, select action { k P , k I , k D }; The slip rate tracking controller calculates and issues the target braking force based on the target wheel slip rate, controller parameters and the current actual slip rate. The actuator executes the control command and the vehicle enters the braking process.

9. The method according to claim 8, characterized in that Calculate the rewards for the dual-agent actions, including: Calculate the reward for the first agent's action {- a 1 L - a 2[( o >0.5)|( o <0)]}; Calculate the reward for the second agent's action {- b 1|Δ d |- b 2Δ F b }.

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