Brake Pressure Mapping for Wheel Slip Stability Control
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Solution Overview
Problem
Optimizing brake pressure control in vehicles to prevent unstable driving situations due to excessive wheel slip, particularly in adverse conditions like aquaplaning or wintry conditions, is complex and requires manual adjustment of numerous parameters by application engineers.
Innovation Solution
A method that uses a reinforcement learning approach to determine optimal brake pressure changes based on current wheel status parameters, generating a brake pressure characteristic map that adjusts brake pressure to maintain stability and minimize braking distance, allowing for automatic optimization of antilock controllers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual parameter adjustment by application engineers is used to optimize brake pressure control, then braking performance can be optimized for various vehicles, but the process becomes extremely complex and time-consuming
Solution Approach 1:
The antilock controller autonomously determines optimal brake pressure parameters by independently learning from driving data and evaluating measurement results, eliminating the need for manual parameter adjustment by application engineers. The controller performs self-optimization through automated data collection, analysis, and parameter determination across multiple driving cycles.
Solution Approach 2:
The manual mechanical adjustment process by application engineers is replaced with an automated electronic learning system. The controller uses algorithms to automatically analyze driving data, evaluate braking performance, and determine optimal parameters, substituting human expertise with automated intelligent processing.
2Productivity
If multiple ALC maneuvers are driven and evaluated manually to find optimal parameters, then braking performance can be enhanced, but the process requires repeating many times until target performance is achieved
Solution Approach 1:
The parameter optimization process continues automatically across multiple driving cycles without interruption. The controller continuously collects data, evaluates performance, and refines parameters in an ongoing manner, eliminating the need for repeated manual intervention and significantly reducing the time required to achieve target performance.
Solution Approach 2:
The controller performs preliminary data collection and analysis during normal driving operations before final parameter determination is needed. By continuously gathering and preprocessing driving data in advance, the system is prepared to quickly determine optimal parameters when required, reducing overall optimization time.
3Ease of operation
If the controller independently learns optimal brake pressure changes through reinforcement learning, then the parameter search process is simplified, but the controller must process and analyze大量 driving data
Solution Approach 1:
The data processing function is segmented and distributed across multiple components: sensors collect raw data, the controller processes and evaluates this data, and the learning algorithm analyzes results to determine parameters. This segmentation simplifies the overall operation while managing data processing complexity through modular functional division.
Data Source
AI summary
A method for determining a brake pressure change for a wheel of a vehicle to optimize a braking operation. The method includes: supplying a current wheel status of the wheel, wherein the wheel status includes a plurality of status parameters; determining at least one status parameter whose value deviates from a target wheel status; determining a change direction of the brake pressure change depending on a deviation of the at least one status parameter from the target wheel status; supplying a brake pressure characteristic map for determining a value of the brake pressure change, wherein the brake pressure characteristic map associates a brake pressure change with the plurality of status parameters and is specific to the determined change direction of the brake pressure change and status parameter change; determining a value of the brake pressure change using the current wheel status and the supplied brake pressure characteristic map.