Brake Friction Coefficient Modeling for Real-Time Torque Prediction
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Solution Overview
Problem
Conventional methods for predicting brake torque in vehicle performance simulations inaccurately use a fixed mean friction coefficient, neglecting real-time changes due to vehicle driving conditions such as disc rotation speed, temperature, and hydraulic pressure, leading to unreliable simulation results.
Innovation Solution
A friction coefficient meta model is generated using machine learning, based on raw data from test evaluations, incorporating disc rotation speed, disc temperature, and brake hydraulic pressure to accurately estimate the friction coefficient in real-time, enabling precise brake torque calculation and improved vehicle performance prediction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If a fixed mean friction coefficient is used for brake torque calculation, then the calculation process is simple, but the prediction accuracy of brake performance deteriorates
Solution Approach 1:
The patent applies dynamics by transitioning from a static fixed friction coefficient to a dynamic friction coefficient that varies with operating conditions (disc rotation speed, temperature, and hydraulic pressure). The friction coefficient is now a function μ(ω, T, p) that adapts to real-time brake operating conditions, thereby improving prediction accuracy while maintaining computational feasibility through the use of pre-established lookup tables and interpolation methods.
Solution Approach 2:
The patent implements parameter changes by introducing multiple variables (disc rotation speed ω, disc temperature T, and hydraulic pressure p) that influence the friction coefficient. Instead of using a single fixed value, the friction coefficient is determined based on the combined effect of these three parameters, allowing the system to account for the nonlinear relationship between operating conditions and friction characteristics.
2Measurement precision
If real-time friction coefficient determination considering driving conditions is implemented, then brake torque prediction accuracy is improved, but the system complexity increases
Solution Approach 1:
The patent applies preliminary action by pre-establishing friction coefficient maps and lookup tables during the design phase, based on extensive testing and characterization of the brake system under various operating conditions. These pre-computed data structures store the relationship between (ω, T, p) and μ, allowing the control system to quickly retrieve and interpolate friction coefficients during real-time operation without performing complex calculations, thus reducing computational burden and system complexity.
Solution Approach 2:
The patent replaces complex mechanical testing and measurement systems with a computational approach using pre-established friction coefficient maps and interpolation algorithms. Instead of requiring real-time physical measurement of friction coefficients during brake operation, the system uses mathematical models and data lookup methods to determine μ based on measured operating parameters, simplifying the overall system architecture.
3Measurement precision
If friction coefficient is determined as a function of disc rotation speed, temperature, and hydraulic pressure, then the friction coefficient accuracy is improved, but the data processing complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the three-dimensional parameter space (ω, T, p) into discrete grids or tables during the pre-processing phase. The friction coefficient data is organized into structured lookup tables where each dimension (rotation speed, temperature, pressure) is discretized into manageable steps. This segmentation allows the system to handle complex nonlinear relationships through simple table lookups and linear interpolation, reducing the computational complexity of real-time data processing while maintaining high accuracy.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach allows for accurate prediction of friction coefficients and brake torque under dynamic driving conditions, enhancing the reliability of vehicle performance simulations and control logic.
Implementation Method 1
The brake device generates braking force for decelerating or stopping the vehicle using friction. The brake device generates braking force by converting kinetic energy of the vehicle into thermal energy through friction
Data Source
AI summary
An apparatus and a method can accurately estimate and determine a friction coefficient of a brake friction material in real time taking into consideration current driving conditions of a vehicle. The apparatus includes a model generation device configured to generate a friction coefficient meta model to determine the friction coefficient based on information of an operation state of a brake using raw data acquired through a preceding test evaluation process.