Tie Rod Load Modeling for Fast Vehicle Road Friction Estimation
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Solution Overview
Problem
Existing vehicle control systems struggle to accurately and efficiently estimate the coefficient of friction for various driving surfaces, which affects the effectiveness of control strategies, particularly in conditions with low friction such as ice, snow, or hydroplaning.
Innovation Solution
A system and method that utilizes vehicle sensors and an electronic processor to estimate the coefficient of friction by measuring motor torque, torsion bar torque, and lateral slip angle, employing a look-up table to determine the driving surface type and control vehicle operations accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods are used to estimate coefficient of friction, then the estimation can be obtained, but the calculation time is excessive and affects control strategy effectiveness
Solution Approach 1:
The system pre-calculates and stores rack force values in a look-up table (LUT) before actual operation. During runtime, the processor simply retrieves pre-computed values based on measured lateral slip angle and vehicle speed, eliminating complex real-time calculations while maintaining estimation accuracy
Solution Approach 2:
The patent replaces complex mechanical calculation systems with a data retrieval system using look-up tables. Instead of performing real-time mathematical computations involving multiple physical parameters, the system substitutes these with direct table lookups based on key measured variables (lateral slip angle and vehicle speed)
2Measurement precision
If complex calculation methods are used to improve estimation accuracy, then measurement precision improves, but device complexity increases
Solution Approach 1:
The estimation process is segmented into distinct operational phases: measurement phase (collecting lateral slip angle and vehicle speed), lookup phase (retrieving pre-computed values from LUT), and control phase (applying estimation to control strategy). This segmentation simplifies the real-time processing requirements while maintaining accuracy
Solution Approach 2:
The system uses a simplified computational approach that trades off some calculation depth for speed and simplicity. Rather than implementing complex multi-parameter models, it uses a lighter-weight look-up table method that provides sufficient accuracy for control purposes while reducing processor burden
Data Source
AI summary
Examples provide a system and method for controlling a vehicle or a fleet of vehicles. The system includes a set of vehicle sensors configured to measure a speed of the vehicle and a motor torque. The system also includes an electronic processor configured to determine a modeled rack force of the vehicle, determine a normal force factor of the vehicle, determine a vehicle speed factor of the vehicle, determine an adjusted rack force based on a product of the modeled rack force, the normal force factor, and the vehicle speed factor, determine a lateral slip angle of the vehicle, and determine a coefficient of friction estimation based on the adjusted rack force and the lateral slip angle. The electronic processor is further configured to control the vehicle based on the coefficient of friction estimation.