Validation and Refinement of a Steering Friction Increase Detection Algorithm Using Test Drive Data

Abstract

The Electric Power Steering (EPS) System provides steering assist in conventional vehicle driving and is the main actuator for vehicle lateral control in active safety features. While the driver can sometimes compensate for reduced or loss of steering assist caused by EPS mechanical or electrical degradations, it may become very difficult to steer for larger vehicles. Furthermore, active safety functions cannot control the vehicle effectively for lateral motions without a healthy EPS system. Hence, comprehensive EPS system fault monitoring is essential for the next generation of vehicles. Previous works have utilized computer simulation and hardware-in-the-loop experiments to develop fault diagnosis and prognosis algorithms for electrical and mechanical failures in EPS systems. Using test drive data collected, this paper validates and refines a previously developed algorithm designed for detecting increases in EPS system internal mechanical friction. The data include 215 minutes of natural driving with different speeds and steering maneuvers. Noise factors such as tire type and level of friction introduced are also considered. The previous algorithm is refined to enhance performance addressing issues of time delays and parameter uncertainty specific to the previous model-based algorithm. Specifically, a Kalman filter-based joint state-parameter estimator that uses a simplified vehicle dynamic model is developed to provide a direct estimate of steering friction increase. Data collected from test drives indicate that the refined algorithm can robustly indicate a friction increase before an average human driver notices a difference in steering feel.