Chen, Zhentao (Tsinghua University), Ye, Xiaoming (Dongfeng Technology Center), Liu, Zijun (Tsinghua University), SHI, Biaofei (Tsinghua University), Wang, Xiangyu (Tsinghua University), Li, Liang (Tsinghua University)

An Adaptive Wheel Cylinder Pressure Estimator Based on Comprehensive Hydraulic Model for Integrated Brake System

Scheduled for presentation during the Invited Session "Learning-empowered Intelligent Transportation Systems: Foundation Vehicles and Coordination Technique I" (WeAT1), Wednesday, September 25, 2024, 11:10−11:30, Salon 1

2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC), September 24- 27, 2024, Edmonton, Canada

This information is tentative and subject to change. Compiled on December 26, 2024

Abstract

Integrated brake system (IBS) is a state-of-the-art brake-by-wire system with superior performance and suitability in high-level autonomous vehicles. The precise estimation of the wheel cylinder pressure is the foundation of the control accuracy and fault-tolerance control. A comprehensive model of the IBS system is built in this paper encompassing all hydraulic cylinders. To enhance the estimation precision meanwhile ensure numerical stability, an estimator based on the square-root cubature Kalman filter (SCKF) is proposed. Further, to mitigate the estimation divergence caused by the model distortion under the low speed and standstill conditions, an adaptive mechanism is designed. Vehicle tests are conducted to verify the effectiveness of the proposed adaptive SCKF (ASCKF) estimator in conditions involving the anti-lock braking system. The results demonstrate that the ASCKF exhibits superior estimation precision compared to the extended Kalman filter by 29.8% in comprehensive RMSE and 23.9% in comprehensive MnAE, and maintains effectiveness in the presence of model distortion.