Road Adaptive Anti-Slip Regulator for a Distributed Drive Electric Vehicle

2020-01-5122

12/14/2020

Features
Event
SAE 2020 Vehicle Electrification and Autonomous Vehicle Technology Forum
Authors Abstract
Content
Anti-slip regulator (ASR) is one of the most important research focuses in the field of vehicle active safety. An ASR for a distributed drive electric vehicle (DDEV) driven by four in-wheel motors is proposed in this paper, where a tire-road friction coefficient estimator and a road slope estimator are included making the ASR adaptive to road changes. The tire-road friction coefficient estimator is adopted to estimate road condition using improved Burckhardt model, so the optimal reference slip ratio is selected according to the estimated road adhesion coefficient for the maximum driving efficiency and the realization of adaptive anti-slip regulation. At the same time, the road slope is estimated using recursive least square with forgetting factor and the longitudinal acceleration sensor information is calibrated by the road slope estimation for slope adaptive velocity estimation. Because there is no driven wheel in such a DDEV, estimators for small and large slip ratios based on dynamic and kinematic methods are designed respectively, which can switch according to wheel slip conditions. The slip ratio controller in the ASR is designed based on anti-windup sliding mode control law, which is robust to wheel model uncertainties, slip ratio estimation errors and disturbances. Multi-condition field tests and simulations results show that compared with the DDEV without an ASR, the controlled vehicle can prevent serious wheel skid on low adhesion roads and improve driving performance. In addition, the slip ratio controller and estimator are adaptive to road friction and slop changes.
Meta TagsDetails
DOI
https://doi.org/10.4271/2020-01-5122
Pages
8
Citation
Leng, B., Xiong, L., Yu, Z., Chen, X. et al., "Road Adaptive Anti-Slip Regulator for a Distributed Drive Electric Vehicle," SAE Technical Paper 2020-01-5122, 2020, https://doi.org/10.4271/2020-01-5122.
Additional Details
Publisher
Published
Dec 14, 2020
Product Code
2020-01-5122
Content Type
Technical Paper
Language
English