Adaptive model predictive traction control for electric vehicles

Abstract

With the recent emergence of electric powertrains, a faster and easy to model actuator, the electric motor, became available for the control of longitudinal dynamics. Therefore model-based control approaches promise an increase in control performance, especially for processes such as traction control that require highly dynamic control intervention. The task of traction controllers is to prevent the driven wheels from slipping and thus ensure the vehicle's steerability. In this paper, a model predictive control approach to traction control is developed. A semi implicit method to discretize the underlying model was proposed to handle numerical stability problems at low speeds in real time. Due to changing environmental conditions, the functionality of the traction controller is limited and may lead to performance degradation or even failure. Therefore, a maximum friction coefficient estimation utilizing an unscentend Kalman filter is integrated. The overall control scheme is experimentally evaluated with a Volkswagen Golf GTE Plug-In Hybrid on a test track with a wet steel road surface.