Anti-jerk model predictive cruise control for connected electric vehicles with changing road conditions

All electric vehicles are fitted with Cruise Control (CC) systems, an Advanced Driver Assistance System (ADAS) designed to regulate the vehicle at a desired velocity. However, road and weather related effects have not yet been included in the design of CC systems. With the advent of autonomous vehicles, CC systems will need to provide control based on road-friction conditions. In this research, we develop a anti-jerk model predictive cruise controller for electric vehicles adaptive to road conditions. A high-fidelity longitudinal dynamics model has been developed for the test vehicle for our research, a Toyota Rav4EV. A powertrain model based on Pacejka relaxation length tire model has been used to study the slip response characteristics and a recursive least square estimator has been used for estimating the road characteristics. The performance of the adaptive controller has been assessed based on the high-fidelity vehicle model on a low-friction road surface.

I. Introduction

Cruise controllers are designed typically to regulate the vehicle at a desired velocity or maintain a fixed distance with respect to the preceding vehicle. According to a survey report by National Highway Traffic Safety Administration (NHTSA) [1], high jerk in cruise controllers is preventing users from using cruise-control and is now an important criteria for evaluating the performance of ACC. EVs fitted with central drive powertrain configurations are especially susceptible to torsional oscillations resulting in jerks during tip-in and tip-out maneuvers [2] as well as slip of tires [3].