A Computationally Efficient Robust Model Predictive Control Framework for Ecological Adaptive Cruise Control Strategy of Electric Vehicles

TL;DR

This paper presents a computationally efficient robust model predictive control framework for electric vehicles that enhances energy efficiency and safety in adaptive cruise control by combining feedback linearisation with RMPC.

Contribution

It introduces a novel RMPC scheme with feedback linearisation for electric vehicle control, improving computational speed and robustness against model mismatch.

Findings

Higher energy efficiency compared to benchmark methods

Improved passenger comfort levels

Validated robustness through numerical simulations

Abstract

The recent advancement in vehicular networking technology provides novel solutions for designing intelligent and sustainable vehicle motion controllers. This work addresses a car-following task, where the feedback linearisation method is combined with a robust model predictive control (RMPC) scheme to safely, optimally and efficiently control a connected electric vehicle. In particular, the nonlinear dynamics are linearised through a feedback linearisation method to maintain an efficient computational speed and to guarantee global optimality. At the same time, the inevitable model mismatch is dealt with by the RMPC design. The control objective of the RMPC is to optimise the electric energy efficiency of the ego vehicle with consideration of a bounded model mismatch disturbance subject to satisfaction of physical and safety constraints. Numerical results first verify the validity and robustness through a comparison between the proposed RMPC and a nominal MPC. Further investigation into the performance of the proposed method reveals a higher energy efficiency and passenger comfort level as compared to a recently proposed benchmark method using the space-domain modelling approach.