Model Predictive Control for Battery Electric Vehicles Considering Energy Efficiency, Battery Degradation and Tire Wear

Eco-driving of Battery Electric Vehicles (BEVs) has been extensively studied in the past decade because of its potential of enhancing the energy efficiency of individual BEVs without significantly increasing the hardware investment. In this study, we propose a model predictive control (MPC)-based eco-driving control scheme which simultaneously considers the vehicle efficiency, battery degradation, and tire wear of BEVs in the optimization of speed profile. An electro-chemical battery degradation model is deployed to account for different aging factors, and a regression model is utilized to quantify the tire wear based on non-exhaust particle matter emissions. Furthermore, a deep neural network-based velocity prediction model is trained and integrated to the control framework to accommodate the requirements of forecasting future speed due to the nature of MPC. Comparative studies have been performed in a real-world driving cycle. Optimization results show that tire particulate matter (PM) emissions, battery degradation, and energy consumption can be reduced by 44.15%, 2.88%, and 0.73%, respectively, when compared to the baseline controller. Copyright (c) 2024 The Authors.