Learning model predictive control for efficient energy management of electric vehicles under car following and road slopes

In this paper, a novel control algorithm is proposed using Model Predictive Control for improving energy consumption of fully electric vehicles (FEVs) and Deep Reinforcement Learning (DRL) for understanding the driving environment in real-time, respectively. The dynamics of the FEVs and the brushless DC motor (BLDC) based powertrain system is utilized for the DRL process, and a Model Predictive Control (MPC) based optimal speed strategy for minimizing battery power consumption is developed by updating the approximation model through the repeated experience. Using the high fidelity car simulator (CarSim), the proposed algorithm is evaluated under freeway cruising scenarios with road slopes as well as preceding car following. The simulation results demonstrate in overall potential energy saving of 3.2%. (C) 2022 The Author(s). Published by Elsevier Ltd.