A Standalone Energy Management System of Battery/Supercapacitor Hybrid Energy Storage System for Electric Vehicles Using Model Predictive Control
Ngoc-Duc Nguyen, Changwoo Yoon, Young Il Lee
Abstract
In this article, a standalone model predictive control (MPC) based energy management strategy (EMS) is proposed for the hybrid energy storage system in electric vehicles. The proposed EMS does not require any knowledge of vehicle speed or future demands, so it can be implemented as a standalone system without interfacing with the motor drive system. Furthermore, only one tuning parameter is used to adjust the performance of the proposed MPC-based EMS. The cost function is made of the deviation of the predicted supercapacitor (SC) voltage from its desired value and the difference between the battery current and its steady-state value. Furthermore, the constraints on the battery current rate and the SC voltage will be enforced when solving the optimization problem of EMS. Based on the finite set of battery current references, the online optimal solution can be implemented in the experiment. Then, the fast convergent current control is designed to track the optimal current reference using a continuous control set MPC. To show the effectiveness of the proposed method, the comparison with the rule-based EMS will be presented in simulation and experiments.