Model predictive control based real-time energy management for a hybrid energy storage system
Huan Chen, Rui Xiong, Cheng Lin, Weixiang Shen
Abstract
An accurate driving cycle prediction is a vital function of an onboard energy management strategy (EMS) for a battery/ultracapacitor hybrid energy storage system (HESS) in electric vehicles. In this paper, we address the requirements to achieve better EMS performances for a HESS. First, a long short-term memory-based method is proposed to predict driving cycles under the framework of a model predictive control (MPC) algorithm. Secondly, the performances of three EMSs based on fuzzy logic, MPC, and dynamic programming are systematically evaluated and analyzed. For online implementation, the MPC-based EMS can alleviate the stress on the battery in the HESS and significantly reduce energy dissipation by up to 15.3% in comparison with the fuzzy logic-based EMS. Thirdly, the impact of battery aging on EMS performances is explored; it indicates that battery aging consciousness can slightly extend battery life. Finally, a hardware-in-the-loop test platform is established to verify the effectiveness of the MPC-based EMS for the power allocation of a HESS in electric vehicles.