A Real-time Predictive Energy Management Strategy for Power-split Plug-in Hybrid Electric Bus

This paper proposes a real-time predictive energy management strategy (EMS) for a power-split plug-in hybrid electric bus (PHEB) to improve fuel economy by decreasing the total operation cost of fuel and electricity. Firstly, a two- dimensional (2-D) velocity prediction method is adopted to improve the accuracy of the prediction. Then, an online optimal controller is designed to distribute power flows optimally by tracking the SOC reference trajectory accurately. At last, comprehensive comparative simulations are conducted to validate the effectiveness of the proposed EMS in terms of fuel economy improvement and real-time application performance. Simulation results indicate that the proposed EMS in this paper can reduce the total cost by 8.65% in comparison with rule-based strategy and the longest prediction horizon can reach 15 $s$ at least for real-time application.

I. Introduction

Energy management strategies (EMSs) determines the fuel economy of plug-in hybrid electric vehicles (PHEVs) [1], there are multifarious EMSs that have been researched, which can be mainly divided into three categories.: rule-based, optimization-based, and learning-based [2], [3]. Rule-based EMSs are derived from engineering experience, which have strong practicability but lack of adaptability to different driving cycles [4].