Optimization of energy management strategy for extended range electric vehicles using multi-island genetic algorithm

Abstract

This study aims to improve the fuel economy of extended range electric vehicles (EREVs) and reduce the cumulative battery workload. Energy management strategy (EMS) of EREVs has a significant impact on improving the energy efficiency, prolonging the service life of batteries, and reducing the fuel consumption. To the best knowledge of the authors, most of existing studies are aimed at optimizing fuel economy, but few researches have taken the service life of the battery into account while improving fuel economy. Our study reflects the power fluctuation range of the battery from the perspective of the battery current, and also further analyzes it from the perspective of the battery energy flow. On the premise of meeting the vehicle power requirement, matrix calculation studies are carried out on the transmission ratio and the key parameters of EMS in the cooperative operation mode of hybrid energy storage system (HESS) based on regular EMS and auxiliary power unit (APU) based on equivalent fuel consumption minimum strategy (ECMS). Via Simulink software, the corresponding vehicle EMS model is developed, and the joint simulation platform of AVL Cruise and Simulink is constructed to verify the effectiveness of the proposed EMS. In order to further tap the energy-saving potential of the proposed strategy based on HESS, the multi-island genetic algorithm is adopted. Under WLTP working condition, the global optimization is conducted with the objectives of minimizing the equivalent fuel consumption and the cumulative ampere-hour through the battery when the vehicle adopts HESS and APU & ECMS operate in concert operation mode. The correlation analysis of the optimization variables is performed. The results show that the fuel economy of the optimized operation mode under WLTP condition is increased by 4.49 %, and the cumulative ampere-hour through the battery is reduced by 11.37 %.