Self-Adaptive Equivalent Consumption Minimization Strategy for Hybrid Electric Vehicles

Abstract

In order to achieve nearly optimal fuel economy for hybrid electric vehicles (HEVs) using an equivalent consumption minimum strategy (ECMS), it is necessary to dynamically tune the equivalent factor (EF). Unlike widely studied ECMSs that adapt EF to driving conditions, the proposed self-adaptive equivalent consumption minimization strategy (SECMS) applies a new idea of determining the EF from the historical driving conditions. To this end, a dynamic EF self-determining algorithm is designed according to the assumption that the electrical energy used at the current moment comes from past recuperation and charging, and an adaptive charge-sustaining algorithm is developed to balance the electrical self-sustainability and optimization performance. To show its effectiveness, the SECMS is implemented to a single-shaft parallel HEV over four standard driving cycles and benchmarked against two conventional ECMS-type control strategies: standard ECMS and adaptive ECMS (AECMS). The results show that the SECMS can achieve significant improvements regarding both the fuel economy and battery charge-sustainability compared to ECMS and AECMS. The proposed SECMS could facilitate a new development of energy management strategies for HEVs based on historical driving information.