Dual-objective energy management strategy for HEV

Abstract

Based on equivalent consumption minimization strategy (ECMS), the approaches by genetic algorithm (GA), learning vector quantization neural networks (LVQ NNs) and fuzzy logic algorithm (FLA) are integrated to adjust/tune the power split ratio between internal combustion engines (ICE) and belt-driven starter generators (BSG). The proposed bi-object equivalent consumption minimization strategy (BOECMS) possesses three key features: being realtime, causal and capable of fulfilling two objects, namely, (i) minimizing fuel consumption, and (ii) ensuring a stable battery state of charge (SOC) within a relatively narrow range. A hybrid electric vehicle (HEV) model and its corresponding power split strategy are developed and verified by using the vehicle simulator ADVISOR (advanced vehicle simulator) and Simulink at the design stage. For practicality, the proposed control strategy, BOECMS, is converted into C code and then written into the embedded micro-processor to conduct the necessary hardware-inthe-loop (HIL) experiments at the verification stage. According to computer simulation results, fuel economy improved by 40.39 % over pure ICE vehicles for the "MANHATTAN" drive cycle. In addition, the SOC can be retained within a relatively narrow range: [0.4, 0.6]. Finally and significantly, the experimental results by HIL converge well with computer simulation results using Simulink, implying BOECMS can potentially be applied to the real -world driving in the future.