Fuzzy Adaptive-Equivalent Consumption Minimization Strategy for a Parallel Hybrid Electric Vehicle

Abstract

Hybrid electric vehicles (HEVs) have proved a feasible option to reduce fuel consumption and emissions. Furthermore, energy management strategies (EMSs) play a pivotal role in the performance of HEVs. This paper presents a novel real-time EMS, namely fuzzy adaptive-equivalent consumption minimization strategy (Fuzzy A-ECMS), for a parallel HEV. The proposed EMS is formulated by combining the ECMS, which is derived from Pontryagin's minimum principle (PMP), with a fuzzy logic controller adjusting the equivalent factor (EF) based on the deviation between reference state of charge (SOC) and actual SOC for a better SOC trajectory. Improved fuel economy and SOC charge sustainability are the main control objectives. To test and verify the performance of the studied controller, comparative simulations of the Fuzzy A-ECMS and rule-based EMS, conventional SOC-based A-ECMS together with standard ECMS under different standard driving cycles and a real driving cycle are conducted via MATLAB/Simulink and AVL CRUISE. The simulation results show the feasibility and effectiveness of Fuzzy A-ECMS, yielding 0.46% to 5.91% reduction of fuel consumption and more stable SOC charge sustainability compared with the other three EMSs.