Comprehensive optimization of fuzzy logic-based energy management system for fuel-cell hybrid electric vehicle using genetic algorithm

This article presents a comprehensive optimization approach for a fuzzy logic-based energy management system (EMS) designed for a fuel cell hybrid electric vehicle (FCHEV) powered by a fuel cell and a battery. The aim is to enhance EMS performance and overall system efficiency by optimizing fuzzy logic parameters using a genetic algorithm (GA). The effectiveness of the proposed strategy is demonstrated through simulations using ADVISOR software and the MATLAB/SIMULINK environment. The optimization process consists of four phases: tuning the membership function types, fuzzy variable ranges, membership function parameters, and rule weights. The proposed method has resulted in a significant improvement in the fitness function, highlighting the high sensitivity of the fuzzy logic system to optimization parameters. The optimized EMS is compared with the original one and a thermostat-based strategy built into ADVISOR. Results show a 28.5% improvement in fuel utilization and a 13% reduction in the low-efficiency operation of the fuel cell system.