Robustness Optimization of the Energy Management Strategy for a Fuel Cell Vehicle Using Adversary Evolutionary Learning

The energy management strategy (EMS) of the fuel cell electric vehicle (FCEV) is a control process that crucially affects the FCEV’s economic performance and driving range. Conventionally, EMS optimization relies on standard driving cycles, such as the worldwide harmonized light vehicles test cycle (WLTC). However, these often lack robustness due to their limited representation of real-world scenarios. This study proposes an adversary evolutionary learning (AEL) scheme that generates challenging driving cases during the optimization process to enhance robustness. This study demonstrated the effectiveness of AEL based on an FCEV controlled by the equivalent consumption minimization strategy (ECMS), where the equivalent factor (EF) settings must be fine-tuned. The AEL scheme involves interactive attack and defense rounds. In the attack round, a genetic algorithm (GA)-based cycle generator creates challenging driving cycles to stress-test the EF settings. In the defense round, particle swarm optimization (PSO) tunes the EF set by a fuzzy inference system (FIS) for energy efficiency and stability in the state of charge (SoC) of the battery. After 30 rounds, AEL identifies 30 harsh driving cycles and 30 optimized ECMS. Crossover testing is then performed to select the most robust ECMS (R-ECMS) set. Processor-in-the-loop (PiL) experiments on standard real-world cycles demonstrate that AEL effectively identifies the R-ECMS, potentially saving up to 1.37% of hydrogen and reducing SoC fluctuations by up to 47.43%.