A fuel cell and battery hybrid driving system is an important approach to reducing the carbon emissions of heavy-duty commercial vehicles, which require longer driving ranges and higher driving power. For fuel cell heavy-duty commercial vehicles (FCHCVs), the component sizing of the fuel cell and battery, as well as the energy management strategy (EMS), exert a significant influence on operating costs. To address the challenge of high computational cost in component sizing optimization, the bi-level offline optimization strategy with higher efficiency is proposed. The outer-layer optimization leverages a genetic algorithm (GA) to determine the optimal sizes of the fuel cell and battery by minimizing the total operating cost of FCHCVs, while the inner-layer optimization utilizes linear programming (LP) to identify the optimal power allocation strategy between the fuel cell and battery. LP exhibits the same accuracy as the benchmark method, dynamic programming (DP), yet offers significantly higher computational efficiency than DP. After determining the optimal component sizes, an online multi-objective EMS (MOEMS) is developed. The MOEMS allocates power between the fuel cell and battery by minimizing in real time the total cost, which includes hydrogen consumption, battery equivalent hydrogen consumption, and degradation of the fuel cell and battery. Simulation results from standard driving cycles and a practical long-range driving cycle indicate that the MOEMS achieves lower overall costs compared to the original rule-based EMS (REMS).
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