Capacity configuration of fuel cell hybrid vehicles using enhanced multi-objective particle swarm optimization with competitive mechanism

A well-designed hybrid powertrain is crucial for ensuring the safe, efficient, and durable operation of fuel cell hybrid vehicles. This paper introduces a modular design approach for powertrains, utilizing a Competitive mechanism-based Multi-Objective Particle Swarm Optimization (CMOPSO) algorithm integrated with nested dynamic programming. In this approach, the upper layer employs the CMOPSO algorithm to design the powertrain system, while the lower layer optimizes power coordination for each proposed design. This two-layer optimization framework considers factors such as vehicle economy and durability. Under WLTP conditions, the capacity configuration results are a fuel cell with a rated power of 22 kW, 100 batteries in series, and 7 batteries in parallel. Furthermore, the modular approach outperforms three other algorithms in terms of solution count, diversity, and overall performance metrics. The study also highlights that the vehicle’s power demand characteristics, influenced by different driving cycles, significantly affect capacity configuration results. Sensitivity analysis reveals that both the total operating cost and manufacturing cost of the vehicle are most sensitive to variations in the fuel cell rated power.