基于多目标优化的重载卡车双燃料电池系统分层能量管理策略

Abstract Multiple fuel cell systems integrated with a battery system have become a leading powertrain configuration for long-haul heavy-duty trucks. However, the inherent complexity of the multi-source architecture poses substantial challenges for effective energy management. Current energy management strategies for such hybrid systems often rely on single-objective optimization, which limits their ability to effectively balance fuel efficiency and system durability, particularly under dynamic operating conditions. This study presents a dual-layer energy management strategy aimed at optimizing power distribution between multiple fuel cell systems and a battery system, thereby achieving improved trade-offs between fuel economy and system lifespan. The proposed strategy comprises two layers: the lower layer performs optimal power allocation between the two parallel fuel cell subsystems, while the upper layer employs Q-learning to regulate power distribution between the aggregated fuel cell system output and the battery system. Compared to two widely adopted dual-layer strategies that primarily target fuel economy, the proposed approach achieves a reduction in system degradation by 41.57% and 24.64%, respectively, while incurring only marginal increases in fuel consumption, 2.96% and 0.06%. Hardware-in-the-loop simulations further confirm the real-time performance of the proposed strategy across varying driving cycles. By successfully balancing fuel efficiency and system durability, the dual-layer energy management strategy presents a promising solution for energy management in fuel cell-powered heavy-duty trucks.