Hybrid fuel cell and battery propulsion systems have the potential to offer improved emission performance for coastal ships with access to H₂ replenishment and battery charging infrastructures in ports. However, such systems could be constrained by high power source degradation and energy costs. Cost-effective energy management strategies are essential for such hybrid systems to mitigate the high costs. This article presents a Double Q reinforcement learning based energy management system for such systems to achieve near-optimal average voyage cost. The Double Q agent is trained using stochastic power profiles collected from continuous monitoring of a passenger ferry, using a plug-in hybrid fuel cell and battery propulsion system model. The energy management strategies generated by the agent were validated using another test dataset collected over a different period. The proposed methodology provides a novel approach to optimal use hybrid fuel cell and battery propulsion systems for ships. The results show that without prior knowledge of future power demands, the strategies can achieve near-optimal cost performance (96.9%) compared to those derived from using dynamic programming with the equivalent state space resolution.

混合燃料电池和电池推进系统有潜力为接触H _2的沿海船舶提供改善的排放性能港口的补给和电池充电基础设施。但是，此类系统可能会受到高功率源退化和能源成本的限制。具有成本效益的能源管理策略对于此类混合动力系统减轻高成本至关重要。本文介绍了一种基于Double Q强化学习的能源管理系统，该系统可实现接近最佳的平均航行成本。使用插入式混合燃料电池和电池推进系统模型，使用从连续监控客船的过程中收集到的随机功率曲线来训练Double Q代理。使用不同时期收集的另一个测试数据集验证了代理生成的能量管理策略。所提出的方法为船舶的最优使用混合燃料电池和电池推进系统提供了一种新颖的方法。结果表明，与使用等效状态空间分辨率的动态规划所得出的策略相比，无需事先了解未来的电力需求，该策略就可以实现接近最佳的性价比（96.9％）。