• A novel energy management strategy based on Double Deep Q-Learning (DDQL) is developed for fuel cell hybrid electric buses, incorporating fuel cell power change rate as action variables to respect power response characteristics.
• The proposed DDQL-based EMS achieves a 15.4% improvement in fuel economy over a rule-based strategy under training scenarios, and 13.3% under testing scenarios, demonstrating strong generalization.
• Comparative analysis with dynamic programming (DP) and rule-based strategies validates the effectiveness of the DDQL approach in balancing fuel economy and real-time applicability.
• The study addresses the high operational costs of fuel cell systems by optimizing energy management, contributing to the economic viability of fuel cell hybrid electric vehicles.