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Deep reinforcement learning-based energy management strategy play an essential role in improving fuel economy and extending fuel cell lifetime for fuel cell hybrid electric vehicles. In this work, the traditional Deep Q-Network is compared with the Deep Q-Network with prioritized experience replay. Furthermore, the Deep Q-Network with prioritized experience replay is designed for energy management strategy to minimize hydrogen consumption and compared with the dynamic programming. Moreover, the fuel cell system degradation is incorporated into the objective function, and a balance between fuel economy and fuel cell system degradation is achieved by adjusting the degradation weight and the hydrogen consumption weight. Finally, the combined driving cycle is selected to further verify the effectiveness of the proposed strategy in unfamiliar driving environments and untrained situations. The training …