Abstract: In order to reduce the fuel economy cost of fuel cell hybrid systems of city EMUs and improve the durability of the fuel cell, this paper proposes an energy management method based on proximal policy optimization algorithm. The method models the hybrid system energy management problem as a Markov decision process, and sets the reward function with the optimization objective of minimizing the comprehensive value depletion considering both fuel economy and fuel cell durability. Then, a deep reinforcement learning algorithm with high convergence speed, the proximal policy optimization algorithm, is used to solve the problem and achieve a reasonable and effective distribution of load power between the fuel cell and lithium battery, and finally, the actual operating conditions of EMUs are used for experimental verification. The experimental results show that the proposed method reduces the comprehensive value depletion by 19.71% and 5.87% under the training condition compared with the equivalent hydrogen consumption minimum and the Q-learning respectively, and reduces the comprehensive value depletion by 18.05% and 13.52% under the unknown condition respectively. The results show that the proposed method can effectively reduce the comprehensive value depletion and has good adaptability to working conditions.