To apply the actual fuel cell hybrid system, improve the efficiency of fuel cells, and reduce the operating cost, the model predictive control (MPC)-based energy management strategy (EMS) combining self-trending prediction and the subset-searching algorithm is proposed in this article. Compared with the traditional MPC, the proposed EMS reduces the error of speed prediction and simplifies the process of solving the optimal control trajectory, which increases the potential for practical engineering applications. Moreover, the proposed EMS considers the degradation of the power sources, which would lead to low operating costs from a long-term scale perspective. Finally, this article carries out a hardware-in-the-loop experiment to verify the feasibility and superiority of the proposed EMS. The results show that compared with the optimal benchmark, the operating cost is only increased by 7.70%, and the fuel cells operate in the high-efficiency range. Otherwise, compared with the traditional Markov chain and dynamic programming, the root-mean-square error and single time of each rolling optimization, respectively, reduce 27.79% and 64.9% at least. In addition, this article tests the adaptability of the proposed EMS on other track sections. The results show that in any track section, the proposed EMS can maintain the state of charge (SOC) and reduce the operating cost.

中文翻译：

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基于模型预测控制的氢电动车组自趋势预测与子集搜索算法相结合的能量管理


为了应用于实际的燃料电池混合动力系统，提高燃料电池的效率，降低运行成本，提出了一种基于模型预测控制（MPC）的自趋势预测与子集搜索算法相结合的能量管理策略（EMS）。在本文中。与传统的MPC相比，所提出的EMS减少了速度预测的误差，简化了最优控制轨迹的求解过程，增加了实际工程应用的潜力。此外，拟议的环境管理体系考虑了电源的退化，从长期规模的角度来看，这将导致较低的运营成本。最后，本文进行了硬件在环实验，验证了所提出的EMS的可行性和优越性。结果表明，与最优基准相比，运行成本仅增加7.70%，燃料电池运行在高效区间。另外，与传统马尔可夫链和动态规划相比，每次滚动优化的均方根误差和单次优化时间分别至少减少了27.79%和64.9%。此外，本文还测试了所提出的 EMS 在其他轨道路段上的适应性。结果表明，在任何轨道路段，所提出的 EMS 都可以维持充电状态（SOC）并降低运营成本。