Abstract In this article, a multi-objective optimization-oriented energy management strategy is investigated for fuel cell hybrid vehicles on the basis of rule learning. The degradation of fuel cells and lithium-ion batteries are considered as the objective function and translated into the equivalent hydrogen consumption. The optimal fuel cell power sequence and state of charge trajectory, considered as the energy management input, are solved offline via the Pontryagin’s minimum principle. The K-means algorithm is employed to hierarchically cluster the optimal data set for preparation of rules extraction, and then the rules are excavated by the improved repeated incremental pruning to production error reduction algorithm and fitted by the quasi-Newton method. The simulation results highlight that the proposed rule learning-based energy management strategy can effectively save hydrogen consumption and prolong fuel cell life with real-time application potential.

中文翻译：

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基于规则学习的燃料电池混合动力汽车能量管理策略考虑多目标优化

摘要 本文研究了基于规则学习的燃料电池混合动力汽车多目标优化能源管理策略。燃料电池和锂离子电池的退化被视为目标函数并转化为等效的氢消耗量。最佳燃料电池电源顺序和充电状态轨迹被视为能量管理输入，通过庞特里亚金最小原理离线求解。采用K-means算法对最优数据集进行层次聚类，为规则提取做准备，然后通过改进的重复增量剪枝到生产误差减少算法挖掘规则，并通过拟牛顿法进行拟合。