Rule learning based energy management strategy of fuel cell hybrid vehicles considering multi-objective optimization. (15th September 2020) - Liu, Yonggang; Liu, Junjun; Zhang, Yuanjian; Wu, Yitao; Chen, Zheng; Ye, Ming
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. Highlights: A rule-learning based multi-objective energy management strategy is proposed. The repeated incremental pruning to production error reduction algorithm is applied. The parameters of the fitted multiple nonlinear regression are solved out. The hydrogen economy is optimized together with extension of fuel cell life.