Data-driven reinforcement-learning-based hierarchical energy management strategy for fuel cell/battery/ultracapacitor hybrid electric vehicles

A reinforcement-learning-based energy management strategy is proposed in this paper for managing energy system of Fuel Cell Hybrid Electric Vehicles (FCHEV) equipped with three power sources. A hierarchical power splitting structure is employed to shrink large state-action space based on an adaptive fuzzy filter. Then, the reinforcement-learning-based algorithm using Equivalent Consumption Minimization Strategy (ECMS) is proposed for tackling high-dimensional state-action space, and finding a trade-off between global learning and real-time implementation. The power splitting policy based on experimental data is obtained by using reinforcement learning algorithm, which allows for many different driving cycles and traffic conditions. The proposed energy management strategy can achieve low computation cost, optimal fuel cell efficiency and energy consumption economy. Simulation results confirm that, compared with existing learning algorithms and optimization methods, the proposed reinforcement-learning-based energy management strategy using ECMS can achieve high computation efficiency, lower power fluctuation of fuel cell and optimal fuel economy of FCHEV.