Energy management strategy for fuel cell electric vehicles based on scalable reinforcement learning in novel environment

To optimize the hydrogen consumption, lifespan of the fuel cell (FC), and durability of the lithium -ion battery in fuel cell electric vehicles (FCEVs), energy management strategies (EMSs) have been developed and implemented in the vehicle control units (VCUs) by engineers. Recently, with the rapid development of artificial intelligence, reinforcement learning (RL)-based EMS have shown great promise among various strategies. However, RL-based EMS tends to exhibit relatively poorer performance when faced with novel environments owing to the requirement for learning. This study proposes an enhanced EMS, Scalable Learning in Novel Environment (SLNE)-based EMS. This EMS performs well in unknown and dynamic environments by integrating a memory library (ML) composed of the Dirichlet process (DRP) clustering algorithm combined with the Chinese restaurant process (CRP) using the expectation -maximization (EM) algorithm for updates and the deep Q -network (DQN). Simulation and comparison are conducted under two novel driving cycles to evaluate the performance of the dynamic programming (DP) -based EMS, rule -based EMS, DQN-based EMS, and SLNE-based EMS. By comparing reward, power, and degradation, the results indicate that the proposed SLNE-based EMS demonstrates excellent convergence and adaptability when facing a novel and dynamic environment. Additionally, the proposed SLNEbased EMS achieves approximately 5% improvement in fuel economy, approximately 4.5% reduction in fuel cell degradation rate, and improved durability of the lithium -ion battery, compared with DQN-based EMS.