Energy management strategy of fuel cell vehicles with hybrid energy sources: A novel framework via deep reinforcement learning and transfer learning

Abstract

This study presents a multicriteria energy management strategy (EMS) for hybrid energy sources (HES) composed of fuel cell/battery/supercapacitor hybrid power system for logistics trucks, which uses a model-free deep reinforcement learning (DRL) algorithm, namely deterministic strategy gradient (DDPG), to improve the portability and reusability of the system. The proposed EMS is capable of reducing the hydrogen consumption cost, the degradation of fuel cell and battery, as well as sustaining the state of charge (SOC) of battery and supercapacitor. The results of the study found that the total cost was reduced by 9.5% compared to equivalence consumption minimization strategy (ECMS) based EMS under the WLTP driving cycle. A novel deep transfer learning (DTL) based framework for DRL-based EMS is further elaborated and evaluated by four metrics. Two DTL techniques including policy transfer and experience transfer are leveraged to transfer the EMS from original logistic truck to a B-class passenger car powered by fuel cell and battery. The results indicate that the proposed DTL framework is an appropriate approach to transfer EMSs from different vehicle model with various power topology. The convergence speed of DTL-based EMS is apparently accelerated over 50% in comparison to DRL-based EMS. Besides, the fuel optimality, robustness, convergence, and generalization stability for DTL-based EMS is also improved.