Health- and behavior-aware energy management strategy for fuel cell hybrid electric vehicles based on parallel deep deterministic policy gradient learning

To find a more optimal way to solve the energy management strategy (EMS) of fuel cell hybrid electric vehicles (FCHEVs), the majority of existing research focuses on external driving conditions, while the driver’s behavior as a more important internal influence factor also needs to be taken into account. In this paper, a health- and behavior-aware two-layer hierarchical energy management framework using an improved adaptive parallel deep deterministic policy gradient (DDPG) learning algorithm is proposed for obtaining the optimal EMS of a multi-source FCHEV. In the upper layer, machine learning approaches are employed to recognize the real-time driver’s behavior, and Pontryagin’s minimum principle is applied to calculate the optimal equivalent factor of each driver’s behavior. In the lower layer, to protect the service life of fuel cell and battery as well as increase the learning efficiency, an adaptive fuzzy filter is used, and a health- and behavior-aware multi-objective adaptive equivalent consumption minimization strategy model is constructed and solved by an improved adaptive parallel DDPG-based algorithm. Simulation results show that, the EMS obtained by the proposed DDPG algorithm can achieve the highest fuel cell (FC) working efficiency (approximate to 56%), apparently reduce the degree of degradation of battery (BAT) from 0.42% to 0.28%, and achieve a reduction of 9.24% in terms of the total cost to use compared with deep Q network (DQN)-based EMS.