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The deep reinforcement learning-based energy management strategies (EMS) havebecome a promising solution for hybrid electric vehicles (HEVs). When drivingcycles are changed, the neural network will be retrained, which is atime-consuming and laborious task. A more efficient way of choosing EMS is tocombine deep reinforcement learning (DRL) with transfer learning, which cantransfer knowledge of one domain to the other new domain, making the network ofthe new domain reach convergence values quickly. Different exploration methodsof DRL, including adding action space noise and parameter space noise, arecompared against each other in the transfer learning process in this work.Results indicate that the network added parameter space noise is more stableand faster convergent than the others. In conclusion, the best explorationmethod for transferable EMS is to add noise in the parameter space, while thecombination of action space noise and parameter space noise generally performspoorly. Our code is available athttps://github.com/BIT-XJY/RL-based-Transferable-EMS.git.

A Comparative Study of Deep Reinforcement Learning-based Transferable Energy Management Strategies for Hybrid Electric Vehicles