Hierarchical reinforcement learning based energy management strategy for hybrid electric vehicle

As the core technology of hybrid electric vehicles (HEVs), energy management strategy directly affects the fuel consumption of vehicles. This research proposes a novel reinforcement learning (RL)-based algorithm for energy management strategy of HEVs. Hierarchical structure is used in deep Q-learning algorithm (DQL-H) to get the optimal solution of energy management. Through this new RL method, we not only solve the problem of sparse reward in training process, but also achieve the optimal power distribution. In addition, as a kind of hierarchical algorithm, DQL-H can change the way of exploration of the vehicle environment and make it more effective. The experimental results show that the proposed DQL-H method realizes better training efficiency and lower fuel consumption, compared to other RLbased ones. (c) 2021 Elsevier Ltd. All rights reserved.

Automated summary

This research introduces a novel reinforcement learning-based deep Q-learning algorithm for the energy management strategy of HEVs. The proposed method not only addresses the issue of sparse reward during training, but also achieves optimal power distribution. Additionally, the hierarchical structure of the algorithm enhances exploration of the vehicle environment, leading to improved training efficiency and reduced fuel consumption.