期刊
IEEE ACCESS
卷 8, 期 -, 页码 165837-165848出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2020.3022944
关键词
Energy management; Batteries; Generators; Resistance; Mechanical power transmission; Engines; Training; Deep reinforcement learning; transfer learning; hybrid tracked vehicle; energy management strategy; deep deterministic policy gradient
资金
- Chongqing Science and Technology Project [cstc2019jcyj-msxmX0636, cstc2019jcyj-msxmX0481]
- Yangtze Normal University [2016KYQD16]
- Chongqing Education Commission [KJ1712297, KJQN201901321]
This paper proposes an adaptive energy management strategy for hybrid electric vehicles by combining deep reinforcement learning (DRL) and transfer learning (TL). This work aims to address the defect of DRL in tedious training time. First, an optimization control modeling of a hybrid tracked vehicle is built, wherein the elaborate powertrain components are introduced. Then, a bi-level control framework is constructed to derive the energy management strategies (EMSs). The upper-level is applying the particular deep deterministic policy gradient (DDPG) algorithms for EMS training at different speed intervals. The lower-level is employing the TL method to transform the pre-trained neural networks for a novel driving cycle. Finally, a series of experiments are executed to prove the effectiveness of the presented control framework. The optimality and adaptability of the formulated EMS are illuminated. The founded DRL and TL-enabled control policy is capable of enhancing energy efficiency and improving system performance.
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