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.

中文翻译：

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混合动力履带车辆迁移深度强化学习能源管理策略


本文结合深度强化学习（DRL）和迁移学习（TL），提出了一种混合动力电动汽车的自适应能量管理策略。这项工作旨在解决 DRL 训练时间繁琐的缺陷。首先，建立了混合动力履带车辆的优化控制模型，其中引入了复杂的动力总成部件。然后，构建双层控制框架来导出能源管理策略（EMS）。上层应用特定的深度确定性策略梯度（DDPG）算法进行不同速度间隔的 EMS 训练。下层采用 TL 方法来转换预先训练的神经网络，以适应新的驾驶循环。最后，进行了一系列实验来证明所提出的控制框架的有效性。阐述了所配制的 EMS 的最优性和适应性。所建立的DRL和TL支持的控制策略能够提高能源效率并改善系统性能。