论文标题
转移深钢筋学习支持的混合动力汽车的能源管理策略
Transfer Deep Reinforcement Learning-enabled Energy Management Strategy for Hybrid Tracked Vehicle
论文作者
论文摘要
本文提出了通过结合深度增强学习（DRL）和转移学习（TL）的混合电动汽车的自适应能源管理策略。这项工作旨在解决繁琐的训练时间中DRL的缺陷。首先，建造了混合轨道车辆的优化控制建模，其中引入了精心设计的动力总成组件。然后，建立了一个双层控制框架，以得出能源管理策略（EMSS）。高层正在以不同的速度间隔应用特定的深层确定性政策梯度（DDPG）算法。较低级别正在采用TL方法来转换预训练的神经网络以进行新的驾驶周期。最后，执行了一系列实验，以证明提出的控制框架的有效性。配制的EMS的最佳性和适应性被照亮。建立的DRL和启用TL的控制政策能够提高能源效率并提高系统性能。
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.
