论文标题
通过深度加固学习，数据驱动的混合动力汽车转移能源管理策略
Data-Driven Transferred Energy Management Strategy for Hybrid Electric Vehicles via Deep Reinforcement Learning
论文作者
论文摘要
能源管理策略（EMSS）在混合动力汽车（HEV）中的实时应用是研究人员和工程师的最严厉要求。受深入增强学习（DRL）出色的解决问题的能力的启发，本文提出了通过合并DRL方法和转移学习（TL）的实时EMS。相关的EMS是从从运输安全数据中心（TSDC）的现实世界中收集的驾驶周期数据集中得出并评估的。混凝土DRL算法是属于策略梯度（PG）技术的近端策略优化（PPO）。为了规范，许多来源驱动周期都用于训练基于PPO的深网的参数。在TL框架下，学习的参数转换为目标驱动周期。在不同的训练条件下，估算了与目标驾驶周期相关的EMSS。仿真结果表明，所提出的基于DRL的EMS可以有效地降低时间消耗并确保控制性能。
Real-time applications of energy management strategies (EMSs) in hybrid electric vehicles (HEVs) are the harshest requirements for researchers and engineers. Inspired by the excellent problem-solving capabilities of deep reinforcement learning (DRL), this paper proposes a real-time EMS via incorporating the DRL method and transfer learning (TL). The related EMSs are derived from and evaluated on the real-world collected driving cycle dataset from Transportation Secure Data Center (TSDC). The concrete DRL algorithm is proximal policy optimization (PPO) belonging to the policy gradient (PG) techniques. For specification, many source driving cycles are utilized for training the parameters of deep network based on PPO. The learned parameters are transformed into the target driving cycles under the TL framework. The EMSs related to the target driving cycles are estimated and compared in different training conditions. Simulation results indicate that the presented transfer DRL-based EMS could effectively reduce time consumption and guarantee control performance.
