论文标题
基于驾驶条件识别的混合动力汽车的转移能源管理策略
Transferred Energy Management Strategies for Hybrid Electric Vehicles Based on Driving Conditions Recognition
论文作者
论文摘要
能源管理策略（EMS）是混合动力汽车（HEV）中最重要的组成部分，因为它们决定了节能和减少排放的潜力。这项工作通过结合增强学习方法和驱动条件识别，为平行的HEV提供了转移的EMS。首先，马尔可夫决策过程（MDP）和过渡概率矩阵用于区分驾驶条件。然后，制定了增强学习算法以实现功率分开控制，其中Q-table通过当前的驾驶情况进行调整。最后，在平行的混合拓扑中估算并验证了拟议的转移框架。总结并证明了其在计算效率和燃油经济性方面的优势。
Energy management strategies (EMSs) are the most significant components in hybrid electric vehicles (HEVs) because they decide the potential of energy conservation and emission reduction. This work presents a transferred EMS for a parallel HEV via combining the reinforcement learning method and driving conditions recognition. First, the Markov decision process (MDP) and the transition probability matrix are utilized to differentiate the driving conditions. Then, reinforcement learning algorithms are formulated to achieve power split controls, in which Q-tables are tuned by current driving situations. Finally, the proposed transferred framework is estimated and validated in a parallel hybrid topology. Its advantages in computational efficiency and fuel economy are summarized and proved.
