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Titlebook: Deep Reinforcement Learning-based Energy Management for Hybrid Electric Vehicles; Yuecheng Li,Hongwen He Book 2022 Springer Nature Switzer

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Integrated Language and Study Skillsntinuous actions can exist in the same action space, making it difficult to describe them monolithically by either discrete action space or continuous action space. Taking a power-split hybrid electric bus (HEB) as an example, this chapter will introduce how to address EMS learning problems in such
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Role of Government in Adjusting EconomiesEV energy management requires stable policy improvement during training and robust online performance. To enhance the application effect on different powertrain topologies, energy management problems, and application scenarios, several DRL-based EMSs, ranging from value-based strategy learning to po
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Deep Reinforcement Learning-based Energy Management for Hybrid Electric Vehicles978-3-031-79206-9Series ISSN 2576-8107 Series E-ISSN 2576-8131
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Learning of EMSs in Continuous State Space-Discrete Action Space,riven, end-to-end learning-based EMSs, we desire not only to reduce their reliance on empirical parameter tuning, but also a higher requirement for its data mining capability, i.e., the energy-saving control schemes should be learned quickly from multidimensional environmental information. The DQN m
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Learning of EMSs in Continuous State-Continuous Action Space,ol actions. For such problems, traditional optimization methods usually adopt discretization solutions, but their application scenarios and computational amount are vulnerable to dimensional issues. The study of continuous energy management methods that can directly search for the optimal policy in
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Learning of EMSs in Discrete-Continuous Hybrid Action Space,ntinuous actions can exist in the same action space, making it difficult to describe them monolithically by either discrete action space or continuous action space. Taking a power-split hybrid electric bus (HEB) as an example, this chapter will introduce how to address EMS learning problems in such
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