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

obesity

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Facet-Joints

技術(shù)

Learning of EMSs in Continuous State Space-Discrete Action Space, and efficient learning algorithm in discrete action spaces. Therefore, to address energy management problems with continuous state—discrete action spaces, this chapter describes an energy management method based on deep Q-learning, and further conduct research on its learning stability, optimizatio

limber

擴(kuò)音器

2576-8107 ut also in generalizing previously learned rules to new scenarios without complex manually tunning. Focusing on learning-based energy management with DRL as the978-3-031-79194-9978-3-031-79206-9Series ISSN 2576-8107 Series E-ISSN 2576-8131

Irremediable

Irremediable

生存環(huán)境

Michael Hubbard,Marisol Smith,Renu Kohliion for the DRL-based EMS is described in this chapter. Because all DRL-based EMSs described in this book are represented by DNNs, they share the same hardware deployment procedure. The DRL-based EMS in Chapter 3 is utilized here for the illustration.

erythema

Deep Reinforcement Learning-based Energy Management for Hybrid Electric Vehicles

persistence

2576-8107 cern, the solution of hybrid vehicle systems has proven its value from academic research and industry applications, where energy management plays a key role in taking full advantage of hybrid electric vehicles (HEVs). There are many well-established energy management approaches, ranging from rules-b