標(biāo)題: Titlebook: Deep Reinforcement Learning-based Energy Management for Hybrid Electric Vehicles; Yuecheng Li,Hongwen He Book 2022 Springer Nature Switzer [打印本頁] 作者: obesity 時(shí)間: 2025-3-21 19:28
書目名稱Deep Reinforcement Learning-based Energy Management for Hybrid Electric Vehicles影響因子(影響力)
書目名稱Deep Reinforcement Learning-based Energy Management for Hybrid Electric Vehicles影響因子(影響力)學(xué)科排名
書目名稱Deep Reinforcement Learning-based Energy Management for Hybrid Electric Vehicles網(wǎng)絡(luò)公開度
書目名稱Deep Reinforcement Learning-based Energy Management for Hybrid Electric Vehicles網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Deep Reinforcement Learning-based Energy Management for Hybrid Electric Vehicles被引頻次
書目名稱Deep Reinforcement Learning-based Energy Management for Hybrid Electric Vehicles被引頻次學(xué)科排名
書目名稱Deep Reinforcement Learning-based Energy Management for Hybrid Electric Vehicles年度引用
書目名稱Deep Reinforcement Learning-based Energy Management for Hybrid Electric Vehicles年度引用學(xué)科排名
書目名稱Deep Reinforcement Learning-based Energy Management for Hybrid Electric Vehicles讀者反饋
書目名稱Deep Reinforcement Learning-based Energy Management for Hybrid Electric Vehicles讀者反饋學(xué)科排名
作者: Facet-Joints 時(shí)間: 2025-3-21 21:45 作者: 技術(shù) 時(shí)間: 2025-3-22 04:01
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作者: limber 時(shí)間: 2025-3-22 05:47 作者: 擴(kuò)音器 時(shí)間: 2025-3-22 09:50
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 作者: Irremediable 時(shí)間: 2025-3-22 15:31 作者: Irremediable 時(shí)間: 2025-3-22 20:50 作者: 生存環(huán)境 時(shí)間: 2025-3-22 23:06
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.作者: erythema 時(shí)間: 2025-3-23 02:49
Deep Reinforcement Learning-based Energy Management for Hybrid Electric Vehicles作者: persistence 時(shí)間: 2025-3-23 06:40
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作者: Limerick 時(shí)間: 2025-3-23 12:57 作者: 懶鬼才會(huì)衰弱 時(shí)間: 2025-3-23 17:45
https://doi.org/10.1007/978-3-030-79241-1 the continuous energy management method, this chapter also introduces a PHEV energy management solution integrating history cumulative trip information (HCTI) to improve the EMS learning effect across a wider feasible domain of SoC.作者: anus928 時(shí)間: 2025-3-23 19:18 作者: Canopy 時(shí)間: 2025-3-24 02:04
Role of Government in Adjusting Economieswork could provide some useful clues and basic algorithmic frameworks for future study on more complex and intelligent vehicle control methods with the incorporation of multi-source sensory information.作者: 錯(cuò)事 時(shí)間: 2025-3-24 06:01 作者: 可用 時(shí)間: 2025-3-24 10:24
Learning of EMSs in Discrete-Continuous Hybrid Action Space,rain information is described, and accordingly, the influence of the multi-source information on learning-based EMSs is discussed in terms of fuel economy, strategy performance under specific driving scenarios, and the strategy decisions.作者: Nonconformist 時(shí)間: 2025-3-24 14:23 作者: Neutropenia 時(shí)間: 2025-3-24 18:27
The Value of the Developer Economytate. Meanwhile, energy consumption of the powertrain occurs simultaneously with the transition of vehicle states. This instantaneous energy (or fuel) consumption and the sum of energy (fuel) it consumes over the future will provide a criterion for judging the strategy performance. Then, a new energ作者: Myelin 時(shí)間: 2025-3-24 21:43
https://doi.org/10.1007/978-1-4842-5308-3riven, 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作者: 抱狗不敢前 時(shí)間: 2025-3-24 23:57 作者: ablate 時(shí)間: 2025-3-25 03:31
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 作者: 釘牢 時(shí)間: 2025-3-25 09:21 作者: 加強(qiáng)防衛(wèi) 時(shí)間: 2025-3-25 11:52
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作者: 破譯密碼 時(shí)間: 2025-3-25 17:38 作者: overture 時(shí)間: 2025-3-25 21:23
Deep Reinforcement Learning-based Energy Management for Hybrid Electric Vehicles978-3-031-79206-9Series ISSN 2576-8107 Series E-ISSN 2576-8131 作者: 爆米花 時(shí)間: 2025-3-26 02:10 作者: 惡心 時(shí)間: 2025-3-26 07:07 作者: 騙子 時(shí)間: 2025-3-26 08:32
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作者: 溫和女人 時(shí)間: 2025-3-26 16:29
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 作者: 品嘗你的人 時(shí)間: 2025-3-26 18:59
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 作者: instructive 時(shí)間: 2025-3-26 22:59
An Online Integration Scheme for DRL-Based EMSs,cal systems, most of the research on DRL is carried out based on simulators. The previous chapters also focus on the implementation and optimization of learning-based EMSs in a simulation environment. Currently, the developed vehicle control algorithms still need further assessments with the deploym作者: 豪華 時(shí)間: 2025-3-27 02:08 作者: adj憂郁的 時(shí)間: 2025-3-27 06:02
1615-1844 usly evolving. As it is customary in the LB Series, the results obtained in a given field are quoted as thoroughly as possible. The most relevant among them are presented in the form of figures (or tables), and comments or comparisons with other results are usually provided..978-3-662-47736-6Series ISSN 1615-1844 Series E-ISSN 1616-9522 作者: corn732 時(shí)間: 2025-3-27 12:33 作者: Vulnerable 時(shí)間: 2025-3-27 17:12
Falk Seegersehr schnell auf Schwierigkeiten. Einen überblick über die franz?sische Sozialgeschichte der letzten 200 Jahre gab es bislang nicht. Die überblicksdarstellungen von Michael ., Winfried . und Ernst . sind allgemeine Darstellungen zur franz?sischen Geschichte, die die Sozialgeschichte nur nebenbei beh作者: 象形文字 時(shí)間: 2025-3-27 21:03
