Isbn: 9783031003752 - reinforcement learning-enabled intelligent energy management for hybrid electric vehicles (15 risultati)

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  • Lingua: Inglese

    Editore: Springer, 2019

    3031003756 / 9783031003752

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    Editore: Springer, 2019

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    Editore: Springer, 2019

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  • Lingua: Inglese

    Editore: Springer, 2019

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  • Lingua: Inglese

    Editore: Springer, 2019

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  • Lingua: Inglese

    Editore: Springer, 2019

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  • Lingua: Inglese

    Editore: Springer, 2019

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  • Lingua: Inglese

    Editore: Springer, 2019

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    Da: AHA-BUCH GmbH, Einbeck, GermaniaAHA-BUCH GmbH

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    Taschenbuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - Powertrain electrification, fuel decarburization, and energy diversification are techniques that are spreading all over the world, leading to cleaner and more efficient vehicles. Hybrid electric vehicles (HEVs) are considered a promising technology today to address growing air pollution and energy deprivation. To realize these gains and still maintain good performance, it is critical for HEVs to have sophisticated energy management systems. Supervised by such a system, HEVs could operate in different modes, such as full electric mode and power split mode. Hence, researching and constructing advanced energy management strategies (EMSs) is important for HEVs performance. There are a few books about rule- and optimization-based approaches for formulating energy management systems. Most of them concern traditional techniques and their efforts focus on searching for optimal control policies offline. There is still much room to introduce learning-enabled energy management systems foundedin artificial intelligence and their real-time evaluation and application.In this book, a series hybrid electric vehicle was considered as the powertrain model, to describe and analyze a reinforcement learning (RL)-enabled intelligent energy management system. The proposed system can not only integrate predictive road information but also achieve online learning and updating. Detailed powertrain modeling, predictive algorithms, and online updating technology are involved, and evaluation and verification of the presented energy management system is conducted and executed.…

  • Lingua: Inglese

    Editore: Springer, 2019

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    Da: Brook Bookstore On Demand, Napoli, NA, ItaliaBrook Bookstore On Demand

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  • Lingua: Inglese

    Editore: Springer International Publishing Sep 2019, 2019

    3031003756 / 9783031003752

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    Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, GermaniaBuchWeltWeit Ludwig Meier e.K.

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    Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Powertrain electrification, fuel decarburization, and energy diversification are techniques that are spreading all over the world, leading to cleaner and more efficient vehicles. Hybrid electric vehicles (HEVs) are considered a promising technology today to address growing air pollution and energy deprivation. To realize these gains and still maintain good performance, it is critical for HEVs to have sophisticated energy management systems. Supervised by such a system, HEVs could operate in different modes, such as full electric mode and power split mode. Hence, researching and constructing advanced energy management strategies (EMSs) is important for HEVs performance. There are a few books about rule- and optimization-based approaches for formulating energy management systems. Most of them concern traditional techniques and their efforts focus on searching for optimal control policies offline. There is still much room to introduce learning-enabled energy management systems founded in artificial intelligence and their real-time evaluation and application.In this book, a series hybrid electric vehicle was considered as the powertrain model, to describe and analyze a reinforcement learning (RL)-enabled intelligent energy management system. The proposed system can not only integrate predictive road information but also achieve online learning and updating. Detailed powertrain modeling, predictive algorithms, and online updating technology are involved, and evaluation and verification of the presented energy management system is conducted and executed. 100 pp. Englisch.…

  • Lingua: Inglese

    Editore: Springer, 2019

    3031003756 / 9783031003752

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  • Lingua: Inglese

    Editore: Springer, 2019

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  • Lingua: Inglese

    Editore: Springer, Berlin|Springer International Publishing|Morgan & Claypool|Springer, 2019

    3031003756 / 9783031003752

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    Da: moluna, Greven, Germaniamoluna

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    Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Powertrain electrification, fuel decarburization, and energy diversification are techniques that are spreading all over the world, leading to cleaner and more efficient vehicles. Hybrid electric vehicles (HEVs) are considered a promising technology today.…

  • Lingua: Inglese

    Editore: Springer, Palgrave Macmillan Sep 2019, 2019

    3031003756 / 9783031003752

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    Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germaniabuchversandmimpf2000

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    Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Powertrain electrification, fuel decarburization, and energy diversification are techniques that are spreading all over the world, leading to cleaner and more efficient vehicles. Hybrid electric vehicles (HEVs) are considered a promising technology today to address growing air pollution and energy deprivation. To realize these gains and still maintain good performance, it is critical for HEVs to have sophisticated energy management systems. Supervised by such a system, HEVs could operate in different modes, such as full electric mode and power split mode. Hence, researching and constructing advanced energy management strategies (EMSs) is important for HEVs performance. There are a few books about rule- and optimization-based approaches for formulating energy management systems. Most of them concern traditional techniques and their efforts focus on searching for optimal control policies offline. There is still much room to introduce learning-enabled energy management systems foundedin artificial intelligence and their real-time evaluation and application.In this book, a series hybrid electric vehicle was considered as the powertrain model, to describe and analyze a reinforcement learning (RL)-enabled intelligent energy management system. The proposed system can not only integrate predictive road information but also achieve online learning and updating. Detailed powertrain modeling, predictive algorithms, and online updating technology are involved, and evaluation and verification of the presented energy management system is conducted and executed.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 100 pp. Englisch.…