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Reinforcement Learning-Enabl
US $47.98
ApproximatelyRM 201.66
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eBay item number:386717677145
Item specifics
- Condition
- Brand New: A new, unread, unused book in perfect condition with no missing or damaged pages. See all condition definitionsopens in a new window or tab
- ISBN-13
- 9783031003752
- Type
- NA
- Publication Name
- NA
- ISBN
- 9783031003752
- Book Title
- Reinforcement Learning-Enabled Intelligent Energy Management for Hybrid Electric Vehicles
- Book Series
- Synthesis Lectures on Advances in Automotive Technology Ser.
- Original Language
- English
- Publisher
- Springer International Publishing A&G
- Item Length
- 9.3 in
- Publication Year
- 2019
- Format
- Trade Paperback
- Language
- English
- Illustrator
- Yes
- Genre
- Technology & Engineering
- Topic
- Mechanical, Electrical, Automotive
- Item Weight
- 7.3 Oz
- Item Width
- 7.5 in
- Number of Pages
- X, 90 Pages
About this product
Product Identifiers
Publisher
Springer International Publishing A&G
ISBN-10
3031003756
ISBN-13
9783031003752
eBay Product ID (ePID)
21057290109
Product Key Features
Original Language
English
Book Title
Reinforcement Learning-Enabled Intelligent Energy Management for Hybrid Electric Vehicles
Number of Pages
X, 90 Pages
Language
English
Publication Year
2019
Topic
Mechanical, Electrical, Automotive
Illustrator
Yes
Genre
Technology & Engineering
Book Series
Synthesis Lectures on Advances in Automotive Technology Ser.
Format
Trade Paperback
Dimensions
Item Weight
7.3 Oz
Item Length
9.3 in
Item Width
7.5 in
Additional Product Features
Number of Volumes
1 vol.
Table Of Content
Preface.- Introduction.- Powertrain Modeling and Reinforcement Learning.- Prediction and Updating of Driving Information.- Evaluation of Intelligent Energy Management System.- Conclusion.- References.- Author's Biography.
Synopsis
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., 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.
LC Classification Number
TK1-9971
Item description from the seller
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