Picture 1 of 1
Picture 1 of 1
Reinforcement Learning-Enabl
US $48.23
ApproximatelyAU $72.67
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Located in: Fairfield, Ohio, United States
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eBay item number:386717677145
Item specifics
- Condition
- 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 Information
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.
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.
Lc Classification Number
Tk1-9971
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.
Copyright Date
2019
Item description from the seller
Seller assumes all responsibility for this listing.
eBay item number:386717677145
Postage and handling
Item location:
Fairfield, Ohio, United States
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Item as described, good price, well packaged, arrived slightly later than hoped (not in time for Christmas ) but the order was placed in a very busy shipping period. No issue with the seller’s speed of response and sending the item. Great seller!
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The seller is one of the best there is - high quality books, reasonable prices, securely packaged.
I do, however, recommend some changes to their shipping - they show a shipper (SortHub) on eBay, but the tracking # they email is for a different carrier. It’s difficult to know where my purchase is.
This time, it was delivered a week late. In the grand scheme of things not a big deal. But it shouldn’t be this difficult to accurately track my purchase.
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