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AI techniques for renewable source integration and battery charging methods in electric vehicle applications /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	AI techniques for renewable source integration and battery charging methods in electric vehicle applications // [edited by] S. Angalaeswari, T. Deepa, L. Ashok Kumar.
remainder title:	Artificial intelligence techniques for renewable source integration and battery charging methods in electric vehicle applications
other author:	Kumar, L. Ashok,
Description:	27 PDFs (288 pages) :illustrations (chiefly color) :
Subject:	Battery management systems. -
Online resource:	http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/978-1-6684-8816-4
ISBN:	9781668488188

AI techniques for renewable source integration and battery charging methods in electric vehicle applications /
AI techniques for renewable source integration and battery charging methods in electric vehicle applications /Artificial intelligence techniques for renewable source integration and battery charging methods in electric vehicle applications[edited by] S. Angalaeswari, T. Deepa, L. Ashok Kumar. - 27 PDFs (288 pages) :illustrations (chiefly color)

Includes bibliographical references and index.

Section 1. Electric vehicle batteries. Chapter 1. Peak load reduction via electric car batteries: V2G potential in winter conditions in Kirsehir City in 2030 ; Chapter 2. Swappable battery data management system ; Chapter 3. A deep learning approach for predicting the remaining useful lifetime of lithium-ion batteries using 1-D convolutional neural networks -- Section 2. Electric vehicle charging. Chapter 4. Wireless power transfer for high end and low end EV cars ; Chapter 5. RE-based multilevel inverter for EV charging ; Chapter 6. Autonomous vehicles using opencv and python with wireless charging ; Chapter 7. Software communication interface for OCPP-based DC fast charging stations -- Section 3. Renewable energy: monitoring and storage. Chapter 8. Renewable energy resources and their types ; Chapter 9. A deep learning-based solar photovoltaic emulator ; Chapter 10. IoT-based smart solar energy monitoring system ; Chapter 11. Hybrid energy storage systems for renewable energy integration and application -- Section 4. Recent advancements. Chapter 12. Artificial intelligence-based approaches in vehicular power energy application ; Chapter 13. Environmental economic load dispatch considering demand response using a new heuristic optimization algorithm ; Chapter 14. Implementation of AI techniques for tuning of controller parameters in a nonlinear system.

Restricted to subscribers or individual electronic text purchasers.

"AI Techniques for Renewable Source Integration and Battery Charging Methods in Electric Vehicle Applications focuses on artificial intelligence techniques for the evolving power system field, electric vehicle market, energy storage elements, and renewable energy source integration as distributed generators. Covering key topics such as deep learning, artificial intelligence, and smart solar energy, this premier reference source is ideal for environmentalists, computer scientists, industry professionals, researchers, academicians, scholars, practitioners, instructors, and students"--

Mode of access: World Wide Web.

ISBN: 9781668488188

Standard No.: 10.4018/978-1-6684-8816-4doiSubjects--Topical Terms:

1427645
Battery management systems.
Subjects--Index Terms:

Artificial Intelligence.

LC Class. No.: TL220 / .A77 2023e

Dewey Class. No.: 629.22/93028563

AI techniques for renewable source integration and battery charging methods in electric vehicle applications /
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245 0 0 $a AI techniques for renewable source integration and battery charging methods in electric vehicle applications / $c [edited by] S. Angalaeswari, T. Deepa, L. Ashok Kumar.
246 3 0 $a Artificial intelligence techniques for renewable source integration and battery charging methods in electric vehicle applications
264 1 $a Hershey, Pennsylvania (701 E. Chocolate Avenue, Hershey, Pennsylvania, 17033, USA) : $b IGI Global, $c 2023.
300 $a 27 PDFs (288 pages) : $b illustrations (chiefly color)
336 $a text $2 rdacontent
337 $a electronic $2 isbdmedia
338 $a online resource $2 rdacarrier
504 $a Includes bibliographical references and index.
505 0 $a Section 1. Electric vehicle batteries. Chapter 1. Peak load reduction via electric car batteries: V2G potential in winter conditions in Kirsehir City in 2030 ; Chapter 2. Swappable battery data management system ; Chapter 3. A deep learning approach for predicting the remaining useful lifetime of lithium-ion batteries using 1-D convolutional neural networks -- Section 2. Electric vehicle charging. Chapter 4. Wireless power transfer for high end and low end EV cars ; Chapter 5. RE-based multilevel inverter for EV charging ; Chapter 6. Autonomous vehicles using opencv and python with wireless charging ; Chapter 7. Software communication interface for OCPP-based DC fast charging stations -- Section 3. Renewable energy: monitoring and storage. Chapter 8. Renewable energy resources and their types ; Chapter 9. A deep learning-based solar photovoltaic emulator ; Chapter 10. IoT-based smart solar energy monitoring system ; Chapter 11. Hybrid energy storage systems for renewable energy integration and application -- Section 4. Recent advancements. Chapter 12. Artificial intelligence-based approaches in vehicular power energy application ; Chapter 13. Environmental economic load dispatch considering demand response using a new heuristic optimization algorithm ; Chapter 14. Implementation of AI techniques for tuning of controller parameters in a nonlinear system.
506 $a Restricted to subscribers or individual electronic text purchasers.
520 $a "AI Techniques for Renewable Source Integration and Battery Charging Methods in Electric Vehicle Applications focuses on artificial intelligence techniques for the evolving power system field, electric vehicle market, energy storage elements, and renewable energy source integration as distributed generators. Covering key topics such as deep learning, artificial intelligence, and smart solar energy, this premier reference source is ideal for environmentalists, computer scientists, industry professionals, researchers, academicians, scholars, practitioners, instructors, and students"-- $c Provided by publisher.
530 $a Also available in print.
538 $a Mode of access: World Wide Web.
588 $a Description based on title screen (IGI Global, viewed 03/10/2023).
650 0 $a Battery management systems. $3 1427645
650 0 $a Renewable energy sources. $3 558536
650 0 $a Artificial intelligence $x Engineering applications. $3 889372
650 0 $a Electric vehicles $x Batteries. $3 981658
653 $a Artificial Intelligence.
653 $a Deep Learning.
653 $a Electric Car Batteries.
653 $a Hybrid Energy Storage Systems.
653 $a Renewable Energy.
653 $a Renewable Energy Resources.
653 $a Smart Solar Energy.
653 $a Software Communication Interfaces.
653 $a Solar Photovoltaic Emulator.
653 $a Swappable Battery Data Management System.
653 $a Wireless Power Transfer.
700 1 $a Kumar, L. Ashok, $e editor. $3 1450098
700 1 $a Deepa, T., $d 1978- $e editor. $3 1450097
700 1 $a Angalaeswari, S., $d 1981- $e editor. $3 1450096
710 2 $a IGI Global, $e publisher. $3 1057167
776 0 8 $i Print version: $z 1668488167 $z 9781668488164
856 4 1 $3 Chapter PDFs via platform: $u http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/978-1-6684-8816-4