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

Record Type:	Electronic resources : 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:	Angalaeswari, S.,
Published:	Hershey, Pennsylvania :IGI Global,2023.
Description:	1 online resource (288 p.) :ill. (chiefly col.)
Subject:	Electric vehiclesBatteries.
Online resource:	http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/978-1-6684-8816-4
ISBN:	9781668488188$q(ebook)

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[electronic resource] /Artificial intelligence techniques for renewable source integration and battery charging methods in electric vehicle applications[edited by] S. Angalaeswari, T. Deepa, L. Ashok Kumar. - Hershey, Pennsylvania :IGI Global,2023. - 1 online resource (288 p.) :ill. (chiefly col.)

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.

"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 storageelements, 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, computerscientists, industry professionals, researchers, academicians, scholars, practitioners, instructors, and students"--

ISBN: 9781668488188$q(ebook)Subjects--Topical Terms:

712021
Electric vehicles
--Batteries.Index Terms--Genre/Form:

214472
Electronic books.

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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504 $a Includes bibliographical references and index.
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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 storageelements, 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, computerscientists, industry professionals, researchers, academicians, scholars, practitioners, instructors, and students"-- $c Provided by publisher.
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