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Optimized energy management strategi...

Aggarwal, Puneet Kumar.

Optimized energy management strategies for electric vehicles

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Optimized energy management strategies for electric vehiclesParita Jain, Puneet Kumar Aggarwal, Mandeep Singh, Sushil Kumar Singh, Shailendra Rathore, editors.
其他作者:	Rathore, Shailendra.
出版者:	Hershey, Pennsylvania :IGI Global Scientific Publishing,2025.
面頁冊數:	1 online resource (xxiii, 389 p.)
標題:	Electric vehiclesBatteries.
電子資源:	http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/979-8-3693-6844-2
ISBN:	9798369368466$q(ebook)

Optimized energy management strategies for electric vehicles
Optimized energy management strategies for electric vehicles[electronic resource] /Parita Jain, Puneet Kumar Aggarwal, Mandeep Singh, Sushil Kumar Singh, Shailendra Rathore, editors. - Hershey, Pennsylvania :IGI Global Scientific Publishing,2025. - 1 online resource (xxiii, 389 p.) - Advances in mechatronics and mechanical engineering (AMME) book series. - Advances in mechatronics and mechanical engineering (AMME) book series..

Includes bibliographical references and index.

Preface -- Section 1. Electric Mobility and Urban Transportation -- Chapter 1. Electric Buses for Urban Transport Systems -- Chapter 2. Communication Requirements and Performance Metrics for Electric Vehicle Charging: A Comprehensive Review -- Section 2. Battery Systems and Energy Storage Solutions -- Chapter 3. Advanced Battery Cooling Systems for Enhanced Efficiency and Longevity -- Chapter 4. Life Cycle Assessment of Emerging Battery Technologies for Electric Vehicles -- Chapter 5. Advancements in Renewable Energy Harvesting for EV Charging Infrastructure -- Chapter 6. Sustainable Energy Solutions for Charging Electric Vehicles by Integrating Renewable Sources -- Section 3. Energy Management Strategies and Optimization Techniques -- Chapter 7. An Efficient Energy Management Strategy to Reduce the Comprehensive Cost of Electric Vehicles by Recent Advances -- Chapter 8. A Smart Traffic Management System for Improving EV Range Using Machine Learning Algorithms -- Chapter 9. Optimizing Sustainability: Harnessing AI for Personalized Electric Vehicle Energy Management -- Section 4. Decision-Making Models and Advanced Technologies -- Chapter 10. A Soft Computing-Based MCDM Approach Selection of Best Electric Vehicles Towards SDG- 7 -- Chapter 11. Autonomous Vehicle Performance Prediction System Using Support Vector Machine -- Chapter 12. Securing Vehicle-to-Grid Communications: A Cyber-Physical Approach -- Chapter 13. Innovations in Energy Management for Enhanced Efficiency and Sustainability: Advancing Electric Mobility -- Compilation of References -- About the Contributors -- Index.

Optimized Energy Management Strategies for Electric Vehicles delves into the intricate landscape of energy management in electric vehicles (EVs), offering a comprehensive exploration of cutting-edge methodologies and technologies. From advanced algorithms to real-time data analytics, this book presents a rigorous examination of the most efficient and intelligent solutions for optimizing energy consumption and enhancing overall performance in EVs. This book covers topics such as electrical engineering, automotive engineering, and sustainability, and is a useful resource for automotive engineers, electrical engineers, policymakers, urban planners, business owners, academicians, scientists, and researchers.

Mode of access: World Wide Web.

ISBN: 9798369368466$q(ebook)Subjects--Topical Terms:

712021
Electric vehicles
--Batteries.Subjects--Index Terms:

Automotive Engineering.Index Terms--Genre/Form:

214472
Electronic books.

LC Class. No.: TL220 / .O68 2025eb

Dewey Class. No.: 629.2

Optimized energy management strategies for electric vehicles
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504 $a Includes bibliographical references and index.
505 0 $a Preface -- Section 1. Electric Mobility and Urban Transportation -- Chapter 1. Electric Buses for Urban Transport Systems -- Chapter 2. Communication Requirements and Performance Metrics for Electric Vehicle Charging: A Comprehensive Review -- Section 2. Battery Systems and Energy Storage Solutions -- Chapter 3. Advanced Battery Cooling Systems for Enhanced Efficiency and Longevity -- Chapter 4. Life Cycle Assessment of Emerging Battery Technologies for Electric Vehicles -- Chapter 5. Advancements in Renewable Energy Harvesting for EV Charging Infrastructure -- Chapter 6. Sustainable Energy Solutions for Charging Electric Vehicles by Integrating Renewable Sources -- Section 3. Energy Management Strategies and Optimization Techniques -- Chapter 7. An Efficient Energy Management Strategy to Reduce the Comprehensive Cost of Electric Vehicles by Recent Advances -- Chapter 8. A Smart Traffic Management System for Improving EV Range Using Machine Learning Algorithms -- Chapter 9. Optimizing Sustainability: Harnessing AI for Personalized Electric Vehicle Energy Management -- Section 4. Decision-Making Models and Advanced Technologies -- Chapter 10. A Soft Computing-Based MCDM Approach Selection of Best Electric Vehicles Towards SDG- 7 -- Chapter 11. Autonomous Vehicle Performance Prediction System Using Support Vector Machine -- Chapter 12. Securing Vehicle-to-Grid Communications: A Cyber-Physical Approach -- Chapter 13. Innovations in Energy Management for Enhanced Efficiency and Sustainability: Advancing Electric Mobility -- Compilation of References -- About the Contributors -- Index.
520 3 $a Optimized Energy Management Strategies for Electric Vehicles delves into the intricate landscape of energy management in electric vehicles (EVs), offering a comprehensive exploration of cutting-edge methodologies and technologies. From advanced algorithms to real-time data analytics, this book presents a rigorous examination of the most efficient and intelligent solutions for optimizing energy consumption and enhancing overall performance in EVs. This book covers topics such as electrical engineering, automotive engineering, and sustainability, and is a useful resource for automotive engineers, electrical engineers, policymakers, urban planners, business owners, academicians, scientists, and researchers.
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