As electric vehicle (EV) usage increases worldwide, optimized energy management strategies become crucial for EV efficiency, range, and performance. These strategies cover a variety of techniques, including advanced battery management systems, smart charging solutions, and real-time energy consumption analytics, all aimed at enhancing the driving experience while minimizing environmental impact. By utilizing data-driven insights and innovative technologies, like machine learning and grid integration, these strategies enable EVs to operate efficiently, extend battery life, and reduce charging costs. Developing and implementing effective energy management strategies is essential for individual vehicle performance as well as the sustainable growth of electric mobility. 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.

About the Authors

Parita Jain completed her Ph.D. Degree from Amity University, Noida, and M. M.Tech. (IT) from CDAC, Noida. Having vast teaching experience of approx. 14 years. Currently, she is working as an associate professor and assistant dean (academics and autonomous) in the Department of Computer Science and Engineering of the KIET Group of Institutions, Ghaziabad. Her research interests include Software Engineering, Machine Learning, Data Mining, and Mobile Computing. She has published more than 40 research papers in various international conferences and journals.

Puneet Kumar Aggarwal completed his Ph.D. degree from Amity University, Noida, and his M.Tech. (IT) degree from CDAC, Noida. Having vast teaching experience of approx. 14 years. Currently, he is working as an associate professor in computer science and engineering-data science at ABES Engineering College, Ghaziabad. His research interests include Machine Learning, Internet of Things, Data Mining and Mobile Computing. He has published more than 45 research papers in various international conferences and journals.

Mandeep Singh is an accomplished academician and Assistant Professor in the School of Computer Science, Engineering, and Technology (SCSET) at Bennett University, Greater Noida, India. He holds a Master’s degree with Distinction in Information Security from Ambedkar Institute of Advanced Communication Technologies and Research (AIACTR) (now NSUT East Campus). With over nine years of teaching experience, he is currently pursuing a Ph.D. in Computer Science & Engineering from Dr. B.R. Ambedkar National Institute of Technology (NIT) Jalandhar, where he completed his coursework with Distinction (8.00 CGPA). Mr. Singh's expertise spans Computer Networks, Mobile Ad Hoc Networks (MANETs), Machine Learning, and Deep Learning. His research contributions include book chapters, international journals, and conference proceedings, alongside a notable track record of patent publications. Additionally, he actively reviews papers for international conferences and journals and has held special sessions at prestigious events. His dedication to excellence has earned him awards and recognition from the Institute of Industrial Development (IID) and MSME, Government of India for contributions to research and entrepreneurship.

Dr. Sushil Kumar Singh is an Associate Professor in the Department of Computer Engineering at Marwadi University, Rajkot, India. He received Ph.D. degree from Seoul National University of Science and Technology, Seoul, South Korea. He received M.Tech. Degree in Computer Science and Engineering from Uttarakhand Technical University, Dehradun, India. He also received an M.E. degree in Information Technology from Karnataka State University, Mysore, India. He has also been the lab leader of the UCS Lab at the Department of Computer Science Engineering, Seoul National University of Science and Technology, Seoul, South Korea. He has received Best Lab Leadership Award from UCS Lab for 2019-2021. He has more than 12 years of experience teaching in the field of computer science. He has published Four Books: Computer C Programming, Cyber Security, Big Data Analytics, and Mobile Computing. He has also published many high-quality papers (Q1, Top 10% JCR Rank) in international journals and conferences. He has already delivered international lectures in many countries. His research interests include Blockchain, Artificial Intelligence, Big Data, Internet of Things, Smart City Security, and CPS.

Shailendra Rathore is working as a Lecturer in Division of Cybersecurity at Abertay University, UK and has more than 2 years of working experience as a lecturer at universities based in UK. He is also an active member of the cyberQuarter team, which is a new �18m cybersecurity research and development center located at Abertay University. Prior to joining Abertay, he was a lecturer in cybersecurity at Plymouth University and was associated with the �3 million Research England-supported Cyber-SHIP Lab project. He received a Ph.D. degree from the Graduate School of Computer Science and Engineering at Seoul National University of Science and Technology, Seoul, South Korea. His broad research interests include Cybersecurity, Artificial Intelligence, Blockchain, and Machine Learning. Dr. Rathore has published his research outcomes in various top-tier international journals, including IEEE Communication Magazine, IEEE Network, IEEE Consumer Electronics, FGCS, Information Sciences, and IEEE Access. He has also served as a chair, program committee member or organizing committee chair for many international conferences and workshops, including IEEE and ACM events.

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