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Advanced Hybrid-based State of Charge Method for Lithium-ion Battery Management

£62.99

Advanced Hybrid-based State of Charge Method for Lithium-ion Battery Management

By: Paul Takyi-Aninakwa, Shunli Wang , Qi Huang, Yujie Wang

£62.99

This book presents a groundbreaking approach to estimating the state of charge (SOC) of lithium-ion batteries. This method combines neural networks and Kalman filtering to enhance accuracy and adapt to battery aging, paving the way for improved battery management systems.

In the pursuit of efficient and reliable energy storage solutions, this book presents a groundbreaking approach to estimating the state of charge (SOC) of lithium-ion…

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Authors: Paul Takyi-Aninakwa, Qi Huang, Shunli Wang, Yujie Wang ISBN: 1-0364-4807-X Categories: Physical Sciences, Energy and Power, Renewable Energy and Alternative Technologies, Electrical and Electronic Engineering

In the pursuit of efficient and reliable energy storage solutions, this book presents a groundbreaking approach to estimating the state of charge (SOC) of lithium-ion batteries. This innovative method combines the strengths of neural networks and Kalman filtering techniques to enhance accuracy and adaptability in SOC estimation, addressing the challenges posed by varying operating conditions and battery aging. By leveraging advanced modeling technique, this research provides a comprehensive analysis of battery behavior, paving the way for improved battery management systems that optimize performance and extend lifespan. As the demand for sustainable energy solutions grows, this hybrid SOC methodology stands at the forefront of battery technology, offering significant implications for electric vehicles, renewable energy systems, and beyond. It explores how this advanced approach can transform the future of battery management and contribute to a greener tomorrow.

Paul Takyi-Aninakwa is a postdoctoral research fellow at the School of Materials and Chemistry at the Southwest University of Science and Technology, China. He did his PhD in Control Science and Engineering at the Southwest University of Science and Technology in 2024, and has over 40 Science Citation Index publications.

Shunli Wang obtained his doctoral degree from the Southwest University of Science and Technology in 2018, and went to Aalborg University, Denmark for further studies in 2020. He is currently a professor of Control Science and Engineering.

Qi Huang is a professor and president of the Southwest University of Science and Technology. He has undertaken more than 20 projects, published more than 300 academic research papers, and applied for more than 100 patents.

Yujie Wang is an associate professor with the Department of Automation at the University of Science and Technology of China. He received his PhD degree in Control Science and Engineering from the University of Science and Technology of China in 2017.

Hardback

ISBN: 1-0364-4807-X
ISBN13: 978-1-0364-4807-3
Date of Publication: 2025-09-01

Ebook

ISBN: 1-0364-4808-8
ISBN13: 978-1-0364-4808-0
Date of Publication: 2025-09-01

Categories: Physical Sciences, Energy and Power, Renewable Energy and Alternative Technologies, Electrical and Electronic Engineering

Pages: 115

Subject Codes:

BIC: THX, TJ, TJF
THEMA: THV, TJ, TJF

Pages: 115

Meet The Author

Qi Huang

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From £29.99

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9781527539594-2

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By: Shunli Wang , Yujie Wang, Dan Deng, Carlos Fernandez, Josep M. Guerrero

From £32.99

This book proposes a method for estimating lithium battery state of charge (SOC) by fusing deep learning and filtering algorithms. It uses BiLSTM neural networks to overcome the limitations of standard LSTM, resulting in improved feature extraction and greater accuracy.

9781527539594-2

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By: Qi Huang, Shunli Wang , Yujie Wang, Chao Wang, Carlos Fernandez, Josep M. Guerrero

From £29.99

This book focuses on the co-estimation strategies of State-of-Charge (SOC) and State-of-Health (SOH) for lithium-ion batteries. It proposes a collaborative optimization strategy based on neural networks, providing technical references for scholars and engineers.

Paul Takyi-Aninakwa

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