Advanced Hybrid-based State of Charge Method for Lithium-ion Battery Management
Aug 2025 · Cambridge Scholars Publishing
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115
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About this ebook
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.
About the author
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.
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