Fractional Order Intelligent Modeling for Lithium-Ion Batteries - Theory and Practice

This book focuses on fractional order (non-integer order) modeling (FOM) techniques coupled with deep neural network-based intelligent modeling methods for lithium-ion batteries (LIBs) and battery management systems (BMS) in general. It provides the first one-stop resource on FOM for LIBs with case studies using real operational data sets.

List of contents

1. Introduction 2. A Review on Fractional-Order Modeling 3. Fractional-order Algorithms for Battery State Estimation 4. Fractional-order Algorithms for Battery Capacity Estimation 5. Intelligent Modeling with Smart Sensors for Battery 6. Perspectives on Intelligent Fractional-Order Modeling 7. Conclusions and Take-home Messages

About the author

YaNan Wang is currently an assistant professor and a member of Low-carbon Powertrain Systems Research Lab at Beijing University of Technology, China. Her research focuses on AI-driven battery intelligent management and safety evaluation for power batteries, addressing critical issues such as fast degradation and fault diagnosis.
YangQuan Chen is a professor at the University of California Merced, US. His research interests include mechatronics for sustainability, digital twins, small multi-UAV, applied fractional calculus. His recent publication with CRC Press includes Fractional Calculus for Skeptics I: The Fractal Paradigm.

Product details

• Authors: YangQuan Chen, YaNan Wang, Yanan Chen Wang
• Publisher: Taylor & Francis Ltd.
• Languages: English
• Product format: Hardback
• Released: 04.11.2025
• EAN: 9781041132691
• ISBN: 978-1-0-4113269-1
• No. of pages: 135
• Series: Fractional Order Thinking in Exploring the Frontiers of STEM
• Subjects: Natural sciences, medicine, IT, technology > Physics, astronomy > Miscellaneous; TRANSPORTATION / General, ENERGY, MATHEMATICS / Applied, SCIENCE / Energy, MATHEMATICS / Calculus, TECHNOLOGY & ENGINEERING / Power Resources / Electrical, Electrical Engineering, Applied mathematics, Electronics engineering, Transport technology and trades, COMPUTERS / Data Science / Neural Networks, Transport Technology & Trades, Electric vehicles / alternative fuel vehicles, Neural networks and fuzzy systems, Battery technology / fuel cell technology