Fractional Order Intelligent Modeling for Lithium-Ion Batteries: Theory and Practice
暫譯: 鋰離子電池的分數階智能建模：理論與實踐

Wang, Yanan, Chen, Yangquan

Fractional Order Intelligent Modeling for Lithium-Ion Batteries: Theory and Practice
  • 出版商: CRC
  • 出版日期: 2025-11-04
  • 售價: $4,390
  • 貴賓價: 9.5 折 $4,171
  • 語言: 英文
  • 頁數: 135
  • 裝訂: Hardcover - also called cloth, retail trade, or trade
  • ISBN: 1041132697
  • ISBN-13: 9781041132691
  • 相關分類: Machine Learning

    • 海外代購書籍(需單獨結帳)

商品描述

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.

With the rapid growth of electric vehicles and energy storage systems, battery technology has become critical to global energy solutions. Fractional Order Intelligent Modeling for Lithium-Ion Batteries: Theory and Practice aims to provide several accurate and effective intelligent modeling algorithms for the next generation of advanced BMS. Key topics include intelligent battery modeling, fractional-order modeling, physics-informed machine learning, state estimation, and degradation analysis. By integrating AI and physics-informed machine learning techniques with fractional-order modeling methods, this book presents several innovative solutions for next-generation battery management systems.

This title will serve as an invaluable resource for researchers and advanced students in the fields of transportation, energy storage, and power systems, as well as those studying electric vehicles, control theory, machine learning, and fractional calculus-based modeling.

商品描述(中文翻譯)

本書專注於分數階（非整數階）建模（FOM）技術，並結合基於深度神經網絡的智能建模方法，針對鋰離子電池（LIBs）和電池管理系統（BMS）進行探討。它提供了有關LIBs的FOM的首個一站式資源，並包含使用實際運營數據集的案例研究。

隨著電動車和儲能系統的快速增長，電池技術已成為全球能源解決方案的關鍵。鋰離子電池的分數階智能建模：理論與實踐旨在為下一代先進的BMS提供幾種準確且有效的智能建模算法。主要主題包括智能電池建模、分數階建模、物理知識驅動的機器學習、狀態估計和劣化分析。通過將人工智慧（AI）和物理知識驅動的機器學習技術與分數階建模方法相結合，本書提出了幾種針對下一代電池管理系統的創新解決方案。

本書將成為交通、儲能和電力系統領域的研究人員和高級學生的重要資源，還適合那些研究電動車、控制理論、機器學習和基於分數微積分建模的人士。

作者簡介

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.

作者簡介(中文翻譯)

王雅南目前是中國北京工業大學的助理教授，並且是低碳動力系統研究實驗室的成員。她的研究專注於基於人工智慧的電池智能管理和動力電池的安全評估，解決快速退化和故障診斷等關鍵問題。

陳揚泉是美國加州大學默塞德分校的教授。他的研究興趣包括可持續性的機電一體化、數位雙胞胎、小型多無人機以及應用分數微積分。他最近與CRC Press合作出版的書籍包括懷疑者的分數微積分 I：分形範式。

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