Advancements in battery state prediction methods: A comprehensive review of data-driven approaches and degradation mechanisms

Predicting the state of lithium-ion batteries (LIBs) plays a vital role in proactively identifying aging and damage, thereby enabling timely maintenance and repairs to extend their lifespan and ultimately reduce waste while advancing the development of clean energy. This comprehensive review aims to...

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Vydáno v:Journal of power sources Ročník 659; s. 238192
Hlavní autoři: Zheng, Wentao, Wu, Dinglan, Yuan, Chenbo, Jiang, Huan, Wang, Shenghan
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier B.V 15.12.2025
Témata:
Aging mechanism
Data-driven methods
Lithium-ion battery
Physics-based models
State prediction
Lithium-ion battery
Aging mechanism
Physics-based models
State prediction
Data-driven methods
ISSN:0378-7753
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Abstract Predicting the state of lithium-ion batteries (LIBs) plays a vital role in proactively identifying aging and damage, thereby enabling timely maintenance and repairs to extend their lifespan and ultimately reduce waste while advancing the development of clean energy. This comprehensive review aims to provide an extensive overview of state prediction methods and characterization techniques, emphasizing the need for specific data-driven approaches to characterize battery degradation and establish correlations between inputs and outputs. Furthermore, the evaluation of primary anode, cathode, and separator materials in batteries is discussed. The study delves into the significance of both cycle degradation and calendar degradation, providing insights into degradation phenomena and parameters perspectives. Moreover, the roles and characteristics of physical parameters for degradation characterization and state parameters for battery operation status are analyzed. The review extensively examines the strengths and weaknesses of classic and state-of-the-art physics-based models, as well as data-driven models, highlighting critical issues and challenges related to degradation mechanisms, datasets, performance trade-offs, and optimization methods. Additionally, innovative ideas are proposed, and future development directions are discussed. •Key battery materials and their impact on degradation mechanisms are discussed.•This review examines physical parameters for battery dynamics and state parameters.•This review covers end-to-end prediction method development and future trends.•The review shows the pros and cons of physics-based, data-driven, and fusion models.
AbstractList Predicting the state of lithium-ion batteries (LIBs) plays a vital role in proactively identifying aging and damage, thereby enabling timely maintenance and repairs to extend their lifespan and ultimately reduce waste while advancing the development of clean energy. This comprehensive review aims to provide an extensive overview of state prediction methods and characterization techniques, emphasizing the need for specific data-driven approaches to characterize battery degradation and establish correlations between inputs and outputs. Furthermore, the evaluation of primary anode, cathode, and separator materials in batteries is discussed. The study delves into the significance of both cycle degradation and calendar degradation, providing insights into degradation phenomena and parameters perspectives. Moreover, the roles and characteristics of physical parameters for degradation characterization and state parameters for battery operation status are analyzed. The review extensively examines the strengths and weaknesses of classic and state-of-the-art physics-based models, as well as data-driven models, highlighting critical issues and challenges related to degradation mechanisms, datasets, performance trade-offs, and optimization methods. Additionally, innovative ideas are proposed, and future development directions are discussed. •Key battery materials and their impact on degradation mechanisms are discussed.•This review examines physical parameters for battery dynamics and state parameters.•This review covers end-to-end prediction method development and future trends.•The review shows the pros and cons of physics-based, data-driven, and fusion models.
ArticleNumber 238192
Author Yuan, Chenbo
Wu, Dinglan
Zheng, Wentao
Jiang, Huan
Wang, Shenghan
Author_xml – sequence: 1
  givenname: Wentao
  surname: Zheng
  fullname: Zheng, Wentao
  organization: Key Laboratory of Physics and Technology for Advanced Batteries (Ministry of Education), College of Physics, Jilin University, Changchun, 130012, China
– sequence: 2
  givenname: Dinglan
  surname: Wu
  fullname: Wu, Dinglan
  organization: School of Computer Science and Technology, Harbin University of Science and Technology, Harbin, 150080, China
– sequence: 3
  givenname: Chenbo
  surname: Yuan
  fullname: Yuan, Chenbo
  organization: Key Laboratory of Physics and Technology for Advanced Batteries (Ministry of Education), College of Physics, Jilin University, Changchun, 130012, China
– sequence: 4
  givenname: Huan
  surname: Jiang
  fullname: Jiang, Huan
  email: jianghuan22@mails.jlu.edu.cn
  organization: Jilin Police College, Changchun, 130012, China
– sequence: 5
  givenname: Shenghan
  orcidid: 0000-0001-9586-2997
  surname: Wang
  fullname: Wang, Shenghan
  email: shenghan@jlu.edu.cn
  organization: Key Laboratory of Physics and Technology for Advanced Batteries (Ministry of Education), College of Physics, Jilin University, Changchun, 130012, China
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Keywords Lithium-ion battery
Aging mechanism
Physics-based models
State prediction
Data-driven methods
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Snippet Predicting the state of lithium-ion batteries (LIBs) plays a vital role in proactively identifying aging and damage, thereby enabling timely maintenance and...
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SubjectTerms Aging mechanism
Data-driven methods
Lithium-ion battery
Physics-based models
State prediction
Title Advancements in battery state prediction methods: A comprehensive review of data-driven approaches and degradation mechanisms
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Volume 659
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