A Comprehensive Review on Lithium-Ion Battery Lifetime Prediction and Aging Mechanism Analysis
Lithium-ion batteries experience degradation with each cycle, and while aging-related deterioration cannot be entirely prevented, understanding its underlying mechanisms is crucial to slowing it down. The aging processes in these batteries are complex and influenced by factors such as battery chemis...
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MDPI AG
2025-03-01
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| author | Seyed Saeed Madani Yasmin Shabeer François Allard Michael Fowler Carlos Ziebert Zuolu Wang Satyam Panchal Hicham Chaoui Saad Mekhilef Shi Xue Dou Khay See Kaveh Khalilpour |
| author_facet | Seyed Saeed Madani Yasmin Shabeer François Allard Michael Fowler Carlos Ziebert Zuolu Wang Satyam Panchal Hicham Chaoui Saad Mekhilef Shi Xue Dou Khay See Kaveh Khalilpour |
| author_sort | Seyed Saeed Madani |
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| description | Lithium-ion batteries experience degradation with each cycle, and while aging-related deterioration cannot be entirely prevented, understanding its underlying mechanisms is crucial to slowing it down. The aging processes in these batteries are complex and influenced by factors such as battery chemistry, electrochemical reactions, and operational conditions. Key stressors including depth of discharge, charge/discharge rates, cycle count, and temperature fluctuations or extreme temperature conditions play a significant role in accelerating degradation, making them central to aging analysis. Battery aging directly impacts power, energy density, and reliability, presenting a substantial challenge to extending battery lifespan across diverse applications. This paper provides a comprehensive review of methods for modeling and analyzing battery aging, focusing on essential indicators for assessing the health status of lithium-ion batteries. It examines the principles of battery lifespan modeling, which are vital for applications such as portable electronics, electric vehicles, and grid energy storage systems. This work aims to advance battery technology and promote sustainable resource use by understanding the variables influencing battery durability. Synthesizing a wide array of studies on battery aging, the review identifies gaps in current methodologies and highlights innovative approaches for accurate remaining useful life (RUL) estimation. It introduces emerging strategies that leverage advanced algorithms to improve predictive model precision, ultimately driving enhancements in battery performance and supporting their integration into various systems, from electric vehicles to renewable energy infrastructures. |
| format | Article |
| id | doaj-art-586c8ce544664c6d840763ea52e35ebb |
| institution | OA Journals |
| issn | 2313-0105 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
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| series | Batteries |
| spelling | doaj-art-586c8ce544664c6d840763ea52e35ebb2025-08-20T02:17:20ZengMDPI AGBatteries2313-01052025-03-0111412710.3390/batteries11040127A Comprehensive Review on Lithium-Ion Battery Lifetime Prediction and Aging Mechanism AnalysisSeyed Saeed Madani0Yasmin Shabeer1François Allard2Michael Fowler3Carlos Ziebert4Zuolu Wang5Satyam Panchal6Hicham Chaoui7Saad Mekhilef8Shi Xue Dou9Khay See10Kaveh Khalilpour11Department of Chemical Engineering, University of Waterloo, Waterloo, ON N2L 3G1, CanadaDepartment of Chemical Engineering, University of Waterloo, Waterloo, ON N2L 3G1, CanadaCentre Énergie, Matériaux et Télécommunications (EMT), Institut National de la Recherche Scientifique (INRS), Varennes, QC J3X 1P7, CanadaDepartment of Chemical Engineering, University of Waterloo, Waterloo, ON N2L 3G1, CanadaInstitute of Applied Materials-Applied Materials Physics, Karlsruhe Institute of Technology, 76131 Karlsruhe, GermanyCentre for Efficiency and Performance Engineering, University of Huddersfield, Huddersfield HD1 3DH, UKDepartment of Chemical Engineering, University of Waterloo, Waterloo, ON N2L 3G1, CanadaDepartment of Electronics, Carleton University, Ottawa, ON K1S 5B6, CanadaSchool of Science, Computing and Engineering Technologies, Swinburne University of Technology, Melbourne, VIC 3122, AustraliaInstitute for Superconducting & Electronic Materials (ISEM), Australian Institute for Innovative Materials (AIIM), University of Wollongong, Wollongong, NSW 2500, AustraliaInstitute of Energy Materials Science, University of Shanghai for Science and Technology, Shanghai 200093, ChinaFaculty of Engineering and IT, University of Technology Sydney, Ultimo, NSW 2007, AustraliaLithium-ion batteries experience degradation with each cycle, and while aging-related deterioration cannot be entirely prevented, understanding its underlying mechanisms is crucial to slowing it down. The aging processes in these batteries are complex and influenced by factors such as battery chemistry, electrochemical reactions, and operational conditions. Key stressors including depth of discharge, charge/discharge rates, cycle count, and temperature fluctuations or extreme temperature conditions play a significant role in accelerating degradation, making them central to aging analysis. Battery aging directly impacts power, energy density, and reliability, presenting a substantial challenge to extending battery lifespan across diverse applications. This paper provides a comprehensive review of methods for modeling and analyzing battery aging, focusing on essential indicators for assessing the health status of lithium-ion batteries. It examines the principles of battery lifespan modeling, which are vital for applications such as portable electronics, electric vehicles, and grid energy storage systems. This work aims to advance battery technology and promote sustainable resource use by understanding the variables influencing battery durability. Synthesizing a wide array of studies on battery aging, the review identifies gaps in current methodologies and highlights innovative approaches for accurate remaining useful life (RUL) estimation. It introduces emerging strategies that leverage advanced algorithms to improve predictive model precision, ultimately driving enhancements in battery performance and supporting their integration into various systems, from electric vehicles to renewable energy infrastructures.https://www.mdpi.com/2313-0105/11/4/127lithium-ion batteriesbattery lifetime modelingaging mechanismsstate of health (SOH)RULdegradation factors |
| spellingShingle | Seyed Saeed Madani Yasmin Shabeer François Allard Michael Fowler Carlos Ziebert Zuolu Wang Satyam Panchal Hicham Chaoui Saad Mekhilef Shi Xue Dou Khay See Kaveh Khalilpour A Comprehensive Review on Lithium-Ion Battery Lifetime Prediction and Aging Mechanism Analysis Batteries lithium-ion batteries battery lifetime modeling aging mechanisms state of health (SOH) RUL degradation factors |
| title | A Comprehensive Review on Lithium-Ion Battery Lifetime Prediction and Aging Mechanism Analysis |
| title_full | A Comprehensive Review on Lithium-Ion Battery Lifetime Prediction and Aging Mechanism Analysis |
| title_fullStr | A Comprehensive Review on Lithium-Ion Battery Lifetime Prediction and Aging Mechanism Analysis |
| title_full_unstemmed | A Comprehensive Review on Lithium-Ion Battery Lifetime Prediction and Aging Mechanism Analysis |
| title_short | A Comprehensive Review on Lithium-Ion Battery Lifetime Prediction and Aging Mechanism Analysis |
| title_sort | comprehensive review on lithium ion battery lifetime prediction and aging mechanism analysis |
| topic | lithium-ion batteries battery lifetime modeling aging mechanisms state of health (SOH) RUL degradation factors |
| url | https://www.mdpi.com/2313-0105/11/4/127 |
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