A Comprehensive Review on Lithium-Ion Battery Lifetime Prediction and Aging Mechanism Analysis
Source abstract
Abstract
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.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Advanced Battery Technologies Research · Advancements in Battery Materials · Advanced Battery Materials and Technologies
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Chemical Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada
Centre Énergie, Matériaux et Télécommunications (EMT), Institut National de la Recherche Scientifique (INRS), Varennes, QC J3X 1P7, Canada
Institute of Applied Materials-Applied Materials Physics, Karlsruhe Institute of Technology, 76131 Karlsruhe, Germany
Centre for Efficiency and Performance Engineering, University of Huddersfield, Huddersfield HD1 3DH, UK
Department of Electronics, Carleton University, Ottawa, ON K1S 5B6, Canada
School of Science, Computing and Engineering Technologies, Swinburne University of Technology, Melbourne, VIC 3122, Australia
Institute for Superconducting & Electronic Materials (ISEM), Australian Institute for Innovative Materials (AIIM), University of Wollongong, Wollongong, NSW 2500, Australia
Institute of Energy Materials Science, University of Shanghai for Science and Technology, Shanghai 200093, China
Faculty of Engineering and IT, University of Technology Sydney, Ultimo, NSW 2007, Australia
Funding
European Union’s Horizon 2020 Research and Innovation Programme · Helmholtz Association
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