Predict the lifetime of lithium-ion batteries using early cycles: A review

Minxing Yang, Xiaofei Sun, Rui Liu, Lingzhi Wang, Fei Zhao and Xuesong Mei

Applied Energy, 2024, vol. 376, issue PA, No S030626192401554X

Abstract: With the rapid development of lithium-ion batteries in recent years, predicting their remaining useful life based on the early stages of cycling has become increasingly important. Accurate life prediction using early cycles (e.g., first several cycles) is crucial to rational design, optimal production, efficient management, and safe usage of advanced batteries in energy storage applications such as portable electronics, electric vehicles, and smart grids. In this review, the necessity and urgency of early-stage prediction of battery life are highlighted by systematically analyzing the primary aging mechanisms of lithium-ion batteries, and the latest fast progress on early-stage prediction is then comprehensively outlined into mechanism-guided, experience-based, data-driven, and fusion-combined approaches. The key models of each method and their typical research works are profoundly analyzed, and the pros and cons of each method are then evolved with an in-depth comparison of their prediction performances. The current challenges and future perspectives of early-stage prediction are finally addressed. This review is advantageous in fully and briefly understanding the principles, methods, development, and application of early-stage prediction of battery life and is directed to expedite research on novel, accurate, efficient, and simple theories and technologies for early-stage prediction.

Keywords: Battery life; Progress on life prediction; Mechanism-guided model; Data-driven model; Energy storage (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1016/j.apenergy.2024.124171

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