Predicting the lifetime of Lithium–Ion batteries: Integrated feature extraction and modeling through sequential Unsupervised-Supervised Projections (USP)

Reis, M. S.; Jiang, Benben

doi:10.1016/j.ces.2022.117510

Cited by 4 publications

(1 citation statement)

References 55 publications

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“…Moreover, battery safety is usually related to capacity. State of health (SOH) diagnosis and remaining useful life (RUL) prediction can help EVs to monitor battery health in real time and maintain the battery pack before failure [149]. However, the diversity of aging mechanisms of power batteries and the coupling of aging factors, as well as the dynamic operation of batteries in the actual use process, make the accurate life management of power batteries extremely a huge challenge.…”

Section: Battery Failure Predictionmentioning

confidence: 99%

Electrochemical Failure Results Inevitable Capacity Degradation in Li-Ion Batteries—A Review

et al. 2022

Energies

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Lithium-ion batteries (LIBs) have been widely used in mobile devices, energy storage power stations, medical equipment, and other fields, became an indispensable technological product in modern society. However, the capacity degradation of LIBs limits their long-term deployment, which is not conducive to saving resources. What is more, it will lead to safety problems when the capacity of the battery is degraded. Failure of the battery is a key issue in the research and application of LIBs. Faced with the problem of capacity degradation, various aspects of LIBs have been studied. This paper reviews the electrochemical degradation mechanism of LIBs’ life fade, detection technologies for battery failure, methods to regulate battery capacity degradation, and battery lifetime prognostics. Finally, the development trend and potential challenges of battery capacity degradation research are prospected. All the key insights from this review are expected to advance the research on capacity fading and lifetime prediction techniques for LIBs.

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Section: Battery Failure Predictionmentioning

confidence: 99%

Electrochemical Failure Results Inevitable Capacity Degradation in Li-Ion Batteries—A Review

et al. 2022

Energies

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Kinetic resolution of thermal runaway for lithium-ion batteries: A Gaussian surrogate-assisted separate optimization approach

Feng

Wang²,

Shen

et al. 2023

Chemical Engineering Science

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Battery health prognostics based on improved incremental capacity using a hybrid grey modeling and Gaussian process regression

Li,

Xie

2024

Energy

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Predicting the lifetime of Lithium–Ion batteries: Integrated feature extraction and modeling through sequential Unsupervised-Supervised Projections (USP)

Cited by 4 publications

References 55 publications

Electrochemical Failure Results Inevitable Capacity Degradation in Li-Ion Batteries—A Review

Electrochemical Failure Results Inevitable Capacity Degradation in Li-Ion Batteries—A Review

Kinetic resolution of thermal runaway for lithium-ion batteries: A Gaussian surrogate-assisted separate optimization approach

Battery health prognostics based on improved incremental capacity using a hybrid grey modeling and Gaussian process regression

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