A review of data-driven whole-life state of health prediction for lithium-ion batteries: Data preprocessing, aging characteristics, algorithms, and future challenges

Xie, Yanxin; Wang, Shunli; Zhang, Gexiang; Takyi-Aninakwa, Paul; Fernandez, Carlos; Blaabjerg, Frede

doi:10.1016/j.jechem.2024.06.017

Journal of Energy Chemistry

2024

DOI: 10.1016/j.jechem.2024.06.017

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A review of data-driven whole-life state of health prediction for lithium-ion batteries: Data preprocessing, aging characteristics, algorithms, and future challenges

Yanxin Xie,

Shunli Wang,

Gexiang Zhang

et al.

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Cited by 9 publications

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A review of data-driven whole-life state of health prediction for lithium-ion batteries: Data preprocessing, aging characteristics, algorithms, and future challenges

Cited by 9 publications

References 108 publications

Research on the impact of lithium battery ageing cycles on a data-driven lithium battery model

Research on the impact of lithium battery ageing cycles on a data-driven lithium battery model

A review of equivalent-circuit model, degradation characteristics and economics of Li-ion battery energy storage system for grid applications

Detecting abnormality of battery decline for unbalanced samples via ensemble learning optimization

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