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Understanding of Lithium-ion battery degradation using multisine-based nonlinear characterization method

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  • Fan, Chuanxin
  • Liu, Kailong
  • Zhu, Tao
  • Peng, Qiao

Abstract

The nonlinearity of lithium-ion battery voltage response has been recently gained high attention in battery characterization and health diagnosis. The multisine-based nonlinear characterization method has the potential for development as an expedient on-board technique for analyzing nonlinear responses. Despite this, it remains challenging to analyze the effect of aging degradation on LIB nonlinearity. In this study, the odd random-phase multisine method is performed on fresh and aged three-electrode experimental cells. This allowed for the separation of impedance-related linear approximation and odd or even order nonlinearity toward the full-cell voltage into their respective electrodes. The results demonstrate that, as the LIB degrades, the increase of impedance-related linear approximation estimated by the multisine-based method agrees well with the results of conventional EIS. The variation of nonlinearities is demonstrated in relation to the effect of degradation modes. The multisine-based method presents the advantage of simultaneously capturing impedance-related and nonlinearity information. This makes it become a fast diagnostic method that can be implemented in a BMS to quantify the causes of battery degradation, thereby supporting battery utilization optimization and future battery designs.

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  • Fan, Chuanxin & Liu, Kailong & Zhu, Tao & Peng, Qiao, 2024. "Understanding of Lithium-ion battery degradation using multisine-based nonlinear characterization method," Energy, Elsevier, vol. 290(C).
    • Handle: RePEc:eee:energy:v:290:y:2024:i:c:s036054422400001x
    DOI: 10.1016/j.energy.2024.130230
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    References listed on IDEAS

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    Cited by:

    1. Dan, Zhaohui & Song, Aoye & Yu, Xiaojun & Zhou, Yuekuan, 2024. "Electrification-driven circular economy with machine learning-based multi-scale and cross-scale modelling approach," Energy, Elsevier, vol. 299(C).
    2. Peng, Qiao & Li, Wei & Fowler, Michael & Chen, Tao & Jiang, Wei & Liu, Kailong, 2024. "Battery calendar degradation trajectory prediction: Data-driven implementation and knowledge inspiration," Energy, Elsevier, vol. 294(C).

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