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• W4293432193 abstract "Prognostics and health management (PHM) is crucial to the reliability and safety of lithium-ion batteries. In this respect, the capacity regeneration phenomenon that occurs during the process of battery degradation brings a challenge to the accuracy of capacity prediction. In this paper, a hybrid method is proposed for the accurate prediction of lithium-ion batteries capacity considering regeneration. Firstly, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is applied to decompose the raw capacity signal into the global degradation trend components and the local fluctuation components. Then, each component is separately fed to the adaptive neuro-fuzzy inference system (ANFIS) for prediction. Finally, the individual outputs of the ANFIS models are recomposed to obtain the ultimate prediction results. The proposed method is validated by application to NASA lithium-ion battery experimental data. The results obtained show that the proposed method can obtain satisfactory prediction accuracy, wherein the negative impact of capacity regeneration on the prediction accuracy is reduced." @default.
• W4293432193 created "2022-08-29" @default.
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• W4293432193 date "2022-12-01" @default.
• W4293432193 modified "2023-10-17" @default.
• W4293432193 title "A hybrid method for prognostics of lithium-ion batteries capacity considering regeneration phenomena" @default.
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• W4293432193 doi "https://doi.org/10.1016/j.energy.2022.125278" @default.
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