State of charge-dependent aging mechanisms in graphite/Li(NiCoAl)O2 cells: Capacity loss modeling and remaining useful life prediction

Yongzhi Zhang, Rui Xiong, Hongwen He, Xiaobo Qu and Michael Pecht

Applied Energy, 2019, vol. 255, issue C

Abstract: Capacity loss modeling is required for accurate and reliable lifetime evaluation of lithium-ion batteries. The current capacity loss model parameters cannot be identified online. To address this problem, this paper has developed a capacity loss model based on the aging mechanisms of solid electrolyte interface layer growth and active material loss. Experimental results show that capacity loss due to solid electrolyte interface growth is independent of state of charge ranges during cycling, whereas capacity loss due to active material loss depends on the state of charge ranges. A comprehensive aging model is thus developed, combined with the recursive least squares method to identify the model parameters in realtime. In our case studies, the estimation errors of the capacity loss model are within 1% under different state of charge ranges. To avoid the modeling error caused by cell characteristic inconsistencies, model parameters are further updated adaptively based on online data for predicting the accurate lifetime of the specific cell.

Keywords: Lithium-ion battery; Capacity loss modeling; SEI layer growth; Active material loss; Remaining useful life (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (13)

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DOI: 10.1016/j.apenergy.2019.113818

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