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摘要： 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.
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图1 Capacity loss caused by SEI formation under different SOC ranges.
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图2 RLS-based battery RUL prediction results under: (a) SOC?=?0–100%; (b) SOC?=?10–90%; (c) SOC?=?50–100%; (d) SOC?=?25–75%.
| 引文信息： Zhang Y , Xiong R , He H , et al. State of charge-dependent aging mechanisms in graphite/Li(NiCoAl)O_2 cells: Capacity loss modeling and remaining useful life prediction[J]. Applied Energy, 2019, 255(Dec.1):113818.1-113818.8. (下载链接) | 其他相关论文： 1. Zhang Y Z , Xiong R , He H W , et al. A LSTM-RNN method for the lithuim-ion battery remaining useful life prediction[C]// 2017 Prognostics and System Health Management Conference (PHM-Harbin). 2017.（下载链接）
2.Zhang Y , Xiong R , He H , et al. Lithium-ion battery remaining useful life prediction with Box-Cox transformation and Monte Carlo simulation[J]. IEEE Transactions on Industrial Electronics, 2018:1-1. (下载链接) |
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