基于数据预处理和VMD-LSTM-GPR的锂离子电池剩余寿命预测

doi:10.19595/j.cnki.1000-6753.tces.230210

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Abstract

The performance of lithium-ion battery will deteriorate with the decrease of capacity and the increase of impedance during the continuous charging and discharging process, which can lead to equipment and system failures and even catastrophic losses. Therefore, achieving accurate and reliable prediction of the remaining useful life (RUL) of lithium-ion battery is crucial in scientific research and practical application of battery management system design. However, onthe one hand,probability prediction could not be supported and point prediction could be only supported in previous prediction methods, which did not provide a clear mathematical representation of the confidence level of the prediction results. On the other hand, the noise and capacity rebound phenomena in the original data was ignored. To address these issues, this paper proposed a prediction framework based on data pre-processing combined with variational mode decomposition (VMD), long and short-term memory network (LSTM) and Gaussian regression process (GPR).
Firstly, the indirect health indicator(HI) reflecting the life degradation trend of lithium-ion battery was extracted from the charge/discharge curve. Pearson and Spearman correlation coefficients were used to verify the correlation between the extracted indirect HI and capacity. The kernel principal element analysis(KPCA)method was used to remove the redundant components of indirect HI to transform into fusion HI to reduce the computational complexity. Then, the VMD decomposition method was applied to decompose the obtained fusion HI into multiple modal components. Based on the central frequency of the modal components and the capacity-related coefficient, multiple modal components are divided into three parts: global attenuation, local regeneration and other noise. Further, the time series prediction of the global attenuation and local regeneration components were performed separately using LSTM neural network to obtain the predicted values after the prediction starting point, and the values of each component were summed up correspondingly to obtain the reconstructed HI. Finally, the reconstructed HI and capacity were used as the input and output of the GPR model respectively to achieve the RUL prediction of lithium-ion battery.
NASA lithium-ion battery public data set was used for experimental validation, and different prediction starting points were set for each battery, with the starting point set at about 40% of the total cycle time. The experimental results show that the predicted values of capacity can track the battery aging trend well, and also have good estimation ability for the local regeneration phenomenon of capacity regeneration during the aging process. The true value of capacity basically falls in the 95% confidence interval of the predicted value, while the prediction effect of the model gets better as the training data increases. Among the evaluation indexes of capacity prediction results, the maximum values of root mean square error, mean absolute error and mean absolute percentage error are 2.98%, 2.34% and 1.81%, respectively, and the errors of remaining useful life prediction are all within 2 cycles.
The following conclusions can be drawn from the experimental analysis: (1) The KPCA algorithm can remove the redundant information between indirect health indicatorto reduce the data complexity and achieve the data pre-processing. (2) The VMD decomposition method can mine the intrinsic information in the data and capture the long-term downward trend, local regeneration and noise component implied in the fusion HI, so as to remove the implied noise component in the data. (3) The GPR model can support probabilistic prediction and give confidence intervals for the capacity prediction results.

Key words： Lithium-ion battery remaining useful life health indicator variational mode decomposition Gaussian regression process long and short-term memory

Received: 22 February 2023

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TM912

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	Li Yingshun
	Kan Hongda
	Guo Zhannan
	Wang Debiao
	Wang Cheng

Cite this article:

Li Yingshun,Kan Hongda,Guo Zhannan等. Prediction of Remaining Useful Life of Lithium-Ion BatteryBased on Data Preprocessing and VMD-LSTM- GPR[J]. Transactions of China Electrotechnical Society, 0, (): 9017-17.

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.230210 OR https://dgjsxb.ces-transaction.com/EN/Y0/V/I/9017

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