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| Prediction of Remaining Useful Life of Lithium-Ion Battery Based on Data Preprocessing and VMD-LSTM-GPR |
| Li Yingshun1, Kan Hongda1, Guo Zhannan1, Wang Debiao2, Wang Cheng2 |
1. School of Control Science and Engineering Dalian University of Technology Dalian 116000 China;
2. Shenyang Shunyi Science and Technology Co. Shenyang 110000 China |
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Abstract The performance of lithium-ion batteriescandeteriorate with the decrease of capacity and the increase of impedance during continuous charging and discharging process, which poses a risk of equipment and system failures, includingcatastrophic losses. Accurate and reliable prediction of the remaining useful life (RUL) of lithium-ion batteries is crucial. However, previous prediction methods mainly supportedpoint predictions without offering a clear mathematical representation of the confidence level of the prediction results. The noise and capacity rebound phenomena in the original data are ignored. Therefore, this paper proposes 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, anindirect health indicator (HI) reflecting the life degradation trend of lithium-ion batteriesisextracted from the charge/discharge curve. Pearson and Spearman correlation coefficients are used to verify the correlation between the extracted indirect HI and capacity. The kernel principal element analysis (KPCA) method reduces computational complexity by removingredundant components of indirect HI, transform them into fusion HI. Then, the VMD decomposition method decomposes the fusion HI into multiple modal components. Based on the central frequency of modal components and capacity-related coefficients, multiple modal components are divided into three parts: global attenuation, local regeneration, and other noise. Time series prediction of the global attenuation and local regeneration components are performed separately using LSTM neural networks to obtain the predicted values after the prediction starting point. The values of each component aresummed up to obtain the reconstructed HI. Finally, the reconstructed HI and capacity serve as the input and output of the GPR model RUL prediction, respectively.
NASA lithium-ion battery public data set is used for experimental validation, and different prediction starting points are set for each battery. The starting point set is at about 40% of the total cycle. The experimental results show that the predicted capacity values closely track the battery aging trend and effectively estimate the local regeneration phenomenon during the aging process. The true capacity values generally fall within the 95% confidence interval of the predicted value, and the prediction effect improves as the training data increase. Regarding evaluation indexes for capacity prediction results, the maximum root mean square error, mean absolute error, and mean absolute percentage error are 2.98%, 2.34%, and 1.81%, respectively. Errors in the remaining useful life prediction are all within 2 cycles.
The following conclusions can be drawn from the experimental analysis: (1) The KPCA algorithm removesredundant information between indirect health indicators to reduce data complexity and achieve data pre-processing. (2) The VMD decomposition method minesintrinsic information, capturing the long-term downward trend, local regeneration, and noise component to remove implied noise in the data. (3) The GPR model supports probabilistic prediction and provides confidence intervals for capacity prediction results.
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Received: 22 February 2023
Published: 07 June 2024
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2024, Vol. 39 
