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Accurately predicting the lifespan of lithium-ion batteries is crucial for reducing maintenance costs and advancing clean energy technologies. Traditional prediction methods often to accurately estimate the lifespan due to the diverse volatility characteristics of lithium-ion battery degradation. This paper proposes a Kurtosis-driven SMA-ARIMA-LSTM method to the lifespan of lithium-ion batteries. First, the data is decomposed into low and high volatility components using a moving average (SMA). Then, the I model is applied to the low volatility part, while the LSTM network handles the high volatility part. Finally, the parallel prediction results of these two parts are combined to the remaining lifespan of the lithium-ion battery. The model is validated using four sets of CS2 series lithium-ion battery degradation data provided by the CALCE center at University of Maryland. The results show that the hybrid model significantly improves prediction accuracy compared to standalone LSTM or ARIMA models, achieving near-perfect determination coefficients while significantly mean and root mean square errors. This effectively captures the overall degradation trend of the battery and the capacity regeneration phenomenon. The experimental results demonstrate that the proposed SMA-IMA-LSTM method achieves a fitting rate of over 95%, with MAE kept within 0.9% and RMSE kept within 1.4%, thus realizing precise prediction of the remaining lifespan of lithium-ion batteries.
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