MULTI-CHANNEL FEATURE FUSION-BASED VMD-SCNN-LSTM FOR LITHIUN-ION BATTERY RUL PREDICTION

Yong Ye; Wu Shenlin; Xue Mingfeng; Mao Xiaobo

doi:10.19912/j.0254-0096.tynxb.2024-1457

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Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (12) : 41-51. DOI: 10.19912/j.0254-0096.tynxb.2024-1457

MULTI-CHANNEL FEATURE FUSION-BASED VMD-SCNN-LSTM FOR LITHIUN-ION BATTERY RUL PREDICTION

Yong Ye¹, Wu Shenlin², Xue Mingfeng¹, Mao Xiaobo¹

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Abstract

Aiming at the problem of nonlinear decline of battery capacity, a RUL prediction method for lithium battery based on multi-channel feature fusion optimization VMD-SCNN-LSTM is proposed. Firstly, the health factors are selected according to the Pearson correlation coefficient, and the time-series characteristic curves of voltage, current and temperature during the charging and discharging process of Li-ion battery are extracted. Secondly, the battery capacity degradation curves are decomposed using variational modal decomposition （VMD）. Finally, the HI and the frequency-domain features of the capacity decomposed by VMD are used as the multi-channel parallel inputs to the convolutional neural network （CNN）, and the original capacity was used as the input to the long-short-term memory network （LSTM）, and then the two extracted features are fused to carry out RUL prediction. A triple cross-validation training method and dropout technique are also introduced to avoid the overfitting problem. The optimal hybrid model proposed in this study has an RMSE of no more than 3.1% and a MAPE of no more than 1.3%.

Key words

lithium-ion battery / remaining useful life / variational mode decomposition / convolutional neural network / long short-term memory network

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Yong Ye, Wu Shenlin, Xue Mingfeng, Mao Xiaobo. MULTI-CHANNEL FEATURE FUSION-BASED VMD-SCNN-LSTM FOR LITHIUN-ION BATTERY RUL PREDICTION[J]. Acta Energiae Solaris Sinica. 2025, 46(12): 41-51 https://doi.org/10.19912/j.0254-0096.tynxb.2024-1457

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