EN基于多通道特征融合优化VMD-SCNN-LSTM的锂电池RUL预测
基于多通道特征融合优化VMD-SCNN-LSTM的锂电池RUL预测
- 太阳能学报 2025年46卷第12期 页码：41-51
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DOI：doi:10.19912/j.0254-0096.tynxb.2024-1457
中图分类号： TM912-
网络出版：2026-01-07，
纸质出版：2025
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勇晔, 吴参林, 薛溟枫, 等. 基于多通道特征融合优化VMD-SCNN-LSTM的锂电池RUL预测[J]. 太阳能学报, 2025,46(12):41-51.
勇晔, 吴参林, 薛溟枫, et al. 基于多通道特征融合优化VMD-SCNN-LSTM的锂电池RUL预测[J]. 2025, 46(12): 41-51.
勇晔, 吴参林, 薛溟枫, 等. 基于多通道特征融合优化VMD-SCNN-LSTM的锂电池RUL预测[J]. 太阳能学报, 2025,46(12):41-51. DOI： doi:10.19912/j.0254-0096.tynxb.2024-1457.
勇晔, 吴参林, 薛溟枫, et al. 基于多通道特征融合优化VMD-SCNN-LSTM的锂电池RUL预测[J]. 2025, 46(12): 41-51. DOI： doi:10.19912/j.0254-0096.tynxb.2024-1457.
摘要
针对电池容量非线性衰退问题
提出一种基于多通道特征融合优化VMD-SCNN-LSTM的锂电池剩余使用寿命（RUL）预测方法。首先
根据皮尔逊相关系数选取健康因子（HI）
提取锂电池充放电过程中电压、电流和温度的时序特性曲线。其次
采用变分模态分解（VMD）对电池容量劣化曲线进行分解。最后
将HIs和经过VMD分解的容量频域特征作为卷积神经网络（CNN）的多通道并行输入
原始容量作为长短期记忆网络（LSTM）的输入
再将两者提取的特征进行融合开展RUL预测。同时引入3倍交叉验证训练方法和dropout技术来避免过拟合问题。实验结果表明
所提最优混合模型的均方根误差RMSE不超过3.1%
平均绝对百分比误差MAPE不超过1.3%。
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%.
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references
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