基于序列分解与改进紧耦合卷积Transformer模型的锂离子电池剩余使用寿命预测

Remaining Useful Life Prediction of Lithium-ion Battery Based on Sequence Decomposition and Improved Tightly-coupled Convolutional Transformer Model

  • 摘要: 针对现有锂离子电池剩余使用寿命(Remaining Useful Life,RUL)预测中数据驱动方法在预测RUL时面临的噪声干扰及预测精度的提升需求,提出一种自适应噪声完全集合经验模态分解(Complete Ensemble Empirical Mode Decomposition with Adaptive Noise,CEEMDAN)与基于多头信息熵稀疏注意力机制(Multi-Head Information-Entropy Sparse Attention,MESA)的紧耦合卷积Transformer(Tightly-coupled Convolutional Transformer,TCCT)模型结合的锂离子电池RUL预测方法。首先,选取电池容量退化趋势作为健康因子,利用CEEMDAN分解电池容量数据,降低容量再生和噪声的影响;然后,使用MESA机制降低计算复杂度并优化注意力权重分配;最后,使用改进后的TCCT模型对分解的残差和分量深度挖掘学习,构建出更精确的锂离子电池RUL预测模型。为验证方法的有效性,采用NASA和CALCE的公开数据集进行验证,对比分析CNN(Convolutional Neural Network)-Transformer模型、CNN-LSTM(Long Short-Term Memory)模型及Informer模型,所提方法的误差指标均有所降低,实验表明所提方法能够更有效的预测锂离子电池的RUL。

     

    Abstract: Aiming at the noise interference and the improvement of prediction accuracy in the prediction of remaining useful life (RUL) of lithium-ion batteries by data-driven methods, we propose a lithium-ion battery RUL prediction method combining complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and tightly-coupled convolutional Transformer (TCCT) model based on multi-head information-entropy sparse attention (MESA). Firstly, we select the battery capacity decline trend as the health factor, and decompose the battery capacity data by CEEMDAN to reduce the impact of capacity regeneration and noise. Secondly, we use the MESA mechanism to reduce the computational complexity and optimize the attention weight distribution. Finally, we use the improved TCCT model to deeply mine the decomposed residuals and components to construct a more accurate lithium-ion battery RUL prediction model. In order to verify the effectiveness of the method, we use the public data sets of NASA and CALCE for verification. We compare and analyze the CNN-Transformer model, CNN-LSTM model and Informer model. The error index of the proposed method is reduced. Experiments show that the proposed method can more effectively predict the RUL of lithium-ion batteries.

     

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