Correlation between capacity and health factors.

As the primary power source for electric vehicles, the accurate estimation of the State of Health (SOH) of lithium-ion batteries is crucial for ensuring the reliable operation of the power system. Long Short-Term Memory (LSTM), a special type of recurrent neural network, achieves sequence information estimation through a gating mechanism. However, traditional LSTM-based SOH estimation methods do not account for the fact that the degradation sequence of battery SOH exhibits trend-like nonlinearity and significant dynamic variations between samples. Therefore, this paper proposes an LSTM-based lithium-ion SOH estimation method incorporating data characteristics and spatio-temporal attention. First, considering the trend-like nonlinearity of the degradation sequence, which is initially gradual and then rapid, input features are filtered and divided into trend and non-trend features. Then, to address the significant dynamic variations between samples, especially for capacity regeneration,a spatio-temporal attention mechanism is designed to extract spatio-temporal features from multidimensional non-trend features. Subsequently, an LSTM model is built with trend features, spatio-temporal features, and actual capacity as inputs to estimate capacity. Finally, the model is trained and tested on different datasets. Experimental results demonstrate that the proposed method outperforms traditional methods in terms of SOH estimation accuracy and robustness.

作为电动汽车的主要动力源，锂离子电池健康状态（State of Health, SOH）的精准估算对保障动力系统可靠运行至关重要。长短期记忆网络（Long Short-Term Memory, LSTM）作为一类特殊的循环神经网络，通过门控机制实现序列信息估算。然而，传统基于LSTM的SOH估算方法未考虑两大核心特性：一是电池SOH退化序列呈现先缓后快的趋势类非线性特性，二是不同样本间存在显著的动态差异。为此，本文提出一种融合数据特性与时空注意力机制的LSTM锂离子SOH估算方法。首先，针对退化序列先缓后快的趋势类非线性特性，对输入特征进行筛选并划分为趋势特征与非趋势特征。随后，针对样本间显著的动态差异（尤其针对容量再生现象），设计时空注意力机制以从多维非趋势特征中提取时空特征。接着，以趋势特征、时空特征与实际容量作为输入构建LSTM模型以实现容量估算。最后，在多组不同数据集上完成模型的训练与测试。实验结果表明，所提方法在SOH估算精度与鲁棒性方面均优于传统方法。