基于 VAE-STAGCNN 模型的锂电池容量预测方法

廖卫强; 张梦远; 王毅龙; 张长坤; 吴跃高

doi:10.19783/j.cnki.pspc.260016

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基于 VAE-STAGCNN 模型的锂电池容量预测方法

理论分析 | 更新时间：2026-07-27

- 基于 VAE-STAGCNN 模型的锂电池容量预测方法
- Capacity prediction of lithium-ion batteries based on a VAE-STAGCNN model
- 电力系统保护与控制 2026年54卷第13期
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- DOI：10.19783/j.cnki.pspc.260016
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- 纸质出版：2026
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廖卫强, 张梦远, 王毅龙, 等. 基于 VAE-STAGCNN 模型的锂电池容量预测方法[J]. 电力系统保护与控制, 2026,54(13).

LIAO Weiqiang, ZHANG Mengyuan, WANG Yilong, et al. Capacity prediction of lithium-ion batteries based on a VAE-STAGCNN model[J]. Power System Protection and Control, 2026, 54(13).
廖卫强, 张梦远, 王毅龙, 等. 基于 VAE-STAGCNN 模型的锂电池容量预测方法[J]. 电力系统保护与控制, 2026,54(13). DOI： 10.19783/j.cnki.pspc.260016.

LIAO Weiqiang, ZHANG Mengyuan, WANG Yilong, et al. Capacity prediction of lithium-ion batteries based on a VAE-STAGCNN model[J]. Power System Protection and Control, 2026, 54(13). DOI： 10.19783/j.cnki.pspc.260016.

摘要

锂离子电池容量预测面临工况复杂、测试成本高及衰退过程强非线性等挑战，传统数据驱动方法在数据匮乏场景下准确性与泛化性受限。为此，提出融合变分自编码器 (variational autoencoder

VAE) 与时空注意力图卷积神经网络 (spatio-temporal attention graph convolutional neural network

STAGCNN) 的预测方法。首先，利用 VAE 学习得到电池老化潜在分布，通过隐空间重采样生成多样化样本，增强少样本条件下的数据覆盖与模型鲁棒性。其次，将电池多维度特征构建为图节点，基于特征相关性动态生成邻接矩阵，并引入时间注意力机制，实现空间关联与时间依赖的协同建模。最后，利用实际电池数据集进行实验验证。结果表明，所提模型预测精度显著提升，并具有一定的跨电池泛化潜力。

Abstract

Lithium-ion battery capacity prediction faces challenges such as complex operating conditions

high testing costs

and highly nonlinear degradation processes. Traditional data-driven methods often suffer from limited accuracy and generalization capability in scenarios with scarce data. To address these issues

this paper proposes a prediction method that integrates variational autoencoder (VAE) with spatiotemporal attention graph convolutional neural network (STAGCNN). First

the latent distribution of battery aging is learned using a VAE

and diversified samples are then generated through latent space resampling

thereby enhancing data coverage and model robustness under small-sample conditions. Second

multidimensional battery features are represented as graph nodes

and an adjacency matrix is dynamically generated based on feature correlations. A temporal attention mechanism is further introduced to achieve collaborative modeling of spatial associations and temporal dependencies. Finally

experiments are conducted using real-world battery datasets. The results show that the proposed model significantly improves the prediction accuracy while exhibiting promising cross-battery generalization capability.

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Keywords

references

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