用残差连接增强卷积LSTM，更准预测锂枝晶生长
Residual Connection-Enhanced ConvLSTM for Lithium Dendrite Growth Prediction
- 在ConvLSTM中加入残差连接，缓解梯度消失问题
- 在0.1V~0.5V电压下，准确率提升7%，MSE显著降低
- 适合电池安全预警与实时性能优化研究者
锂枝晶生长严重影响可充电电池的性能与安全性，易引发短路和容量衰减。本文提出一种残差连接增强型卷积LSTM模型，用于更精准地预测枝晶生长模式，同时提升计算效率。通过在ConvLSTM中引入残差连接，模型有效缓解梯度消失问题，增强跨层特征保留能力，能够同时捕捉局部枝晶生长动态与宏观电池行为。数据集基于相场模型生成，模拟不同条件下的枝晶演化过程。实验结果表明，在0.1V、0.3V、0.5V电压条件下，该模型相比传统ConvLSTM最高提升7%的预测准确率，并显著降低均方误差（MSE）。这验证了残差连接在电化学系统建模的时空深度网络中的有效性。该方法为电池诊断提供了可靠工具，有助于实现锂离子电池的实时监控与性能优化。未来可拓展至其他电池体系，并结合真实实验数据进一步验证。
原文摘要 · Abstract (English)
The growth of lithium dendrites significantly impacts the performance and safety of rechargeable batteries, leading to short circuits and capacity degradation. This study proposes a Residual Connection-Enhanced ConvLSTM model to predict dendrite growth patterns with improved accuracy and computational efficiency. By integrating residual connections into ConvLSTM, the model mitigates the vanishing gradient problem, enhances feature retention across layers, and effectively captures both localized dendrite growth dynamics and macroscopic battery behavior. The dataset was generated using a phase-field model, simulating dendrite evolution under varying conditions. Experimental results show that the proposed model achieves up to 7% higher accuracy and significantly reduces mean squared error (MSE) compared to conventional ConvLSTM across different voltage conditions (0.1V, 0.3V, 0.5V). This highlights the effectiveness of residual connections in deep spatiotemporal networks for electrochemical system modeling. The proposed approach offers a robust tool for battery diagnostics, potentially aiding in real-time monitoring and optimization of lithium battery performance. Future research can extend this framework to other battery chemistries and integrate it with real-world experimental data for further validation
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