Residual Connection-Enhanced ConvLSTM for Lithium Dendrite Growth Prediction
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866909656147820544 |
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| author | Lee, Hosung Hwang, Byeongoh Kim, Dasan Kang, Myungjoo |
| author_facet | Lee, Hosung Hwang, Byeongoh Kim, Dasan Kang, Myungjoo |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_17756 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Residual Connection-Enhanced ConvLSTM for Lithium Dendrite Growth Prediction Lee, Hosung Hwang, Byeongoh Kim, Dasan Kang, Myungjoo Materials Science Artificial Intelligence 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 |
| title | Residual Connection-Enhanced ConvLSTM for Lithium Dendrite Growth Prediction |
| topic | Materials Science Artificial Intelligence |
| url | https://arxiv.org/abs/2506.17756 |