Residual Connection-Enhanced ConvLSTM for Lithium Dendrite Growth Prediction

TL;DR

This paper introduces a Residual Connection-Enhanced ConvLSTM model that improves the prediction accuracy of lithium dendrite growth, aiding battery safety and performance analysis.

Contribution

It presents a novel ConvLSTM architecture with residual connections that mitigates vanishing gradients and enhances feature learning for dendrite growth prediction.

Findings

Achieves up to 7% higher accuracy than conventional ConvLSTM

Reduces mean squared error significantly across voltage conditions

Effectively captures localized and macroscopic dendrite dynamics

Abstract

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