AI-Driven Prognostics for State of Health Prediction in Li-ion Batteries: A Comprehensive Analysis with Validation

Tianqi Ding, Dawei Xiang, Tianyao Sun, YiJiashum Qi, Zunduo Zhao
4/8/2025
cs.AIcs.LG

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Abstract

This paper presents a comprehensive review of AI-driven prognostics for State of Health (SoH) prediction in lithium-ion batteries. We compare the effectiveness of various AI algorithms, including FFNN, LSTM, and BiLSTM, across multiple datasets (CALCE, NASA, UDDS) and scenarios (e.g., varying temperatures and driving conditions). Additionally, we analyze the factors influencing SoH fluctuations, such as temperature and charge-discharge rates, and validate our findings through simulations. The results demonstrate that BiLSTM achieves the highest accuracy, with an average RMSE reduction of 15% compared to LSTM, highlighting its robustness in real-world applications.

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Cite this paper

@article{ding2025aidriven,
  title  = {AI-Driven Prognostics for State of Health Prediction in Li-ion Batteries: A Comprehensive Analysis with Validation},
  author = {Tianqi Ding and Dawei Xiang and Tianyao Sun and YiJiashum Qi and Zunduo Zhao},
  year   = {2025},
  eprint = {2504.05728},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2504.05728v1}
}

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