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<p>The integration of artificial intelligence (AI) into energy storage prognostics presents a transformative approach for enhancing the safety, reliability, and longevity of next-generation battery technologies. This study introduces a robust temporal deep learning framework for predictive modeling of lithium-metal battery (LMB) degradation, with a focus on AI-driven health forecasting. A comprehensive dataset comprising 23 LMB cells—diverse in capacity, chemistry, and cycling conditions—was curated to train and validate a suite of sequential models including Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Transformer networks, and multiple fully connected Deep Neural Networks (DNNs). These were subsequently integrated into a stacked ensemble meta-model (S-DNN) using an Extreme Learning Machine (ELM), designed to enhance forecast accuracy and generalization. The ensemble achieved superior performance with an RMSE of 0.026 Ah, R<sup>2</sup> of 0.9917, and CVRMSE of 0.6955%, outperforming all individual models. Crucially, the framework demonstrated strong early-stage prediction capabilities using only 15% of the cycling data, maintaining a CVRMSE below 6.5%. Rich regression analyses and error visualizations were used to support interpretability and deployment readiness. Limitations related to uniform temperature cycling and the need for broader cross-domain validation are acknowledged as directions for future work. This work advances the frontier of AI for prognostics by introducing an interpretable, generalizable, and ensemble-based architecture for real-time health monitoring in complex electrochemical systems.</p>

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Stacked temporal deep learning for early-stage degradation forecasting in lithium-metal batteries

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月29日

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  • Wasnaa Kadhim Jawad,
  • Luttfi A. Al-Haddad

摘要

The integration of artificial intelligence (AI) into energy storage prognostics presents a transformative approach for enhancing the safety, reliability, and longevity of next-generation battery technologies. This study introduces a robust temporal deep learning framework for predictive modeling of lithium-metal battery (LMB) degradation, with a focus on AI-driven health forecasting. A comprehensive dataset comprising 23 LMB cells—diverse in capacity, chemistry, and cycling conditions—was curated to train and validate a suite of sequential models including Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Transformer networks, and multiple fully connected Deep Neural Networks (DNNs). These were subsequently integrated into a stacked ensemble meta-model (S-DNN) using an Extreme Learning Machine (ELM), designed to enhance forecast accuracy and generalization. The ensemble achieved superior performance with an RMSE of 0.026 Ah, R2 of 0.9917, and CVRMSE of 0.6955%, outperforming all individual models. Crucially, the framework demonstrated strong early-stage prediction capabilities using only 15% of the cycling data, maintaining a CVRMSE below 6.5%. Rich regression analyses and error visualizations were used to support interpretability and deployment readiness. Limitations related to uniform temperature cycling and the need for broader cross-domain validation are acknowledged as directions for future work. This work advances the frontier of AI for prognostics by introducing an interpretable, generalizable, and ensemble-based architecture for real-time health monitoring in complex electrochemical systems.