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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\u2014diverse in capacity, chemistry, and cycling conditions\u2014was 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\n                    <jats:sup>2<\/jats:sup>\n                    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.\n                  <\/jats:p>","DOI":"10.1007\/s44163-025-00582-5","type":"journal-article","created":{"date-parts":[[2025,10,29]],"date-time":"2025-10-29T13:03:47Z","timestamp":1761743027000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Stacked temporal deep learning for early-stage degradation forecasting in lithium-metal batteries"],"prefix":"10.1007","volume":"5","author":[{"given":"Wasnaa Kadhim","family":"Jawad","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Luttfi A.","family":"Al-Haddad","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,10,29]]},"reference":[{"key":"582_CR1","doi-asserted-by":"publisher","DOI":"10.3390\/batteries11080286","author":"LO Schmidt","year":"2025","unstructured":"Schmidt LO, Wehbe H, Hartwig S, Kandula MW. 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