{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,25]],"date-time":"2026-02-25T19:24:02Z","timestamp":1772047442453,"version":"3.50.1"},"reference-count":22,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2025,6,12]],"date-time":"2025-06-12T00:00:00Z","timestamp":1749686400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computation"],"abstract":"<jats:p>The accurate estimation of the remaining useful life (RUL) of lithium-ion batteries (LIBs) is essential for ensuring safety and enabling effective battery health management systems. To address this challenge, data-driven solutions leveraging advanced machine learning and deep learning techniques have been developed. This study introduces a novel framework, Deep Neural Networks with Memory Features (DNNwMF), for predicting the RUL of LIBs. The integration of memory features significantly enhances the model\u2019s accuracy, and an autoencoder is incorporated to optimize the feature representation. The focus of this work is on feature engineering and uncovering hidden patterns in the data. The proposed model was trained and tested using lithium-ion battery cycle life datasets from NASA\u2019s Prognostic Centre of Excellence and CALCE Lab. The optimized framework achieved an impressive RMSE of 6.61%, and with suitable modifications, the DNN model demonstrated a prediction accuracy of 92.11% for test data, which was used to estimate the RUL of Nissan Leaf Gen 01 battery modules.<\/jats:p>","DOI":"10.3390\/computation13060147","type":"journal-article","created":{"date-parts":[[2025,6,12]],"date-time":"2025-06-12T11:47:07Z","timestamp":1749728827000},"page":"147","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Advanced Deep Learning Framework for Predicting the Remaining Useful Life of Nissan Leaf Generation 01 Lithium-Ion Battery Modules"],"prefix":"10.3390","volume":"13","author":[{"given":"Shamaltha M.","family":"Wickramaarachchi","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering, University of Moratuwa, Moratuwa 10400, Sri Lanka"}]},{"given":"S. A. Dewmini","family":"Suraweera","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, University of Moratuwa, Moratuwa 10400, Sri Lanka"}]},{"given":"D. M. Pasindu","family":"Akalanka","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, University of Moratuwa, Moratuwa 10400, Sri Lanka"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3767-8280","authenticated-orcid":false,"given":"V.","family":"Logeeshan","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, University of Moratuwa, Moratuwa 10400, Sri Lanka"}]},{"given":"Chathura","family":"Wanigasekara","sequence":"additional","affiliation":[{"name":"Institute of Maritime Energy Systems, German Aerospace Centre (DLR), 21502 Geesthacht, Germany"}]}],"member":"1968","published-online":{"date-parts":[[2025,6,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Wickramaarachchi, S.M., Suraweera, S.A.D., Akalanka, D.M.P., Logeeshan, V., and Wanigasekara, C. (2024, January 29\u201331). Accurate Prediction of Remaining Useful Life for Lithium-ion Battery Cells Using Deep Neural Networks. 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