{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T16:51:52Z","timestamp":1783097512051,"version":"3.54.6"},"reference-count":57,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2023,10,27]],"date-time":"2023-10-27T00:00:00Z","timestamp":1698364800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"\u201cRegional Innovation Strategy (RIS)\u201d through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (MOE)"},{"name":"Ministry Of Trade, Industry &amp; ENERGY (MOTIE) through the fostering project of The Establishment Project of Industry-University Fusion District"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"<jats:p>Predicting the remaining useful life (RUL) is a pivotal step in ensuring the reliability of lithium-ion batteries (LIBs). In order to enhance the precision and stability of battery RUL prediction, this study introduces an innovative hybrid deep learning model that seamlessly integrates convolutional neural network (CNN) and gated recurrent unit (GRU) architectures. Our primary goal is to significantly improve the accuracy of RUL predictions for LIBs. Our model excels in its predictive capabilities by skillfully extracting intricate features from a diverse array of data sources, including voltage (V), current (I), temperature (T), and capacity. Within this novel architectural design, parallel CNN layers are meticulously crafted to process each input feature individually. This approach enables the extraction of highly pertinent information from multi-channel charging profiles. We subjected our model to rigorous evaluations across three distinct scenarios to validate its effectiveness. When compared to LSTM, GRU, and CNN-LSTM models, our CNN-GRU model showcases a remarkable reduction in root mean square error, mean square error, mean absolute error, and mean absolute percentage error. These results affirm the superior predictive capabilities of our CNN-GRU model, which effectively harnesses the strengths of both CNNs and GRU networks to achieve superior prediction accuracy. This study draws upon NASA data to underscore the outstanding predictive performance of the CNN-GRU model in estimating the RUL of LIBs.<\/jats:p>","DOI":"10.3390\/computers12110219","type":"journal-article","created":{"date-parts":[[2023,10,27]],"date-time":"2023-10-27T09:42:52Z","timestamp":1698399772000},"page":"219","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":45,"title":["A CNN-GRU Approach to the Accurate Prediction of Batteries\u2019 Remaining Useful Life from Charging Profiles"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9308-1062","authenticated-orcid":false,"given":"Sadiqa","family":"Jafari","sequence":"first","affiliation":[{"name":"Department of Electronic Engineering, Institute of Information Science & Technology, Jeju National University, Jeju 63243, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1107-9941","authenticated-orcid":false,"given":"Yung-Cheol","family":"Byun","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Major of Electronic Engineering, Institute of Information Science & Technology, Jeju National University, Jeju 63243, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,10,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"466","DOI":"10.1016\/j.energy.2018.06.220","article-title":"Novel battery state-of-health online estimation method using multiple health indicators and an extreme learning machine","volume":"160","author":"Pan","year":"2018","journal-title":"Energy"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Chen, L., Xu, L., and Zhou, Y. 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