{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,7]],"date-time":"2026-02-07T20:31:51Z","timestamp":1770496311991,"version":"3.49.0"},"reference-count":24,"publisher":"Wiley","issue":"12","license":[{"start":{"date-parts":[[2025,7,20]],"date-time":"2025-07-20T00:00:00Z","timestamp":1752969600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["advanced.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Advanced Intelligent Systems"],"published-print":{"date-parts":[[2025,12]]},"abstract":"<jats:p>Accurate prediction for performance of rechargeable batteries is critical to both academic research and industry applications. With the availability of big data and deep learning framework, machine learning becomes increasingly important to battery research. The performance of machine learning models is strongly influenced by input methodology. Herein, a true 2D ResNet method to train and predict battery performance based on battery test image data is applied. With theoretical analysis and tests on two large battery datasets, the superiority of 2D image representation compared with traditional 1D representation in principal component analysis method and different models is illustrated. Moreover, a 2D ResNet model is also trained to explore the full potential of deep learning in battery cycle modeling, allowing it to learn directly from raw cycling images. This makes the process a true\u20102D ResNet method beyond the previous work. When using deep learning models to predict state of health and remaining useful life of the cell, it is found that the method shows great potential for the state\u2010of\u2010the\u2010art prediction accuracy. This work enhances the understanding of advantages of 2D image representation, which offers new perspectives on how to build efficient machine models for batteries.<\/jats:p>","DOI":"10.1002\/aisy.202500279","type":"journal-article","created":{"date-parts":[[2025,7,21]],"date-time":"2025-07-21T04:18:30Z","timestamp":1753071510000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["True\u20102D ResNet Approach on Battery Data Images for Machine Learning Performance Prediction"],"prefix":"10.1002","volume":"7","author":[{"given":"Yuchuang","family":"Cao","sequence":"first","affiliation":[{"name":"John A. Paulson School of Engineering and Applied Sciences Harvard University  Cambridge 02138 Massachusetts United States"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mingyang","family":"Wang","sequence":"additional","affiliation":[{"name":"John A. Paulson School of Engineering and Applied Sciences Harvard University  Cambridge 02138 Massachusetts United States"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Minjeong","family":"Byeon","sequence":"additional","affiliation":[{"name":"BMS AI Development Team LG Energy Solution  Seoul 07335 Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xi","family":"Chen","sequence":"additional","affiliation":[{"name":"John A. Paulson School of Engineering and Applied Sciences Harvard University  Cambridge 02138 Massachusetts United States"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jeesoon","family":"Choi","sequence":"additional","affiliation":[{"name":"BMS AI Development Team LG Energy Solution  Seoul 07335 Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9390-0830","authenticated-orcid":false,"given":"Xin","family":"Li","sequence":"additional","affiliation":[{"name":"John A. Paulson School of Engineering and Applied Sciences Harvard University  Cambridge 02138 Massachusetts United States"},{"name":"Institute of Physics Chinese Academy of Sciences  Beijing 100190 China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2025,7,20]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.est.2021.102591"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1149\/2.018203jes"},{"key":"e_1_2_8_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jpowsour.2013.01.041"},{"key":"e_1_2_8_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jpowsour.2013.05.040"},{"key":"e_1_2_8_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIE.2018.2880703"},{"key":"e_1_2_8_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.electacta.2019.02.055"},{"key":"e_1_2_8_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jclepro.2018.09.065"},{"key":"e_1_2_8_9_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41578-020-0216-y"},{"key":"e_1_2_8_10_1","doi-asserted-by":"publisher","DOI":"10.1002\/adma.202101474"},{"key":"e_1_2_8_11_1","doi-asserted-by":"publisher","DOI":"10.1149\/1945-7111\/abec55"},{"key":"e_1_2_8_12_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41560-019-0356-8"},{"key":"e_1_2_8_13_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-020-16233-5"},{"key":"e_1_2_8_14_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ensm.2022.05.007"},{"key":"e_1_2_8_15_1","doi-asserted-by":"publisher","DOI":"10.1021\/acscentsci.8b00229"},{"key":"e_1_2_8_16_1","doi-asserted-by":"publisher","DOI":"10.1021\/acs.chemmater.8b03272"},{"key":"e_1_2_8_17_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-020-1994-5"},{"key":"e_1_2_8_18_1","doi-asserted-by":"publisher","DOI":"10.1021\/jacsau.2c00009"},{"key":"e_1_2_8_19_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2016.05.051"},{"key":"e_1_2_8_20_1","doi-asserted-by":"publisher","DOI":"10.1002\/aisy.201900102"},{"key":"e_1_2_8_21_1","doi-asserted-by":"publisher","DOI":"10.1021\/acsenergylett.2c01817"},{"key":"e_1_2_8_22_1","doi-asserted-by":"crossref","unstructured":"K.He X.Zhang S.Ren J.Sun inProc. of the IEEE Conf. on Computer Vision and Pattern Recognition Las Vegas 27\u201330 June 2016 770\u2013778.","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_2_8_23_1","first-page":"2825","volume":"12","author":"Pedregosa F.","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"e_1_2_8_24_1","first-page":"8024","volume":"32","author":"Paszke A.","year":"2019","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"e_1_2_8_25_1","first-page":"18","volume":"2","author":"Liaw A.","year":"2002","journal-title":"R News"}],"container-title":["Advanced Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/advanced.onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/aisy.202500279","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,13]],"date-time":"2025-12-13T06:05:29Z","timestamp":1765605929000},"score":1,"resource":{"primary":{"URL":"https:\/\/advanced.onlinelibrary.wiley.com\/doi\/10.1002\/aisy.202500279"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,20]]},"references-count":24,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2025,12]]}},"alternative-id":["10.1002\/aisy.202500279"],"URL":"https:\/\/doi.org\/10.1002\/aisy.202500279","archive":["Portico"],"relation":{},"ISSN":["2640-4567","2640-4567"],"issn-type":[{"value":"2640-4567","type":"print"},{"value":"2640-4567","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7,20]]},"assertion":[{"value":"2025-03-12","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-07-20","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"e202500279"}}