{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,10]],"date-time":"2026-01-10T03:40:22Z","timestamp":1768016422167,"version":"3.49.0"},"reference-count":40,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"name":"National Key Research and Development Program of China","award":["2018YFB0104000"],"award-info":[{"award-number":["2018YFB0104000"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61763021"],"award-info":[{"award-number":["61763021"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100010665","name":"EU-funded Marie Sk\u0142odowska-Curie Individual Fellowships Project","doi-asserted-by":"publisher","award":["845102-HOEMEV-H2020-MSCA-IF-2018"],"award-info":[{"award-number":["845102-HOEMEV-H2020-MSCA-IF-2018"]}],"id":[{"id":"10.13039\/100010665","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2020]]},"DOI":"10.1109\/access.2020.3025766","type":"journal-article","created":{"date-parts":[[2020,9,22]],"date-time":"2020-09-22T23:07:26Z","timestamp":1600816046000},"page":"172783-172798","source":"Crossref","is-referenced-by-count":36,"title":["Capacity Prediction and Validation of Lithium-Ion Batteries Based on Long Short-Term Memory Recurrent Neural Network"],"prefix":"10.1109","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1634-7231","authenticated-orcid":false,"given":"Zheng","family":"Chen","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9916-6750","authenticated-orcid":false,"given":"Qiao","family":"Xue","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3612-1045","authenticated-orcid":false,"given":"Yitao","family":"Wu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4739-4812","authenticated-orcid":false,"given":"Shiquan","family":"Shen","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5563-8480","authenticated-orcid":false,"given":"Yuanjian","family":"Zhang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1853-0782","authenticated-orcid":false,"given":"Jiangwei","family":"Shen","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref39","first-page":"1929","article-title":"Dropout: A simple way to prevent neural networks from overfitting","volume":"15","author":"srivastava","year":"2014","journal-title":"J Mach Learn Res"},{"key":"ref38","first-page":"257","article-title":"Adaptive subgradient methods for online learning and stochastic optimization","volume":"12","author":"duchi","year":"2010","journal-title":"J Mach Learn Res"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2014.11.051"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1016\/j.etran.2019.100005"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1038\/s41560-019-0356-8"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2018.2805189"},{"key":"ref37","first-page":"2545","article-title":"Variants of rmsprop and adagrad with logarithmic regret bounds","author":"mukkamala","year":"2017","journal-title":"Proc 34th Int Conf Mach Learn"},{"key":"ref36","article-title":"Adam: A method for stochastic optimization","author":"kingma","year":"2014","journal-title":"arXiv 1412 6980"},{"key":"ref35","doi-asserted-by":"crossref","first-page":"3772","DOI":"10.1109\/TNNLS.2017.2741598","article-title":"Efficient online learning algorithms based on LSTM neural networks","volume":"29","author":"ergen","year":"2018","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1016\/j.jpowsour.2018.10.069"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2018.2794997"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2892062"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2956326"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2019.113817"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2018.09.182"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.3390\/en13061410"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/TPEL.2017.2670081"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2017.02.016"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1016\/j.est.2020.101250"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2019.03.177"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1016\/j.jpowsour.2019.03.008"},{"key":"ref28","article-title":"A critical review of recurrent neural networks for sequence learning","author":"lipton","year":"2015","journal-title":"arXiv 1506 00019"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1016\/j.jpowsour.2020.228132"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298935"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2923095"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2930680"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1016\/j.jpowsour.2019.227149"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/TCST.2016.2572362"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TCST.2018.2885681"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1155\/2019\/5327319"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jclepro.2018.12.041"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1016\/j.jpowsour.2014.06.133"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/TPEL.2017.2787909"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1016\/j.jpowsour.2018.03.015"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.3390\/app8060925"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2015.08.119"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.2972344"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2017.2759094"},{"key":"ref26","first-page":"457","article-title":"Efficient transfer learning schemes for personalized language modeling using recurrent neural network","author":"yoon","year":"2017","journal-title":"Proc 31st AAAI Conf Artif Intell"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.3390\/app8112078"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/8948470\/09203802.pdf?arnumber=9203802","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,12,17]],"date-time":"2021-12-17T19:56:05Z","timestamp":1639770965000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9203802\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"references-count":40,"URL":"https:\/\/doi.org\/10.1109\/access.2020.3025766","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020]]}}}