{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T09:01:59Z","timestamp":1775206919272,"version":"3.50.1"},"reference-count":28,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"name":"PIANO NAZIONALE DI RIPRESA E RESILIENZA (PNRR) ? MISSIONE 4 COMPONENTE 2, INVESTIMENTO 1.4","award":["CN00000023"],"award-info":[{"award-number":["CN00000023"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2024]]},"DOI":"10.1109\/access.2024.3410675","type":"journal-article","created":{"date-parts":[[2024,6,6]],"date-time":"2024-06-06T17:32:20Z","timestamp":1717695140000},"page":"80244-80254","source":"Crossref","is-referenced-by-count":21,"title":["Joint State of Charge and State of Health Estimation Using Bidirectional LSTM and Bayesian Hyperparameter Optimization"],"prefix":"10.1109","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3604-2072","authenticated-orcid":false,"given":"Panagiotis","family":"Eleftheriadis","sequence":"first","affiliation":[{"name":"Politecnico di Milano, Milan, Italy"}]},{"given":"Spyridon","family":"Giazitzis","sequence":"additional","affiliation":[{"name":"Institute of Energy and Automation Technology, Technische Universit&#x00E4;t Berlin, Berlin, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8802-6365","authenticated-orcid":false,"given":"Julia","family":"Kowal","sequence":"additional","affiliation":[{"name":"Institute of Energy and Automation Technology, Technische Universit&#x00E4;t Berlin, Berlin, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7883-0034","authenticated-orcid":false,"given":"Sonia","family":"Leva","sequence":"additional","affiliation":[{"name":"Politecnico di Milano, Milan, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2106-0374","authenticated-orcid":false,"given":"Emanuele","family":"Ogliari","sequence":"additional","affiliation":[{"name":"Politecnico di Milano, Milan, Italy"}]}],"member":"263","reference":[{"key":"ref1","volume-title":"2030 Climate & Energy Framework","author":"Commission","year":"2030"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1016\/j.rser.2012.11.042"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3036556"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1016\/j.rser.2022.112671"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2023.02.031"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1177\/09544070231153440"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1016\/j.est.2023.107028"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/MIE.2013.2250351"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.3390\/batteries10010034"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1016\/j.est.2022.104061"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1016\/j.egyr.2021.10.095"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1016\/j.etran.2019.100005"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1016\/j.jpowsour.2019.227575"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.3390\/wevj13090159"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2942213"},{"issue":"3","key":"ref16","doi-asserted-by":"crossref","first-page":"576","DOI":"10.3390\/forecast5030032","article-title":"Data-driven methods for the state of charge estimation of lithium-ion batteries: An overview","volume":"5","author":"Eleftheriadis","year":"2023","journal-title":"Forecasting"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/EEEIC\/ICPSEurope57605.2023.10194864"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1016\/j.est.2023.109071"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1016\/j.est.2021.103319"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/JESTPE.2020.3004972"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.3390\/en16145313"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2019.114408"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2017.2715333"},{"key":"ref24","volume-title":"PoliMi-TUB Dataset LG 18650HE4 Li-Ion Battery","year":"2024"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1016\/j.segan.2023.101160"},{"key":"ref26","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1016\/j.neucom.2020.07.061","article-title":"On hyperparameter optimization of machine learning algorithms: Theory and practice","volume":"415","author":"Yang","year":"2020","journal-title":"Neurocomputing"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2015.2494218"},{"key":"ref28","volume-title":"Keras-FLOPs","year":"2020"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/6287639\/10380310\/10550193.pdf?arnumber=10550193","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,25]],"date-time":"2024-06-25T21:03:02Z","timestamp":1719349382000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10550193\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"references-count":28,"URL":"https:\/\/doi.org\/10.1109\/access.2024.3410675","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]}}}