{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T03:07:11Z","timestamp":1778123231723,"version":"3.51.4"},"reference-count":38,"publisher":"Springer Science and Business Media LLC","license":[{"start":{"date-parts":[[2024,8,2]],"date-time":"2024-08-02T00:00:00Z","timestamp":1722556800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,8,2]],"date-time":"2024-08-02T00:00:00Z","timestamp":1722556800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"DOI":"10.1007\/s00521-024-10210-5","type":"journal-article","created":{"date-parts":[[2024,8,2]],"date-time":"2024-08-02T21:02:08Z","timestamp":1722632528000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Balancing accuracy and efficiency: a homogeneous ensemble approach for lithium-ion battery state of charge estimation in electric vehicles"],"prefix":"10.1007","author":[{"given":"Rae Hann","family":"Wong","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8486-9851","authenticated-orcid":false,"given":"Denesh","family":"Sooriamoorthy","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Aaruththiran","family":"Manoharan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nohaidda","family":"Binti Sariff","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zool","family":"Hilmi Ismail","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,8,2]]},"reference":[{"key":"10210_CR1","unstructured":"\u201cWorld Energy Outlook 2022,\u201d International Energy Agency. Accessed: May 25, 2023. [Online]. Available: https:\/\/www.iea.org\/reports\/world-energy-outlook-2022"},{"key":"10210_CR2","doi-asserted-by":"publisher","first-page":"227558","DOI":"10.1016\/j.jpowsour.2019.227558","volume":"449","author":"C Bian","year":"2020","unstructured":"Bian C, He H, Yang S, Huang T (2020) State-of-charge sequence estimation of lithium-ion battery based on bidirectional long short-term memory encoder-decoder architecture. J Power Sour 449:227558. https:\/\/doi.org\/10.1016\/j.jpowsour.2019.227558","journal-title":"J Power Sour"},{"key":"10210_CR3","doi-asserted-by":"publisher","unstructured":"A Manoharan and KM Begam (2023) Investigation on multiphase multistage bidirectional DC-DC converter for electric vehicles using virtual vehicle driving platform. In: 2023 IEEE 17th international conference on industrial and information systems, ICIIS 2023 \u2013 proceedings. pp. 275\u2013280. https:\/\/doi.org\/10.1109\/ICIIS58898.2023.10253526","DOI":"10.1109\/ICIIS58898.2023.10253526"},{"key":"10210_CR4","doi-asserted-by":"publisher","first-page":"106920","DOI":"10.1016\/j.resconrec.2023.106920","volume":"192","author":"F Maisel","year":"2023","unstructured":"Maisel F, Neef C, Marscheider-Weidemann F, Nissen NF (2023) A forecast on future raw material demand and recycling potential of lithium-ion batteries in electric vehicles. Resour Conserv Recycl 192:106920. https:\/\/doi.org\/10.1016\/j.resconrec.2023.106920","journal-title":"Resour Conserv Recycl"},{"issue":"9","key":"10210_CR5","doi-asserted-by":"publisher","first-page":"1592","DOI":"10.3390\/en12091592","volume":"12","author":"C Li","year":"2019","unstructured":"Li C, Xiao F, Fan Y (2019) An approach to state of charge estimation of lithium-ion batteries based on recurrent neural networks with gated recurrent unit. Energies 12(9):1592. https:\/\/doi.org\/10.3390\/en12091592","journal-title":"Energies"},{"issue":"13","key":"10210_CR6","doi-asserted-by":"publisher","first-page":"10307","DOI":"10.1002\/er.5654","volume":"44","author":"W Sun","year":"2020","unstructured":"Sun W, Qiu Y, Sun L, Hua Q (2020) Neural network-based learning and estimation of battery state-of-charge: a comparison study between direct and indirect methodology. Int J Energy Res 44(13):10307\u201310319. https:\/\/doi.org\/10.1002\/er.5654","journal-title":"Int J Energy Res"},{"key":"10210_CR7","doi-asserted-by":"publisher","unstructured":"NA Chaturvedi, R Klein, J Christensen, J Ahmed and A Kojic (2010) Modeling, estimation, and control challenges for lithium-ion batteries. Proceedings of the 2010 american control conference, ACC 2010. 1997\u20132002. https:\/\/doi.org\/10.1109\/acc.2010.5531623","DOI":"10.1109\/acc.2010.5531623"},{"key":"10210_CR8","doi-asserted-by":"publisher","first-page":"124328","DOI":"10.1016\/j.energy.2022.124328","volume":"254","author":"A Maheshwari","year":"2022","unstructured":"Maheshwari A, Nageswari S (2022) Real-time state of charge estimation for electric vehicle power batteries using optimized filter. Energy 254:124328. https:\/\/doi.org\/10.1016\/j.energy.2022.124328","journal-title":"Energy"},{"key":"10210_CR9","doi-asserted-by":"publisher","first-page":"272","DOI":"10.1016\/j.jpowsour.2012.10.060","volume":"226","author":"L Lu","year":"2013","unstructured":"Lu L, Han X, Li J, Hua J, Ouyang M (2013) A review on the key issues for lithium-ion battery management in electric vehicles. J Power Sour 226:272\u2013288. https:\/\/doi.org\/10.1016\/j.jpowsour.2012.10.060","journal-title":"J Power Sour"},{"key":"10210_CR10","doi-asserted-by":"publisher","unstructured":"A Manoharan, KM Begam, D Sooriamoorthy and VR Aparow (2023) Study on artificial neural network optimization for electric vehicle battery state of charge estimation. In: 2023 9th international conference on computer and communication engineering (ICCCE). pp. 334\u2013339. https:\/\/doi.org\/10.1109\/iccce58854.2023.10246092.","DOI":"10.1109\/iccce58854.2023.10246092"},{"issue":"1","key":"10210_CR11","doi-asserted-by":"publisher","first-page":"76","DOI":"10.1109\/TVT.2010.2089647","volume":"60","author":"KWE Cheng","year":"2011","unstructured":"Cheng KWE, Divakar BP, Wu H, Ding K, Ho HF (2011) Battery-management system (BMS) and SOC development for electrical vehicles. IEEE Trans Veh Technol 60(1):76\u201388. https:\/\/doi.org\/10.1109\/TVT.2010.2089647","journal-title":"IEEE Trans Veh Technol"},{"issue":"14","key":"10210_CR12","doi-asserted-by":"publisher","first-page":"4070","DOI":"10.3390\/en14144074","volume":"14","author":"K Movassagh","year":"2021","unstructured":"Movassagh K, Raihan A, Balasingam B, Pattipati K (2021) A critical look at coulomb counting approach for state of charge estimation in batteries. Energies (Basel) 14(14):4070. https:\/\/doi.org\/10.3390\/en14144074","journal-title":"Energies (Basel)"},{"key":"10210_CR13","doi-asserted-by":"publisher","first-page":"104141","DOI":"10.1016\/j.est.2022.104141","volume":"49","author":"BM Othman","year":"2022","unstructured":"Othman BM, Salam Z, Husain AR (2022) A computationally efficient adaptive online state-of-charge observer for Lithium-ion battery for electric vehicle. J Energy Storage 49:104141. https:\/\/doi.org\/10.1016\/j.est.2022.104141","journal-title":"J Energy Storage"},{"key":"10210_CR14","doi-asserted-by":"publisher","first-page":"102572","DOI":"10.1016\/j.est.2021.102572","volume":"39","author":"B Yang","year":"2021","unstructured":"Yang B et al (2021) Classification, summarization and perspectives on state-of-charge estimation of lithium-ion batteries used in electric vehicles: a critical comprehensive survey. J Energy Storage 39:102572. https:\/\/doi.org\/10.1016\/j.est.2021.102572","journal-title":"J Energy Storage"},{"key":"10210_CR15","doi-asserted-by":"publisher","first-page":"114789","DOI":"10.1016\/j.apenergy.2020.114789","volume":"265","author":"Y Tian","year":"2020","unstructured":"Tian Y, Lai R, Li X, Xiang L, Tian J (2020) A combined method for state-of-charge estimation for lithium-ion batteries using a long short-term memory network and an adaptive cubature Kalman filter. Appl Energy 265:114789. https:\/\/doi.org\/10.1016\/j.apenergy.2020.114789","journal-title":"Appl Energy"},{"key":"10210_CR16","doi-asserted-by":"publisher","first-page":"124110","DOI":"10.1016\/j.jclepro.2020.124110","volume":"277","author":"MS Hossain Lipu","year":"2020","unstructured":"Hossain Lipu MS et al (2020) Data-driven state of charge estimation of lithium-ion batteries: algorithms, implementation factors, limitations and future trends. J Clean Prod 277:124110. https:\/\/doi.org\/10.1016\/j.jclepro.2020.124110","journal-title":"J Clean Prod"},{"issue":"1","key":"10210_CR17","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-020-61464-7","volume":"10","author":"MA Hannan","year":"2020","unstructured":"Hannan MA et al (2020) Toward enhanced state of charge estimation of lithium-ion batteries using optimized machine learning techniques. Sci Rep 10(1):1\u201315. https:\/\/doi.org\/10.1038\/s41598-020-61464-7","journal-title":"Sci Rep"},{"key":"10210_CR18","doi-asserted-by":"publisher","first-page":"53792","DOI":"10.1109\/ACCESS.2019.2912803","volume":"7","author":"F Yang","year":"2019","unstructured":"Yang F, Song X, Xu F, Tsui KL (2019) State-of-charge estimation of lithium-ion batteries via long short-term memory network. IEEE Access 7:53792\u201353799. https:\/\/doi.org\/10.1109\/ACCESS.2019.2912803","journal-title":"IEEE Access"},{"key":"10210_CR19","doi-asserted-by":"publisher","first-page":"98168","DOI":"10.1109\/ACCESS.2020.2996225","volume":"8","author":"F Zhao","year":"2020","unstructured":"Zhao F, Li Y, Wang X, Bai L, Liu T (2020) Lithium-ion batteries state of charge prediction of electric vehicles using RNNs-CNNs neural networks. IEEE Access 8:98168\u201398180. https:\/\/doi.org\/10.1109\/ACCESS.2020.2996225","journal-title":"IEEE Access"},{"key":"10210_CR20","doi-asserted-by":"publisher","first-page":"120043","DOI":"10.1016\/j.apenergy.2022.120043","volume":"326","author":"P Takyi-Aninakwa","year":"2022","unstructured":"Takyi-Aninakwa P, Wang S, Zhang H, Yang X, Fernandez C (2022) An optimized long short-term memory-weighted fading extended Kalman filtering model with wide temperature adaptation for the state of charge estimation of lithium-ion batteries. Appl Energy 326:120043. https:\/\/doi.org\/10.1016\/j.apenergy.2022.120043","journal-title":"Appl Energy"},{"key":"10210_CR21","doi-asserted-by":"publisher","first-page":"758","DOI":"10.3390\/en14030758","volume":"14","author":"G Javid","year":"2021","unstructured":"Javid G, Ould Abdeslam D, Basset M (2021) Adaptive online state of charge estimation of EVs lithium-ion batteries with deep recurrent neural networks. Energies (Basel) 14:758. https:\/\/doi.org\/10.3390\/en14030758","journal-title":"Energies (Basel)"},{"key":"10210_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2019.116538","author":"C Bian","year":"2020","unstructured":"Bian C, He H, Yang S (2020) Stacked bidirectional long short-term memory networks for state-of-charge estimation of lithium-ion batteries. Energy. https:\/\/doi.org\/10.1016\/j.energy.2019.116538","journal-title":"Energy"},{"key":"10210_CR23","doi-asserted-by":"publisher","first-page":"108333","DOI":"10.1016\/j.est.2023.108333","volume":"72","author":"A Manoharan","year":"2023","unstructured":"Manoharan A, Sooriamoorthy D, Begam KM, Rau V (2023) Electric vehicle battery pack state of charge estimation using parallel artificial neural networks. J Energy Storage 72:108333. https:\/\/doi.org\/10.1016\/j.est.2023.108333","journal-title":"J Energy Storage"},{"key":"10210_CR24","doi-asserted-by":"publisher","first-page":"104664","DOI":"10.1016\/J.EST.2022.104664","volume":"52","author":"Y Liu","year":"2022","unstructured":"Liu Y, He Y, Bian H, Guo W, Zhang X (2022) A review of lithium-ion battery state of charge estimation based on deep learning: directions for improvement and future trends. J Energy Storage 52:104664. https:\/\/doi.org\/10.1016\/J.EST.2022.104664","journal-title":"J Energy Storage"},{"key":"10210_CR25","doi-asserted-by":"publisher","first-page":"105537","DOI":"10.1016\/J.EST.2022.105537","volume":"55","author":"C Hu","year":"2022","unstructured":"Hu C, Li B, Ma L, Cheng F (2022) State-of-charge estimation for lithium-ion batteries of electric vehicle based on sensor random error compensation. J Energy Storage 55:105537. https:\/\/doi.org\/10.1016\/J.EST.2022.105537","journal-title":"J Energy Storage"},{"key":"10210_CR26","doi-asserted-by":"publisher","unstructured":"Y Zhu, F Yan, J Kang and C Du (2018) State of charge estimation based on state of health correction for lithium-ion batteries. In: 2018 IEEE intelligent vehicles symposium (IV). pp. 1578\u20131583. https:\/\/doi.org\/10.1109\/IVS.2018.8500654","DOI":"10.1109\/IVS.2018.8500654"},{"issue":"2","key":"10210_CR27","doi-asserted-by":"publisher","first-page":"241","DOI":"10.1007\/s11704-019-8208-z","volume":"14","author":"X Dong","year":"2020","unstructured":"Dong X, Yu Z, Cao W, Shi Y, Ma Q (2020) A survey on ensemble learning. Front Comput Sci 14(2):241\u2013258. https:\/\/doi.org\/10.1007\/s11704-019-8208-z","journal-title":"Front Comput Sci"},{"issue":"2","key":"10210_CR28","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3054925","volume":"50","author":"HM Gomes","year":"2017","unstructured":"Gomes HM, Barddal JP, Enembreck AF, Bifet A (2017) A survey on ensemble learning for data stream classification. ACM Comput Surv 50(2):1\u201336. https:\/\/doi.org\/10.1145\/3054925","journal-title":"ACM Comput Surv"},{"key":"10210_CR29","doi-asserted-by":"publisher","unstructured":"RH Wong, A Manoharan, D Sooriamoorthy and NB Sarif (2023) A homogeneous meta-learning LSTM-RNN ensemble method for electric vehicle battery state of charge estimation. In: 2023 9th international conference on computer and communication engineering (ICCCE). 511: 367\u2013372. https:\/\/doi.org\/10.1109\/iccce58854.2023.10246077","DOI":"10.1109\/iccce58854.2023.10246077"},{"key":"10210_CR30","doi-asserted-by":"publisher","first-page":"124415","DOI":"10.1016\/j.energy.2022.124415","volume":"254","author":"Z Ni","year":"2022","unstructured":"Ni Z, Xiu X, Yang Y (2022) Towards efficient state of charge estimation of lithium-ion batteries using canonical correlation analysis. Energy 254:124415. https:\/\/doi.org\/10.1016\/j.energy.2022.124415","journal-title":"Energy"},{"key":"10210_CR31","doi-asserted-by":"publisher","first-page":"513","DOI":"10.1016\/j.apenergy.2016.09.010","volume":"183","author":"F Zheng","year":"2016","unstructured":"Zheng F, Xing Y, Jiang J, Sun B, Kim J, Pecht M (2016) Influence of different open circuit voltage tests on state of charge online estimation for lithium-ion batteries. Appl Energy 183:513\u2013525. https:\/\/doi.org\/10.1016\/j.apenergy.2016.09.010","journal-title":"Appl Energy"},{"issue":"8","key":"10210_CR32","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735\u20131780. https:\/\/doi.org\/10.1162\/neco.1997.9.8.1735","journal-title":"Neural Comput"},{"key":"10210_CR33","doi-asserted-by":"publisher","unstructured":"C Li, F Xiao, Y Fan, G Yang and W Zhang (2019) A recurrent neural network with long short-term memory for state of charge estimation of lithium-ion batteries. In: 2019 IEEE 8th joint international information technology and artificial intelligence conference (ITAIC). IEEE, pp. 1712\u20131716 TS-CrossRef. https:\/\/doi.org\/10.1109\/ITAIC.2019.8785770","DOI":"10.1109\/ITAIC.2019.8785770"},{"issue":"3","key":"10210_CR34","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1109\/MCAS.2006.1688199","volume":"6","author":"R Polikar","year":"2006","unstructured":"Polikar R (2006) Ensemble based systems in decision making. IEEE Circuits Syst Mag 6(3):21\u201344. https:\/\/doi.org\/10.1109\/MCAS.2006.1688199","journal-title":"IEEE Circuits Syst Mag"},{"issue":"10","key":"10210_CR35","doi-asserted-by":"publisher","first-page":"1806","DOI":"10.3390\/diagnostics13101806","volume":"13","author":"GS Chakraborty","year":"2023","unstructured":"Chakraborty GS, Batra S, Singh A, Muhammad G, Torres VY, Mahajan M (2023) a novel deep learning-based classification framework for COVID-19 assisted with weighted average ensemble modeling. Diagnostics 13(10):1806. https:\/\/doi.org\/10.3390\/diagnostics13101806","journal-title":"Diagnostics"},{"key":"10210_CR36","doi-asserted-by":"publisher","unstructured":"Y Han, Y Shao and Y Zhang (2023) Happiness index prediction using hybrid regression model. In: proceedings of the 2nd international academic conference on blockchain, information technology and smart finance (ICBIS 2023). pp. 76\u201387. https:\/\/doi.org\/10.2991\/978-94-6463-198-2_9","DOI":"10.2991\/978-94-6463-198-2_9"},{"key":"10210_CR37","unstructured":"DP Kingma and JL Ba (2015) Adam: a method for stochastic optimization. In: 3rd international conference on learning representations, ICLR 2015\u2014conference track proceedings. pp. 1\u201315"},{"key":"10210_CR38","unstructured":"Simplified version of the federal urban driving schedule for electric vehicle battery testing. | national technical reports library\u2014NTIS. Accessed: Jul. 02, 2023. [Online]. Available: https:\/\/ntrl.ntis.gov\/NTRL\/dashboard\/searchResults\/titleDetail\/DE89004839.xhtml"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-10210-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-024-10210-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-10210-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,2]],"date-time":"2024-08-02T21:03:10Z","timestamp":1722632590000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-024-10210-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,2]]},"references-count":38,"alternative-id":["10210"],"URL":"https:\/\/doi.org\/10.1007\/s00521-024-10210-5","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,8,2]]},"assertion":[{"value":"15 January 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 July 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 August 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}