{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T03:50:01Z","timestamp":1783396201876,"version":"3.54.6"},"reference-count":45,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2023,9,19]],"date-time":"2023-09-19T00:00:00Z","timestamp":1695081600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,9,19]],"date-time":"2023-09-19T00:00:00Z","timestamp":1695081600000},"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":["J Supercomput"],"published-print":{"date-parts":[[2024,3]]},"DOI":"10.1007\/s11227-023-05648-8","type":"journal-article","created":{"date-parts":[[2023,9,19]],"date-time":"2023-09-19T13:02:09Z","timestamp":1695128529000},"page":"4707-4732","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":36,"title":["Accurate remaining useful life estimation of lithium-ion batteries in electric vehicles based on a measurable feature-based approach with explainable AI"],"prefix":"10.1007","volume":"80","author":[{"given":"Sadiqa","family":"Jafari","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yung Cheol","family":"Byun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,9,19]]},"reference":[{"key":"5648_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.envres.2021.110718","volume":"194","author":"J Xiong","year":"2021","unstructured":"Xiong J, Xu D (2021) Relationship between energy consumption, economic growth and environmental pollution in China. Environ Res 194:110718","journal-title":"Environ Res"},{"key":"5648_CR2","doi-asserted-by":"publisher","first-page":"245","DOI":"10.1016\/j.neucom.2020.07.081","volume":"414","author":"L Chen","year":"2020","unstructured":"Chen L, Zhang Y, Zheng Y, Li X, Zheng X (2020) Remaining useful life prediction of lithium-ion battery with optimal input sequence selection and error compensation. Neurocomputing 414:245\u2013254","journal-title":"Neurocomputing"},{"issue":"4","key":"5648_CR3","doi-asserted-by":"publisher","first-page":"898","DOI":"10.1080\/00207179.2018.1487083","volume":"93","author":"Y Zhang","year":"2020","unstructured":"Zhang Y, Wang Z, Alsaadi FE (2020) Detection of intermittent faults for nonuniformly sampled multi-rate systems with dynamic quantisation and missing measurements. Int J Control 93(4):898\u2013909","journal-title":"Int J Control"},{"key":"5648_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2020.119079","volume":"215","author":"D \u0160eruga","year":"2021","unstructured":"\u0160eruga D, Gosar A, Sweeney CA, Jaguemont J, Van Mierlo J, Nagode M (2021) Continuous modelling of cyclic ageing for lithium-ion batteries. Energy 215:119079","journal-title":"Energy"},{"key":"5648_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.etran.2022.100206","volume-title":"A sustainable framework for the second-life battery ecosystem based on blockchain","author":"M Cheng","year":"2022","unstructured":"Cheng M, Sun H, Wei G, Zhou G, Zhang X (2022) A sustainable framework for the second-life battery ecosystem based on blockchain. Elsevier, Amsterdam"},{"key":"5648_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.ress.2021.108082","volume":"217","author":"T Tang","year":"2022","unstructured":"Tang T, Yuan H (2022) A hybrid approach based on decomposition algorithm and neural network for remaining useful life prediction of lithium-ion battery. Reliab Eng Syst Saf 217:108082","journal-title":"Reliab Eng Syst Saf"},{"key":"5648_CR7","doi-asserted-by":"publisher","first-page":"166","DOI":"10.1016\/j.ress.2018.04.030","volume":"182","author":"G Sierra","year":"2019","unstructured":"Sierra G, Orchard M, Goebel K, Kulkarni C (2019) Battery health management for small-size rotary-wing electric unmanned aerial vehicles: An efficient approach for constrained computing platforms. Reliab Eng Syst Saf 182:166\u2013178","journal-title":"Reliab Eng Syst Saf"},{"issue":"2","key":"5648_CR8","doi-asserted-by":"publisher","first-page":"2871","DOI":"10.1021\/acsami.1c21263","volume":"14","author":"Z Liu","year":"2022","unstructured":"Liu Z, He B, Zhang Z, Deng W, Dong D, Xia S, Zhou X, Liu Z (2022) Lithium\/graphene composite anode with 3D structural LiF protection layer for high-performance lithium metal batteries. ACS Appl Mater Interfaces 14(2):2871\u20132880","journal-title":"ACS Appl Mater Interfaces"},{"key":"5648_CR9","doi-asserted-by":"publisher","first-page":"453","DOI":"10.1016\/j.jpowsour.2018.06.036","volume":"396","author":"X Tang","year":"2018","unstructured":"Tang X, Zou C, Yao K, Chen G, Liu B, He Z, Gao F (2018) A fast estimation algorithm for lithium-ion battery state of health. J Power Sour 396:453\u2013458","journal-title":"J Power Sour"},{"key":"5648_CR10","doi-asserted-by":"publisher","first-page":"114","DOI":"10.1016\/j.apenergy.2013.12.020","volume":"118","author":"SSY Ng","year":"2014","unstructured":"Ng SSY, Xing Y, Tsui KL (2014) A naive Bayes model for robust remaining useful life prediction of lithium-ion battery. Appl Energy 118:114\u2013123","journal-title":"Appl Energy"},{"key":"5648_CR11","doi-asserted-by":"publisher","first-page":"5562","DOI":"10.1016\/j.egyr.2021.08.182","volume":"7","author":"S Wang","year":"2021","unstructured":"Wang S, Jin S, Bai D, Fan Y, Shi H, Fernandez C (2021) A critical review of improved deep learning methods for the remaining useful life prediction of lithium-ion batteries. Energy Rep 7:5562\u20135574","journal-title":"Energy Rep"},{"key":"5648_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2021.122581","volume":"244","author":"L Chen","year":"2022","unstructured":"Chen L, Ding Y, Liu B, Wu S, Wang Y, Pan H (2022) Remaining useful life prediction of lithium-ion battery using a novel particle filter framework with grey neural network. Energy 244:122581","journal-title":"Energy"},{"key":"5648_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.jclepro.2020.120813","volume":"261","author":"H Tian","year":"2020","unstructured":"Tian H, Qin P, Li K, Zhao Z (2020) A review of the state of health for lithium-ion batteries: research status and suggestions. J Clean Prod 261:120813","journal-title":"J Clean Prod"},{"key":"5648_CR14","doi-asserted-by":"crossref","unstructured":"Lashgari F, Petkovski E, Cristaldi L (2022) State of health analysis for lithium-ion batteries considering temperature effect. In: 2022 IEEE International Conference on Metrology for Extended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE), IEEE, pp 40\u201345","DOI":"10.1109\/MetroXRAINE54828.2022.9967550"},{"key":"5648_CR15","doi-asserted-by":"crossref","unstructured":"Barcellona S, Cristaldi L, Faifer M, Petkovski E, Piegari L, Toscani S (2021) State of health prediction of lithium-ion batteries. In: 2021 IEEE International Workshop on Metrology for Industry 4.0 & IoT (MetroInd4. 0 &IoT), IEEE, pp 12\u201317","DOI":"10.1109\/MetroInd4.0IoT51437.2021.9488542"},{"key":"5648_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.est.2019.100951","volume":"26","author":"S Zhang","year":"2019","unstructured":"Zhang S, Zhai B, Guo X, Wang K, Peng N, Zhang X (2019) Synchronous estimation of state of health and remaining useful lifetime for lithium-ion battery using the incremental capacity and artificial neural networks. J Energy Storage 26:100951","journal-title":"J Energy Storage"},{"key":"5648_CR17","doi-asserted-by":"publisher","first-page":"310","DOI":"10.1016\/j.joule.2019.11.018","volume":"4","author":"X Hu","year":"2020","unstructured":"Hu X, Xu L, Lin X, Pecht M (2020) Battery lifetime prognostics. Joule 4:310\u2013346","journal-title":"Joule"},{"key":"5648_CR18","doi-asserted-by":"publisher","first-page":"1934","DOI":"10.1016\/j.joule.2021.06.005","volume":"5","author":"V Sulzer","year":"2021","unstructured":"Sulzer V, Mohtat P, Aitio A, Lee S, Yeh YT, Steinbacher F, Khan MU, Lee JW, Siegel JB, Stefanopoulou AG (2021) The challenge and opportunity of battery lifetime prediction from field data. Joule 5:1934\u20131955","journal-title":"Joule"},{"key":"5648_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2021.117386","volume":"300","author":"Y Tian","year":"2021","unstructured":"Tian Y, Lin C, Li H, Du J, Xiong R (2021) Detecting undesired lithium plating on anodes for lithium-ion batteries\u2013a review on the in-situ methods. Appl Energy 300:117386","journal-title":"Appl Energy"},{"issue":"3","key":"5648_CR20","doi-asserted-by":"publisher","first-page":"59","DOI":"10.3390\/wevj11030059","volume":"11","author":"B Yu","year":"2020","unstructured":"Yu B, Qiu H, Weng L, Huo K, Liu S, Liu H (2020) A health indicator for the online lifetime estimation of an electric vehicle power Li-ion battery. World Electr Veh J 11(3):59","journal-title":"World Electr Veh J"},{"key":"5648_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2023.120954","volume":"339","author":"D Zhongwei","year":"2023","unstructured":"Zhongwei D, Xu L, Liu H, Hu X, Duan Z, Xu Y (2023) Prognostics of battery capacity based on charging data and data-driven methods for on-road vehicles. Appl Energy 339:120954","journal-title":"Appl Energy"},{"key":"5648_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2022.125123","volume":"260","author":"J Li","year":"2022","unstructured":"Li J, Deng Z, Liu H, Xie Y, Liu C, Chen L (2022) Battery capacity trajectory prediction by capturing the correlation between different vehicles. Energy 260:125123","journal-title":"Energy"},{"key":"5648_CR23","doi-asserted-by":"publisher","DOI":"10.1016\/j.est.2020.101271","volume":"28","author":"MF Niri","year":"2020","unstructured":"Niri MF et al (2020) Remaining energy estimation for lithium-ion batteries via Gaussian mixture and Markov models for future load prediction. J Energy Storage 28:101271","journal-title":"J Energy Storage"},{"key":"5648_CR24","doi-asserted-by":"publisher","first-page":"155871","DOI":"10.1109\/ACCESS.2021.3128774","volume":"9","author":"TMN Bui","year":"2021","unstructured":"Bui TMN et al (2021) A study of reduced battery degradation through state-of-charge pre-conditioning for vehicle-to-grid operations. IEEE Access 9:155871\u2013155896","journal-title":"IEEE Access"},{"issue":"9","key":"5648_CR25","doi-asserted-by":"publisher","first-page":"5833","DOI":"10.1109\/TITS.2020.3028024","volume":"22","author":"MF Niri","year":"2020","unstructured":"Niri MF et al (2020) State of power prediction for lithium-ion batteries in electric vehicles via Wavelet\u2013Markov load analysis. IEEE Trans Intell Transp Syst 22(9):5833\u20135848","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"5648_CR26","doi-asserted-by":"publisher","first-page":"1830","DOI":"10.1016\/j.procs.2022.12.383","volume":"217","author":"W Song","year":"2023","unstructured":"Song W, Wu D, Shen W, Boulet B (2023) A remaining useful life prediction method for lithium-ion battery based on temporal transformer network. Procedia Comput Sci 217:1830\u20131838","journal-title":"Procedia Comput Sci"},{"key":"5648_CR27","doi-asserted-by":"publisher","DOI":"10.1016\/j.jpowsour.2020.228861","volume":"481","author":"KK Sadabadi","year":"2021","unstructured":"Sadabadi KK, Jin X, Rizzoni G (2021) Prediction of remaining useful life for a composite electrode lithium ion battery cell using an electrochemical model to estimate the state of health. J Power Sour 481:228861","journal-title":"J Power Sour"},{"key":"5648_CR28","doi-asserted-by":"publisher","first-page":"50587","DOI":"10.1109\/ACCESS.2018.2858856","volume":"6","author":"L Ren","year":"2018","unstructured":"Ren L, Zhao L, Hong S, Zhao S, Wang H, Zhang L (2018) Remaining useful life prediction for lithium-ion battery: a deep learning approach. IEEE Access 6:50587\u201350598","journal-title":"IEEE Access"},{"issue":"5","key":"5648_CR29","doi-asserted-by":"publisher","first-page":"4252","DOI":"10.1109\/TVT.2021.3071622","volume":"70","author":"B Zraibi","year":"2021","unstructured":"Zraibi B, Okar C, Chaoui H, Mansouri M (2021) Remaining useful life assessment for lithium-ion batteries using CNN-LSTM-DNN hybrid method. IEEE Trans Veh Technol 70(5):4252\u20134261","journal-title":"IEEE Trans Veh Technol"},{"issue":"20","key":"5648_CR30","doi-asserted-by":"publisher","first-page":"13525","DOI":"10.1007\/s00521-021-05976-x","volume":"33","author":"A Kara","year":"2021","unstructured":"Kara A (2021) A data-driven approach based on deep neural networks for lithium-ion battery prognostics. Neural Comput Appl 33(20):13525\u201313538","journal-title":"Neural Comput Appl"},{"key":"5648_CR31","doi-asserted-by":"publisher","DOI":"10.1016\/j.est.2022.104520","volume":"51","author":"Y Toughzaoui","year":"2022","unstructured":"Toughzaoui Y, Toosi SB, Chaoui H, Louahlia H, Petrone R, Le Masson S, Gualous H (2022) State of health estimation and remaining useful life assessment of lithium-ion batteries: a comparative study. J Energy Storage 51:104520","journal-title":"J Energy Storage"},{"key":"5648_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.ress.2022.108481","volume":"224","author":"RR Ardeshiri","year":"2022","unstructured":"Ardeshiri RR, Liu M, Ma C (2022) Multivariate stacked bidirectional long short term memory for lithium-ion battery health management. Reliab Eng Syst Saf 224:108481","journal-title":"Reliab Eng Syst Saf"},{"key":"5648_CR33","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2022.123622","volume":"248","author":"F Yao","year":"2022","unstructured":"Yao F, He W, Wu Y, Ding F, Meng D (2022) Remaining useful life prediction of lithium-ion batteries using a hybrid model. Energy 248:123622","journal-title":"Energy"},{"issue":"4","key":"5648_CR34","doi-asserted-by":"publisher","first-page":"3170","DOI":"10.1109\/TIE.2020.2973876","volume":"68","author":"K Liu","year":"2020","unstructured":"Liu K, Shang Y, Ouyang Q, Widanage WD (2020) A data-driven approach with uncertainty quantification for predicting future capacities and remaining useful life of lithium-ion battery. IEEE Trans Ind Electron 68(4):3170\u20133180","journal-title":"IEEE Trans Ind Electron"},{"key":"5648_CR35","doi-asserted-by":"publisher","first-page":"160043","DOI":"10.1109\/ACCESS.2019.2947843","volume":"7","author":"J Fan","year":"2019","unstructured":"Fan J, Fan J, Liu F, Qu J, Li R (2019) A novel machine learning method based approach for Li-ion battery prognostic and health management. IEEE Access 7:160043\u2013160061","journal-title":"IEEE Access"},{"key":"5648_CR36","unstructured":"Saha B, Goebel K (2007) NASA Ames prognostics data repository. NASA Ames: moffett field, CA, USA, 2007. Available at: http:\/\/ti.arc.nasa.gov\/project\/prognostic-data-repository"},{"issue":"13","key":"5648_CR37","doi-asserted-by":"publisher","first-page":"4753","DOI":"10.3390\/en15134753","volume":"15","author":"S Jafari","year":"2022","unstructured":"Jafari S et al (2022) Lithium-ion battery health prediction on hybrid vehicles using machine learning approach. Energies 15(13):4753","journal-title":"Energies"},{"key":"5648_CR38","doi-asserted-by":"publisher","first-page":"124685","DOI":"10.1109\/ACCESS.2022.3225093","volume":"10","author":"S Jafari","year":"2022","unstructured":"Jafari S, Byun Y-C (2022) Prediction of the battery state using the digital twin framework based on the battery management system. IEEE Access 10:124685\u2013124696","journal-title":"IEEE Access"},{"issue":"3","key":"5648_CR39","first-page":"1","volume":"70","author":"Z Shahbazi","year":"2022","unstructured":"Shahbazi Z, Byun Y-C (2022) Blockchain and machine learning for intelligent multiple factor-based ride-hailing services. Comput Mater Contin 70(3):1\u201318","journal-title":"Comput Mater Contin"},{"key":"5648_CR40","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1007\/s11192-018-2961-x","volume":"118","author":"F Qayyum","year":"2019","unstructured":"Qayyum F, Afzal MT (2019) Identification of important citations by exploiting research articles\u2019 metadata and cue-terms from content. Scientometrics 118:21\u201343","journal-title":"Scientometrics"},{"issue":"11","key":"5648_CR41","doi-asserted-by":"publisher","first-page":"6471","DOI":"10.1007\/s11192-022-04530-3","volume":"127","author":"F Qayyum","year":"2022","unstructured":"Qayyum F et al (2022) Toward potential hybrid features evaluation using MLP-ANN binary classification model to tackle meaningful citations. Scientometrics 127(11):6471\u20136499","journal-title":"Scientometrics"},{"issue":"10","key":"5648_CR42","doi-asserted-by":"publisher","first-page":"1973","DOI":"10.3390\/sym13101973","volume":"13","author":"F Qayyum","year":"2021","unstructured":"Qayyum F et al (2021) Towards potential content-based features evaluation to tackle meaningful citations. Symmetry 13(10):1973","journal-title":"Symmetry"},{"key":"5648_CR43","doi-asserted-by":"publisher","first-page":"53307","DOI":"10.1109\/ACCESS.2020.2981261","volume":"8","author":"D Zhou","year":"2020","unstructured":"Zhou D et al (2020) State of health monitoring and remaining useful life prediction of lithium-ion batteries based on temporal convolutional network. IEEE Access 8:53307\u201353320","journal-title":"IEEE Access"},{"key":"5648_CR44","doi-asserted-by":"publisher","DOI":"10.1016\/j.ress.2022.108947","volume":"230","author":"Y Wei","year":"2023","unstructured":"Wei Y, Wu D (2023) Prediction of state of health and remaining useful life of lithium-ion battery using graph convolutional network with dual attention mechanisms. Reliab Eng Syst Saf 230:108947","journal-title":"Reliab Eng Syst Saf"},{"issue":"7","key":"5648_CR45","doi-asserted-by":"publisher","first-page":"6261","DOI":"10.3390\/su15076261","volume":"15","author":"X Tang","year":"2023","unstructured":"Tang X et al (2023) Lithium-ion battery remaining useful life prediction based on hybrid model. Sustainability 15(7):6261","journal-title":"Sustainability"}],"container-title":["The Journal of Supercomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-023-05648-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11227-023-05648-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-023-05648-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,17]],"date-time":"2024-02-17T11:18:06Z","timestamp":1708168686000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11227-023-05648-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,19]]},"references-count":45,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2024,3]]}},"alternative-id":["5648"],"URL":"https:\/\/doi.org\/10.1007\/s11227-023-05648-8","relation":{},"ISSN":["0920-8542","1573-0484"],"issn-type":[{"value":"0920-8542","type":"print"},{"value":"1573-0484","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,9,19]]},"assertion":[{"value":"2 September 2023","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 September 2023","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to participate"}}]}}