{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T13:34:42Z","timestamp":1776087282581,"version":"3.50.1"},"reference-count":53,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T00:00:00Z","timestamp":1756684800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T00:00:00Z","timestamp":1756684800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T00:00:00Z","timestamp":1756684800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T00:00:00Z","timestamp":1756684800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T00:00:00Z","timestamp":1756684800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T00:00:00Z","timestamp":1756684800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T00:00:00Z","timestamp":1756684800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/100031839","name":"Korea Research Institute for Defense Technology Planning and Advancement","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100031839","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002642","name":"Korea University","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100002642","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003626","name":"DAPA","doi-asserted-by":"publisher","award":["21-107-D00-009"],"award-info":[{"award-number":["21-107-D00-009"]}],"id":[{"id":"10.13039\/501100003626","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Applied Soft Computing"],"published-print":{"date-parts":[[2025,9]]},"DOI":"10.1016\/j.asoc.2025.113526","type":"journal-article","created":{"date-parts":[[2025,6,27]],"date-time":"2025-06-27T11:24:35Z","timestamp":1751023475000},"page":"113526","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":3,"special_numbering":"C","title":["Random forest-based small training dataset complementation and features selection for capacity estimation of lithium-ion batteries in electric-powered application"],"prefix":"10.1016","volume":"181","author":[{"given":"Seunghwa","family":"Sin","sequence":"first","affiliation":[]},{"given":"Eunjin","family":"Kang","sequence":"additional","affiliation":[]},{"given":"Jonghoon","family":"Kim","sequence":"additional","affiliation":[]},{"given":"Sein","family":"Oh","sequence":"additional","affiliation":[]},{"given":"Jongbok","family":"Baek","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"issue":"April","key":"10.1016\/j.asoc.2025.113526_bib1","article-title":"Concept of reliability and safety assessment of lithium-ion batteries in electric vehicles: basics, progress, and challenges","volume":"251","author":"Gandoman","year":"2019","journal-title":"Appl. Energy"},{"key":"10.1016\/j.asoc.2025.113526_bib2","doi-asserted-by":"crossref","DOI":"10.1016\/j.jclepro.2021.126044","article-title":"Intelligent algorithms and control strategies for battery management system in electric vehicles: progress, challenges and future outlook","volume":"292","author":"Hossain Lipu","year":"2021","journal-title":"J. Clean. Prod."},{"issue":"May 2022","key":"10.1016\/j.asoc.2025.113526_bib3","article-title":"Co-estimation of lithium-ion battery state-of-charge and state-of-health based on fractional-order model","volume":"65","author":"Ye","year":"2023","journal-title":"J. Energy Storage"},{"issue":"May 2022","key":"10.1016\/j.asoc.2025.113526_bib4","article-title":"A novel remaining useful life prediction method for lithium-ion battery based on long short-term memory network optimized by improved sparrow search algorithm","volume":"61","author":"Liu","year":"2023","journal-title":"J. Energy Storage"},{"key":"10.1016\/j.asoc.2025.113526_bib5","doi-asserted-by":"crossref","DOI":"10.1016\/j.etran.2019.100005","article-title":"A review on the key issues of the lithium ion battery degradation among the whole life cycle","volume":"1","author":"Han","year":"2019","journal-title":"ETransportation"},{"key":"10.1016\/j.asoc.2025.113526_bib6","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1016\/j.jpowsour.2016.12.011","article-title":"Degradation diagnostics for lithium ion cells","volume":"341","author":"Birkl","year":"2017","journal-title":"J. Power Sources"},{"issue":"PA","key":"10.1016\/j.asoc.2025.113526_bib7","article-title":"SOH prediction of lithium battery based on IC curve feature and BP neural network","volume":"261","author":"Wen","year":"2022","journal-title":"Energy"},{"issue":"March","key":"10.1016\/j.asoc.2025.113526_bib8","article-title":"Data-driven state-of-health estimation for lithium-ion battery based on aging features","volume":"274","author":"Li","year":"2023","journal-title":"Energy"},{"issue":"March","key":"10.1016\/j.asoc.2025.113526_bib9","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/j.est.2018.07.006","article-title":"Revisiting the dual extended Kalman filter for battery state-of-charge and state-of-health estimation: a use-case life cycle analysis","volume":"19","author":"Wassiliadis","year":"2018","journal-title":"J. Energy Storage"},{"issue":"October 2017","key":"10.1016\/j.asoc.2025.113526_bib10","doi-asserted-by":"crossref","first-page":"586","DOI":"10.1016\/j.measurement.2017.11.016","article-title":"A new state-of-health estimation method for lithium-ion batteries through the intrinsic relationship between ohmic internal resistance and capacity","volume":"116","author":"Chen","year":"2018","journal-title":"Meas. J. Int. Meas. Confed."},{"issue":"July","key":"10.1016\/j.asoc.2025.113526_bib11","article-title":"A novel deep learning framework for state of health estimation of lithium-ion battery","volume":"32","author":"Fan","year":"2020","journal-title":"J. Energy Storage"},{"issue":"February","key":"10.1016\/j.asoc.2025.113526_bib12","article-title":"A data-driven method for state of health prediction of lithium-ion batteries in a unified framework","volume":"51","author":"Cai","year":"2022","journal-title":"J. Energy Storage"},{"key":"10.1016\/j.asoc.2025.113526_bib13","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1016\/j.jechem.2022.06.049","article-title":"A review of deep learning approach to predicting the state of health and state of charge of lithium-ion batteries","volume":"74","author":"Luo","year":"2022","journal-title":"J. Energy Chem."},{"issue":"September","key":"10.1016\/j.asoc.2025.113526_bib14","article-title":"Data-driven state of health modelling\u2014A review of state of the art and reflections on applications for maritime battery systems","volume":"43","author":"Vanem","year":"2021","journal-title":"J. Energy Storage"},{"issue":"PC","key":"10.1016\/j.asoc.2025.113526_bib15","article-title":"Deep learning enabled state of charge, state of health and remaining useful life estimation for smart battery management system: methods, implementations, issues and prospects","volume":"55","author":"Hossain Lipu","year":"2022","journal-title":"J. Energy Storage"},{"key":"10.1016\/j.asoc.2025.113526_bib16","doi-asserted-by":"crossref","first-page":"2993","DOI":"10.1016\/j.egyr.2023.01.108","article-title":"A review of machine learning state-of-charge and state-of-health estimation algorithms for lithium-ion batteries","volume":"9","author":"Ren","year":"2023","journal-title":"Energy Rep."},{"key":"10.1016\/j.asoc.2025.113526_bib17","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1016\/j.jechem.2023.03.026","article-title":"The development of machine learning-based remaining useful life prediction for lithium-ion batteries","volume":"82","author":"Li","year":"2023","journal-title":"J. Energy Chem."},{"issue":"July","key":"10.1016\/j.asoc.2025.113526_bib18","article-title":"Machine learning for battery research","volume":"549","author":"Wei","year":"2022","journal-title":"J. Power Sources"},{"issue":"May 2022","key":"10.1016\/j.asoc.2025.113526_bib19","doi-asserted-by":"crossref","first-page":"894","DOI":"10.1016\/j.psep.2023.02.081","article-title":"Data-driven predictive prognostic model for power batteries based on machine learning","volume":"172","author":"Dong","year":"2023","journal-title":"Process Saf. Environ. Prot."},{"issue":"October 2021","key":"10.1016\/j.asoc.2025.113526_bib20","article-title":"A multi-feature-based multi-model fusion method for state of health estimation of lithium-ion batteries","volume":"518","author":"Lin","year":"2022","journal-title":"J. Power Sources"},{"issue":"October 2021","key":"10.1016\/j.asoc.2025.113526_bib21","article-title":"An estimation model for state of health of lithium-ion batteries using energy-based features","volume":"46","author":"Cai","year":"2022","journal-title":"J. Energy Storage"},{"issue":"January","key":"10.1016\/j.asoc.2025.113526_bib22","article-title":"Model-data fusion domain adaptation for battery State of Health estimation with fewer data and simplified feature extractor","volume":"60","author":"Wang","year":"2023","journal-title":"J. Energy Storage"},{"issue":"July","key":"10.1016\/j.asoc.2025.113526_bib23","article-title":"A novel deep learning framework for state of health estimation of lithium-ion battery","volume":"32","author":"Fan","year":"2020","journal-title":"J. Energy Storage"},{"issue":"May 2022","key":"10.1016\/j.asoc.2025.113526_bib24","article-title":"State of Health prediction of lithium-ion batteries based on temporal degeneration feature extraction with deep cycle attention network","volume":"65","author":"Zou","year":"2023","journal-title":"J. Energy Storage"},{"issue":"June","key":"10.1016\/j.asoc.2025.113526_bib25","article-title":"Applying convolutional neural networks to identify lithofacies of large-n cores from the Permian Basin and Gulf of Mexico: the importance of the quantity and quality of training data","volume":"133","author":"Zhang","year":"2021","journal-title":"Mar. Pet. Geol."},{"issue":"July","key":"10.1016\/j.asoc.2025.113526_bib26","article-title":"Effects of data quality and quantity on deep learning for protein-ligand binding affinity prediction","volume":"72","author":"Fan","year":"2022","journal-title":"Bioorg. Med. Chem."},{"issue":"May 2022","key":"10.1016\/j.asoc.2025.113526_bib27","article-title":"Prediction of remaining useful life and state of health of lithium batteries based on time series feature and Savitzky-Golay filter combined with gated recurrent unit neural network","volume":"270","author":"Guo","year":"2023","journal-title":"Energy"},{"issue":"September 2020","key":"10.1016\/j.asoc.2025.113526_bib28","article-title":"Lithium-ion battery state of health estimation using the incremental capacity and wavelet neural networks with genetic algorithm","volume":"38","author":"Chang","year":"2021","journal-title":"J. Energy Storage"},{"key":"10.1016\/j.asoc.2025.113526_bib29","doi-asserted-by":"crossref","first-page":"606","DOI":"10.1016\/j.egyr.2022.02.220","article-title":"State of health estimation of lithium-ion batteries based on a novel indirect health indicator","volume":"8","author":"Lin","year":"2022","journal-title":"Energy Rep."},{"issue":"March","key":"10.1016\/j.asoc.2025.113526_bib30","doi-asserted-by":"crossref","first-page":"458","DOI":"10.1016\/j.procir.2022.02.076","article-title":"State of health estimation of retired battery for echelon utilization based on charging curve","volume":"105","author":"Ma","year":"2022","journal-title":"Procedia CIRP"},{"issue":"July","key":"10.1016\/j.asoc.2025.113526_bib31","article-title":"State of health estimation of power batteries based on multi-feature fusion models using stacking algorithm","volume":"259","author":"Liu","year":"2022","journal-title":"Energy"},{"key":"10.1016\/j.asoc.2025.113526_bib32","doi-asserted-by":"crossref","DOI":"10.1016\/j.energy.2021.120160","article-title":"Estimation and prediction of state of health of electric vehicle batteries using discrete incremental capacity analysis based on real driving data","volume":"225","author":"Xu","year":"2021","journal-title":"Energy"},{"issue":"8","key":"10.1016\/j.asoc.2025.113526_bib33","doi-asserted-by":"crossref","first-page":"1934","DOI":"10.1016\/j.joule.2021.06.005","article-title":"The challenge and opportunity of battery lifetime prediction from field data","volume":"5","author":"Sulzer","year":"2021","journal-title":"Joule"},{"issue":"1","key":"10.1016\/j.asoc.2025.113526_bib34","doi-asserted-by":"crossref","DOI":"10.3390\/batteries9010007","article-title":"State of health estimation of lithium-ion batteries using a multi-feature-extraction strategy and PSO-NARXNN","volume":"9","author":"Ren","year":"2023","journal-title":"Batteries"},{"issue":"August 2020","key":"10.1016\/j.asoc.2025.113526_bib35","article-title":"A state-of-health estimation method of lithium-ion batteries based on multi-feature extracted from constant current charging curve","volume":"36","author":"Guo","year":"2021","journal-title":"J. Energy Storage"},{"issue":"June","key":"10.1016\/j.asoc.2025.113526_bib36","article-title":"Capacity estimation of batteries: influence of training dataset size and diversity on data driven prognostic models","volume":"216","author":"Nagulapati","year":"2021","journal-title":"Reliab. Eng. Syst. Saf."},{"issue":"March","key":"10.1016\/j.asoc.2025.113526_bib37","article-title":"Electric vehicle battery state of health estimation using incremental capacity analysis","volume":"64","author":"Gismero","year":"2023","journal-title":"J. Energy Storage"},{"issue":"66","key":"10.1016\/j.asoc.2025.113526_bib38","article-title":"State of health estimation for fast-charging lithium-ion battery based on incremental capacity analysis","volume":"51","author":"Zhou","year":"2022","journal-title":"J. Energy Storage"},{"issue":"September","key":"10.1016\/j.asoc.2025.113526_bib39","article-title":"Battery incremental capacity curve extraction by a two-dimensional Luenberger\u2013Gaussian-moving-average filter","volume":"280","author":"Tang","year":"2020","journal-title":"Appl. Energy"},{"issue":"Dec","key":"10.1016\/j.asoc.2025.113526_bib40","article-title":"A state-of-health estimation method based on incremental capacity analysis for Li-ion battery considering charging\/discharging rate","volume":"73","author":"Wang","year":"2023","journal-title":"J. Energy Storage"},{"key":"10.1016\/j.asoc.2025.113526_bib41","doi-asserted-by":"crossref","DOI":"10.1016\/j.jclepro.2020.125700","article-title":"Battery state-of-health modelling by multiple linear regression","volume":"290","author":"Vilsen","year":"2021","journal-title":"J. Clean. Prod."},{"issue":"PA","key":"10.1016\/j.asoc.2025.113526_bib42","article-title":"Experimental road safety study of the actual driver reaction to the street ads using eye tracking, multiple linear regression and decision trees methods","volume":"252","author":"AlKheder","year":"2024","journal-title":"Expert Syst. Appl."},{"issue":"July 2021","key":"10.1016\/j.asoc.2025.113526_bib43","article-title":"State of health estimation of lithium-ion battery based on improved ant lion optimization and support vector regression","volume":"50","author":"Li","year":"2022","journal-title":"J. Energy Storage"},{"key":"10.1016\/j.asoc.2025.113526_bib44","article-title":"State-of-health estimation for lithium-ion batteries by combining model-based incremental capacity analysis with support vector regression","volume":"239","author":"Zhang","year":"2022","journal-title":"Energy"},{"issue":"May","key":"10.1016\/j.asoc.2025.113526_bib45","article-title":"An enhanced uniform simulation approach coupled with SVR for efficient structural reliability analysis","volume":"237","author":"Luo","year":"2023","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"10.1016\/j.asoc.2025.113526_bib46","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2022.109690","article-title":"Unified whale optimization algorithm based multi-kernel SVR ensemble learning for wind speed forecasting","volume":"130","author":"Xian","year":"2022","journal-title":"Appl. Soft Comput."},{"issue":"October","key":"10.1016\/j.asoc.2025.113526_bib47","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1016\/j.apenergy.2018.09.182","article-title":"Random forest regression for online capacity estimation of lithium-ion batteries","volume":"232","author":"Li","year":"2018","journal-title":"Appl. Energy"},{"issue":"Mar","key":"10.1016\/j.asoc.2025.113526_bib48","doi-asserted-by":"crossref","first-page":"1173","DOI":"10.1016\/j.energy.2019.01.083","article-title":"A coulombic efficiency-based model for prognostics and health estimation of lithium-ion batteries","volume":"171","author":"Yang","year":"2019","journal-title":"Energy"},{"issue":"6","key":"10.1016\/j.asoc.2025.113526_bib49","doi-asserted-by":"crossref","DOI":"10.3390\/batteries9060332","article-title":"An optimized random forest regression model for li-ion battery prognostics and health management","volume":"9","author":"Wang","year":"2023","journal-title":"Batteries"},{"key":"10.1016\/j.asoc.2025.113526_bib50","doi-asserted-by":"crossref","DOI":"10.1016\/j.energy.2022.123973","article-title":"A novel method for state of health estimation of lithium-ion batteries based on improved LSTM and health indicators extraction","volume":"251","author":"Ma","year":"2022","journal-title":"Energy"},{"issue":"April","key":"10.1016\/j.asoc.2025.113526_bib51","article-title":"A comparative study of battery state-of-health estimation based on empirical mode decomposition and neural network","volume":"54","author":"Li","year":"2022","journal-title":"J. Energy Storage"},{"issue":"February","key":"10.1016\/j.asoc.2025.113526_bib52","article-title":"Early quality classification and prediction of battery cycle life in production using machine learning","volume":"50","author":"Stock","year":"2022","journal-title":"J. Energy Storage"},{"issue":"10","key":"10.1016\/j.asoc.2025.113526_bib53","doi-asserted-by":"crossref","DOI":"10.3390\/batteries8100192","article-title":"Development of a data-driven method for online battery remaining-useful-life prediction","volume":"8","author":"Hell","year":"2022","journal-title":"Batteries"}],"container-title":["Applied Soft Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1568494625008373?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1568494625008373?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,9,3]],"date-time":"2025-09-03T22:07:02Z","timestamp":1756937222000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1568494625008373"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9]]},"references-count":53,"alternative-id":["S1568494625008373"],"URL":"https:\/\/doi.org\/10.1016\/j.asoc.2025.113526","relation":{},"ISSN":["1568-4946"],"issn-type":[{"value":"1568-4946","type":"print"}],"subject":[],"published":{"date-parts":[[2025,9]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Random forest-based small training dataset complementation and features selection for capacity estimation of lithium-ion batteries in electric-powered application","name":"articletitle","label":"Article Title"},{"value":"Applied Soft Computing","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.asoc.2025.113526","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2025 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"113526"}}