{"id":"https://openalex.org/W7123540608","doi":"https://doi.org/10.1109/tii.2025.3645950","title":"Auxiliary-Label Enhanced Semi Supervised Learning With Selective Pseudolabeling for Battery Capacity Estimation","display_name":"Auxiliary-Label Enhanced Semi Supervised Learning With Selective Pseudolabeling for Battery Capacity Estimation","publication_year":2026,"publication_date":"2026-01-13","ids":{"openalex":"https://openalex.org/W7123540608","doi":"https://doi.org/10.1109/tii.2025.3645950"},"language":"en","primary_location":{"id":"doi:10.1109/tii.2025.3645950","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tii.2025.3645950","pdf_url":null,"source":{"id":"https://openalex.org/S184777250","display_name":"IEEE Transactions on Industrial Informatics","issn_l":"1551-3203","issn":["1551-3203","1941-0050"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Industrial Informatics","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5036997043","display_name":"Yihuan Li","orcid":"https://orcid.org/0000-0002-5521-9224"},"institutions":[{"id":"https://openalex.org/I153473198","display_name":"North China Electric Power University","ror":"https://ror.org/04qr5t414","country_code":"CN","type":"education","lineage":["https://openalex.org/I153473198"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yihuan Li","raw_affiliation_strings":["School of Control and Computer Engineering, North China Electric Power University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-5521-9224","affiliations":[{"raw_affiliation_string":"School of Control and Computer Engineering, North China Electric Power University, Beijing, China","institution_ids":["https://openalex.org/I153473198"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5108619764","display_name":"Kaituo Liu","orcid":null},"institutions":[{"id":"https://openalex.org/I153473198","display_name":"North China Electric Power University","ror":"https://ror.org/04qr5t414","country_code":"CN","type":"education","lineage":["https://openalex.org/I153473198"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kaituo Liu","raw_affiliation_strings":["School of Control and Computer Engineering, North China Electric Power University, Beijing, China"],"raw_orcid":"https://orcid.org/0009-0006-9680-7504","affiliations":[{"raw_affiliation_string":"School of Control and Computer Engineering, North China Electric Power University, Beijing, China","institution_ids":["https://openalex.org/I153473198"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5122983136","display_name":"Wei Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I153473198","display_name":"North China Electric Power University","ror":"https://ror.org/04qr5t414","country_code":"CN","type":"education","lineage":["https://openalex.org/I153473198"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wei Wang","raw_affiliation_strings":["School of Control and Computer Engineering, North China Electric Power University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0003-3952-2861","affiliations":[{"raw_affiliation_string":"School of Control and Computer Engineering, North China Electric Power University, Beijing, China","institution_ids":["https://openalex.org/I153473198"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5122960560","display_name":"Fang Fang","orcid":null},"institutions":[{"id":"https://openalex.org/I153473198","display_name":"North China Electric Power University","ror":"https://ror.org/04qr5t414","country_code":"CN","type":"education","lineage":["https://openalex.org/I153473198"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fang Fang","raw_affiliation_strings":["School of Control and Computer Engineering, North China Electric Power University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0003-3784-3696","affiliations":[{"raw_affiliation_string":"School of Control and Computer Engineering, North China Electric Power University, Beijing, China","institution_ids":["https://openalex.org/I153473198"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5122959907","display_name":"Kang Li","orcid":null},"institutions":[{"id":"https://openalex.org/I130828816","display_name":"University of Leeds","ror":"https://ror.org/024mrxd33","country_code":"GB","type":"education","lineage":["https://openalex.org/I130828816"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Kang Li","raw_affiliation_strings":["School of Electronic and Electrical Engineering, University of Leeds, Leeds, U.K"],"raw_orcid":"https://orcid.org/0000-0001-6657-0522","affiliations":[{"raw_affiliation_string":"School of Electronic and Electrical Engineering, University of Leeds, Leeds, U.K","institution_ids":["https://openalex.org/I130828816"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":4.1649,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.87710724,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":"22","issue":"4","first_page":"2805","last_page":"2816"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10663","display_name":"Advanced Battery Technologies Research","score":0.9442999958992004,"subfield":{"id":"https://openalex.org/subfields/2203","display_name":"Automotive Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10663","display_name":"Advanced Battery Technologies Research","score":0.9442999958992004,"subfield":{"id":"https://openalex.org/subfields/2203","display_name":"Automotive Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12238","display_name":"Green IT and Sustainability","score":0.015799999237060547,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10281","display_name":"Advanced Battery Materials and Technologies","score":0.0066999997943639755,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/transfer-of-learning","display_name":"Transfer of learning","score":0.6258000135421753},{"id":"https://openalex.org/keywords/semi-supervised-learning","display_name":"Semi-supervised learning","score":0.6179999709129333},{"id":"https://openalex.org/keywords/labeled-data","display_name":"Labeled data","score":0.5792999863624573},{"id":"https://openalex.org/keywords/supervised-learning","display_name":"Supervised learning","score":0.5400999784469604},{"id":"https://openalex.org/keywords/extractor","display_name":"Extractor","score":0.5044000148773193},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.49559998512268066},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4900999963283539},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.4505000114440918},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.44940000772476196},{"id":"https://openalex.org/keywords/reduction","display_name":"Reduction (mathematics)","score":0.4447000026702881}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7397000193595886},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.667900025844574},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6366999745368958},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.6258000135421753},{"id":"https://openalex.org/C58973888","wikidata":"https://www.wikidata.org/wiki/Q1041418","display_name":"Semi-supervised learning","level":2,"score":0.6179999709129333},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.5792999863624573},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.5400999784469604},{"id":"https://openalex.org/C117978034","wikidata":"https://www.wikidata.org/wiki/Q5422192","display_name":"Extractor","level":2,"score":0.5044000148773193},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.49559998512268066},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4900999963283539},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.4505000114440918},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.44940000772476196},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.4447000026702881},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.38769999146461487},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.38679999113082886},{"id":"https://openalex.org/C2776959682","wikidata":"https://www.wikidata.org/wiki/Q17005296","display_name":"Co-training","level":3,"score":0.3828999996185303},{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.3714999854564667},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.3630000054836273},{"id":"https://openalex.org/C28006648","wikidata":"https://www.wikidata.org/wiki/Q6934509","display_name":"Multi-task learning","level":3,"score":0.3610999882221222},{"id":"https://openalex.org/C2989104859","wikidata":"https://www.wikidata.org/wiki/Q267298","display_name":"Battery capacity","level":4,"score":0.36059999465942383},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.35519999265670776},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.3458999991416931},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.33629998564720154},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.3294000029563904},{"id":"https://openalex.org/C555008776","wikidata":"https://www.wikidata.org/wiki/Q267298","display_name":"Battery (electricity)","level":3,"score":0.3151000142097473},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.3109999895095825},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.30379998683929443},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.2973000109195709},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.2808000147342682},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2750000059604645},{"id":"https://openalex.org/C116409475","wikidata":"https://www.wikidata.org/wiki/Q1385056","display_name":"External Data Representation","level":2,"score":0.2623000144958496},{"id":"https://openalex.org/C77967617","wikidata":"https://www.wikidata.org/wiki/Q4677561","display_name":"Active learning (machine learning)","level":2,"score":0.2615000009536743},{"id":"https://openalex.org/C196083921","wikidata":"https://www.wikidata.org/wiki/Q7915758","display_name":"Variance (accounting)","level":2,"score":0.2549999952316284}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tii.2025.3645950","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tii.2025.3645950","pdf_url":null,"source":{"id":"https://openalex.org/S184777250","display_name":"IEEE Transactions on Industrial Informatics","issn_l":"1551-3203","issn":["1551-3203","1941-0050"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Industrial Informatics","raw_type":"journal-article"},{"id":"pmh:oai:eprints.whiterose.ac.uk:236908","is_oa":false,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4306400854","display_name":"White Rose Research Online (University of Leeds, The University of Sheffield, University of York)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I2800616092","host_organization_name":"White Rose University Consortium","host_organization_lineage":["https://openalex.org/I2800616092"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":null,"raw_type":"Article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G228503519","display_name":null,"funder_award_id":"52307242","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G2898866592","display_name":null,"funder_award_id":"2025JC003","funder_id":"https://openalex.org/F4320335787","funder_display_name":"Fundamental Research Funds for the Central Universities"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320335787","display_name":"Fundamental Research Funds for the Central Universities","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Recent":[0],"advances":[1],"in":[2],"data-driven":[3],"methods":[4],"have":[5],"significantly":[6],"improved":[7],"battery":[8],"capacity":[9,98,151],"estimation,":[10],"yet":[11],"most":[12],"existing":[13],"approaches":[14],"remain":[15],"constrained":[16],"by":[17],"their":[18,117],"reliance":[19],"on":[20],"supervised":[21,47,185],"learning,":[22],"requiring":[23],"substantial":[24],"amounts":[25],"of":[26,167],"labeled":[27,67,112,174],"cycling":[28],"data":[29,93],"that":[30,49,90],"are":[31],"often":[32],"costly":[33],"to":[34,128],"obtain.":[35],"To":[36],"address":[37],"this":[38,40],"challenge,":[39],"study":[41],"proposes":[42],"a":[43,86,122,163,177],"dual-branch":[44,81],"network-based":[45],"semi":[46,184],"framework":[48,59],"integrates":[50],"self-supervised":[51,73],"learning":[52,55],"and":[53,68,101,113,148],"transfer":[54,110],"mechanisms.":[56],"First,":[57],"the":[58,79,96,102,134,142,150,158,182,189],"derives":[60],"meaningful":[61],"degradation-aware":[62],"auxiliary":[63,103],"labels":[64],"from":[65,133],"both":[66,95],"unlabeled":[69,114,135],"samples,":[70],"creating":[71],"reliable":[72],"signal":[74],"for":[75,94,137],"model":[76],"training.":[77],"Second,":[78],"designed":[80],"neural":[82],"network":[83],"architecture":[84],"employs":[85],"shared":[87],"feature":[88],"extractor":[89],"processes":[91],"input":[92],"primary":[97],"estimation":[99,152],"task":[100],"label":[104],"prediction":[105],"task,":[106],"enabling":[107],"effective":[108],"knowledge":[109],"between":[111],"domains":[115],"through":[116],"common":[118],"representation":[119],"space.":[120],"Third,":[121],"pseudolabel":[123],"filtering":[124],"strategy":[125],"is":[126],"proposed":[127],"dynamically":[129],"select":[130],"high-confidence":[131],"samples":[132],"dataset":[136],"self-training,":[138],"thereby":[139],"effectively":[140],"expanding":[141],"training":[143],"set":[144],"with":[145,171,181],"high-quality":[146],"pseudolabels":[147],"enhancing":[149],"accuracy.":[153],"Finally,":[154],"extensive":[155],"experiments":[156],"validate":[157],"framework\u2019s":[159],"superior":[160],"performance,":[161],"achieving":[162],"worst-case":[164],"root-mean-square":[165],"error":[166],"only":[168],"0.0143":[169],"Ah":[170],"merely":[172],"5%":[173],"data,":[175],"representing":[176],"27.04%":[178],"reduction":[179],"compared":[180],"best-performing":[183],"baseline":[186],"(Co-training)":[187],"under":[188],"same":[190],"conditions.":[191]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-07-15T18:14:33.161393","created_date":"2026-01-14T00:00:00"}
