{"id":"https://openalex.org/W4214886143","doi":"https://doi.org/10.1109/tii.2022.3155597","title":"A Pattern-Driven Stochastic Degradation Model for the Prediction of Remaining Useful Life of Rechargeable Batteries","display_name":"A Pattern-Driven Stochastic Degradation Model for the Prediction of Remaining Useful Life of Rechargeable Batteries","publication_year":2022,"publication_date":"2022-03-03","ids":{"openalex":"https://openalex.org/W4214886143","doi":"https://doi.org/10.1109/tii.2022.3155597"},"language":"en","primary_location":{"id":"doi:10.1109/tii.2022.3155597","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tii.2022.3155597","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/A5100410548","display_name":"Zihan Zhang","orcid":"https://orcid.org/0000-0002-5882-055X"},"institutions":[{"id":"https://openalex.org/I130701444","display_name":"Georgia Institute of Technology","ror":"https://ror.org/01zkghx44","country_code":"US","type":"education","lineage":["https://openalex.org/I130701444"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zihan Zhang","raw_affiliation_strings":["H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, USA"],"raw_orcid":"https://orcid.org/0000-0002-5882-055X","affiliations":[{"raw_affiliation_string":"H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, USA","institution_ids":["https://openalex.org/I130701444"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5029234470","display_name":"Yeonjeong Jeong","orcid":"https://orcid.org/0000-0002-6255-8179"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yeonjeong Jeong","raw_affiliation_strings":["LG AI Research, Toronto, ON, Canada"],"raw_orcid":"https://orcid.org/0000-0002-6255-8179","affiliations":[{"raw_affiliation_string":"LG AI Research, Toronto, ON, Canada","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101431618","display_name":"Jongseong Jang","orcid":"https://orcid.org/0000-0001-6556-0365"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jongseong Jang","raw_affiliation_strings":["LG AI Research, Toronto, ON, Canada"],"raw_orcid":"https://orcid.org/0000-0001-6556-0365","affiliations":[{"raw_affiliation_string":"LG AI Research, Toronto, ON, Canada","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5057636084","display_name":"Chi-Guhn Lee","orcid":"https://orcid.org/0000-0002-0916-0241"},"institutions":[{"id":"https://openalex.org/I185261750","display_name":"University of Toronto","ror":"https://ror.org/03dbr7087","country_code":"CA","type":"education","lineage":["https://openalex.org/I185261750"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Chi-Guhn Lee","raw_affiliation_strings":["Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, ON, Canada"],"raw_orcid":"https://orcid.org/0000-0002-0916-0241","affiliations":[{"raw_affiliation_string":"Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, ON, Canada","institution_ids":["https://openalex.org/I185261750"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.744,"has_fulltext":false,"cited_by_count":38,"citation_normalized_percentile":{"value":0.90067278,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":100},"biblio":{"volume":"18","issue":"12","first_page":"8586","last_page":"8594"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10663","display_name":"Advanced Battery Technologies Research","score":1.0,"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":1.0,"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/T10780","display_name":"Reliability and Maintenance Optimization","score":0.9987000226974487,"subfield":{"id":"https://openalex.org/subfields/2213","display_name":"Safety, Risk, Reliability and Quality"},"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/T10768","display_name":"Electric Vehicles and Infrastructure","score":0.9962000250816345,"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/battery","display_name":"Battery (electricity)","score":0.6782364249229431},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5331406593322754},{"id":"https://openalex.org/keywords/degradation","display_name":"Degradation (telecommunications)","score":0.5286686420440674},{"id":"https://openalex.org/keywords/stochastic-process","display_name":"Stochastic process","score":0.5087404847145081},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.49738314747810364},{"id":"https://openalex.org/keywords/overfitting","display_name":"Overfitting","score":0.4646432399749756},{"id":"https://openalex.org/keywords/state-of-health","display_name":"State of health","score":0.46123623847961426},{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.451900452375412},{"id":"https://openalex.org/keywords/wiener-process","display_name":"Wiener process","score":0.44741690158843994},{"id":"https://openalex.org/keywords/state-of-charge","display_name":"State of charge","score":0.4223124384880066},{"id":"https://openalex.org/keywords/markov-process","display_name":"Markov process","score":0.4181450605392456},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.3607896566390991},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3391219675540924},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.3266623318195343},{"id":"https://openalex.org/keywords/reliability-engineering","display_name":"Reliability engineering","score":0.32442811131477356},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.3073340654373169},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.19154983758926392},{"id":"https://openalex.org/keywords/power","display_name":"Power (physics)","score":0.14219871163368225},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.14044272899627686},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.11313176155090332}],"concepts":[{"id":"https://openalex.org/C555008776","wikidata":"https://www.wikidata.org/wiki/Q267298","display_name":"Battery (electricity)","level":3,"score":0.6782364249229431},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5331406593322754},{"id":"https://openalex.org/C2779679103","wikidata":"https://www.wikidata.org/wiki/Q5251805","display_name":"Degradation (telecommunications)","level":2,"score":0.5286686420440674},{"id":"https://openalex.org/C8272713","wikidata":"https://www.wikidata.org/wiki/Q176737","display_name":"Stochastic process","level":2,"score":0.5087404847145081},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.49738314747810364},{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.4646432399749756},{"id":"https://openalex.org/C2777294910","wikidata":"https://www.wikidata.org/wiki/Q4050070","display_name":"State of health","level":4,"score":0.46123623847961426},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.451900452375412},{"id":"https://openalex.org/C60391097","wikidata":"https://www.wikidata.org/wiki/Q1056809","display_name":"Wiener process","level":2,"score":0.44741690158843994},{"id":"https://openalex.org/C2776582896","wikidata":"https://www.wikidata.org/wiki/Q5368536","display_name":"State of charge","level":4,"score":0.4223124384880066},{"id":"https://openalex.org/C159886148","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov process","level":2,"score":0.4181450605392456},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.3607896566390991},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3391219675540924},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.3266623318195343},{"id":"https://openalex.org/C200601418","wikidata":"https://www.wikidata.org/wiki/Q2193887","display_name":"Reliability engineering","level":1,"score":0.32442811131477356},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.3073340654373169},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.19154983758926392},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.14219871163368225},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.14044272899627686},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.11313176155090332},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tii.2022.3155597","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tii.2022.3155597","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"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320334593","display_name":"Natural Sciences and Engineering Research Council of Canada","ror":"https://ror.org/01h531d29"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":34,"referenced_works":["https://openalex.org/W1501586228","https://openalex.org/W1628954589","https://openalex.org/W2007536021","https://openalex.org/W2007655149","https://openalex.org/W2047152377","https://openalex.org/W2061659083","https://openalex.org/W2078279667","https://openalex.org/W2109539992","https://openalex.org/W2144352195","https://openalex.org/W2171033594","https://openalex.org/W2333401465","https://openalex.org/W2498094064","https://openalex.org/W2547400235","https://openalex.org/W2584089850","https://openalex.org/W2599868532","https://openalex.org/W2770203925","https://openalex.org/W2775002212","https://openalex.org/W2790625295","https://openalex.org/W2791494814","https://openalex.org/W2792573670","https://openalex.org/W2895466770","https://openalex.org/W2907460853","https://openalex.org/W2978202285","https://openalex.org/W2985392355","https://openalex.org/W3012222963","https://openalex.org/W3013372615","https://openalex.org/W3015574081","https://openalex.org/W3047949753","https://openalex.org/W3095729178","https://openalex.org/W3106691138","https://openalex.org/W3122696643","https://openalex.org/W3199081584","https://openalex.org/W6746630573","https://openalex.org/W6788918770"],"related_works":["https://openalex.org/W2785562078","https://openalex.org/W4376058722","https://openalex.org/W2548126483","https://openalex.org/W4376116346","https://openalex.org/W4226209220","https://openalex.org/W2187069005","https://openalex.org/W2096340301","https://openalex.org/W4313473424","https://openalex.org/W2990165279","https://openalex.org/W63511858"],"abstract_inverted_index":{"Recently,":[0],"there":[1],"has":[2],"been":[3],"a":[4,34,57,100,115,121,130,151],"significant":[5],"growth":[6],"in":[7,88,110,186],"the":[8,44,53,65,80,143,147,176,190],"development":[9],"of":[10,38,47],"rechargeable":[11],"battery-powered":[12],"devices":[13],"such":[14],"as":[15,43,99],"electric":[16],"vehicles,":[17],"leading":[18,93],"to":[19,94,138,146,159],"an":[20],"urgent":[21],"need":[22],"for":[23],"reliable":[24],"and":[25,49,182,192],"safe":[26],"batteries.":[27],"The":[28],"remaining":[29,45],"useful":[30],"life":[31],"(RUL)":[32],"is":[33,41],"critical":[35],"health":[36],"indicator":[37],"battery,":[39],"which":[40],"defined":[42],"number":[46],"charge":[48],"recharge":[50],"cycles":[51],"before":[52],"state-of-health":[54],"falls":[55],"below":[56],"user-specified":[58],"threshold":[59],"under":[60],"certain":[61],"operating":[62],"settings.":[63],"Substantially,":[64],"RUL":[66,187],"can":[67],"be":[68],"estimated":[69],"by":[70,119],"adaptive":[71,193],"stochastic":[72],"processes":[73],"or":[74,97],"advanced":[75],"machine":[76],"learning":[77],"techniques.":[78],"However,":[79],"existing":[81],"approaches":[82],"either":[83],"assume":[84],"over-simplified":[85],"degradation":[86,117,136,140],"pattern":[87,137],"accordance":[89],"with":[90,125],"physics":[91],"laws":[92],"poor":[95],"generalizability":[96],"act":[98],"black":[101],"box":[102],"offering":[103],"no":[104],"interpretation.":[105],"To":[106],"address":[107],"these":[108],"limitations,":[109],"this":[111],"article,":[112],"we":[113],"develop":[114],"pattern-driven":[116],"process":[118],"integrating":[120],"recursive":[122],"Gaussian":[123],"distribution":[124],"its":[126],"mean":[127],"learnt":[128],"from":[129],"gated":[131],"recurrent":[132],"unit":[133],"(GRU)":[134],"driven":[135],"capture":[139],"fluctuation":[141],"into":[142],"model.":[144],"Due":[145],"non-Markovian":[148],"state":[149],"transitions,":[150],"joint-learning":[152],"sampling-based":[153],"expectation":[154],"maximization":[155],"algorithm":[156],"was":[157],"developed":[158],"estimate":[160],"model":[161],"parameters":[162],"based":[163],"on":[164],"historical":[165],"observations.":[166],"Finally,":[167],"numerical":[168],"studies":[169],"using":[170],"real":[171],"battery":[172],"data":[173],"showed":[174],"that":[175],"proposed":[177],"method":[178],"achieves":[179],"over":[180],"3%":[181],"40%":[183],"higher":[184],"accuracy":[185],"prediction":[188],"than":[189],"GRU":[191],"Wiener":[194],"process,":[195],"respectively.":[196]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":17},{"year":2024,"cited_by_count":10},{"year":2023,"cited_by_count":6},{"year":2022,"cited_by_count":2}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}