{"id":"https://openalex.org/W7147729811","doi":"https://doi.org/10.1109/icaiic68212.2026.11454214","title":"Bi-LSTM Based LIB RUL Prediction","display_name":"Bi-LSTM Based LIB RUL Prediction","publication_year":2026,"publication_date":"2026-02-24","ids":{"openalex":"https://openalex.org/W7147729811","doi":"https://doi.org/10.1109/icaiic68212.2026.11454214"},"language":null,"primary_location":{"id":"doi:10.1109/icaiic68212.2026.11454214","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icaiic68212.2026.11454214","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2026 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","raw_type":"proceedings-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/A5071170519","display_name":"Haejun Kim","orcid":null},"institutions":[{"id":"https://openalex.org/I163753206","display_name":"Chungbuk National University","ror":"https://ror.org/02wnxgj78","country_code":"KR","type":"education","lineage":["https://openalex.org/I163753206"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Haejun Kim","raw_affiliation_strings":["Chungbuk National University,Dept. of Mechanical Engineering,Cheongju,Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Chungbuk National University,Dept. of Mechanical Engineering,Cheongju,Korea","institution_ids":["https://openalex.org/I163753206"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132648356","display_name":"Kibum Cheon","orcid":null},"institutions":[{"id":"https://openalex.org/I163753206","display_name":"Chungbuk National University","ror":"https://ror.org/02wnxgj78","country_code":"KR","type":"education","lineage":["https://openalex.org/I163753206"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Kibum Cheon","raw_affiliation_strings":["Chungbuk National University,Dept. of Mechanical Engineering,Cheongju,Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Chungbuk National University,Dept. of Mechanical Engineering,Cheongju,Korea","institution_ids":["https://openalex.org/I163753206"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5029437984","display_name":"Jongho Shin","orcid":"https://orcid.org/0000-0001-9817-3556"},"institutions":[{"id":"https://openalex.org/I163753206","display_name":"Chungbuk National University","ror":"https://ror.org/02wnxgj78","country_code":"KR","type":"education","lineage":["https://openalex.org/I163753206"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Jongho Shin","raw_affiliation_strings":["Chungbuk National University,Dept. of Mechanical Engineering,Cheongju,Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Chungbuk National University,Dept. of Mechanical Engineering,Cheongju,Korea","institution_ids":["https://openalex.org/I163753206"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.44269663,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1353","last_page":"1358"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10663","display_name":"Advanced Battery Technologies Research","score":0.9922999739646912,"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":0.9922999739646912,"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/T10281","display_name":"Advanced Battery Materials and Technologies","score":0.0027000000700354576,"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/T10780","display_name":"Reliability and Maintenance Optimization","score":0.0006000000284984708,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/reliability","display_name":"Reliability (semiconductor)","score":0.5976999998092651},{"id":"https://openalex.org/keywords/sequence","display_name":"Sequence (biology)","score":0.5841000080108643},{"id":"https://openalex.org/keywords/battery","display_name":"Battery (electricity)","score":0.5626000165939331},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5540000200271606},{"id":"https://openalex.org/keywords/degradation","display_name":"Degradation (telecommunications)","score":0.5047000050544739},{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.492000013589859},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.4869000017642975},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.46779999136924744},{"id":"https://openalex.org/keywords/statistical-model","display_name":"Statistical model","score":0.4099000096321106},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.40220001339912415}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6291000247001648},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.5976999998092651},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.5841000080108643},{"id":"https://openalex.org/C555008776","wikidata":"https://www.wikidata.org/wiki/Q267298","display_name":"Battery (electricity)","level":3,"score":0.5626000165939331},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5540000200271606},{"id":"https://openalex.org/C2779679103","wikidata":"https://www.wikidata.org/wiki/Q5251805","display_name":"Degradation (telecommunications)","level":2,"score":0.5047000050544739},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.492000013589859},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.4869000017642975},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.46779999136924744},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.42289999127388},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.4099000096321106},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.40220001339912415},{"id":"https://openalex.org/C2982736386","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Statistical learning","level":2,"score":0.39739999175071716},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.3644999861717224},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.36399999260902405},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3499999940395355},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3492000102996826},{"id":"https://openalex.org/C167085575","wikidata":"https://www.wikidata.org/wiki/Q6803654","display_name":"Mean squared prediction error","level":2,"score":0.3465999960899353},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.34299999475479126},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.33719998598098755},{"id":"https://openalex.org/C48103436","wikidata":"https://www.wikidata.org/wiki/Q599031","display_name":"State (computer science)","level":2,"score":0.30379998683929443},{"id":"https://openalex.org/C200601418","wikidata":"https://www.wikidata.org/wiki/Q2193887","display_name":"Reliability engineering","level":1,"score":0.2973000109195709},{"id":"https://openalex.org/C2986587452","wikidata":"https://www.wikidata.org/wiki/Q938438","display_name":"Statistical analysis","level":2,"score":0.29120001196861267},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.2872999906539917},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.28529998660087585},{"id":"https://openalex.org/C40506919","wikidata":"https://www.wikidata.org/wiki/Q7452469","display_name":"Sequence learning","level":2,"score":0.27079999446868896},{"id":"https://openalex.org/C2777294910","wikidata":"https://www.wikidata.org/wiki/Q4050070","display_name":"State of health","level":4,"score":0.2669000029563904},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.2637999951839447},{"id":"https://openalex.org/C40969351","wikidata":"https://www.wikidata.org/wiki/Q3516228","display_name":"Word error rate","level":2,"score":0.2558000087738037},{"id":"https://openalex.org/C129364497","wikidata":"https://www.wikidata.org/wiki/Q3042561","display_name":"Prognostics","level":2,"score":0.2551000118255615},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.2515000104904175},{"id":"https://openalex.org/C133488467","wikidata":"https://www.wikidata.org/wiki/Q6673524","display_name":"Long short term memory","level":4,"score":0.25110000371932983}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icaiic68212.2026.11454214","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icaiic68212.2026.11454214","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2026 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320322120","display_name":"National Research Foundation of Korea","ror":"https://ror.org/013aysd81"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W1628954589","https://openalex.org/W1949231392","https://openalex.org/W2064675550","https://openalex.org/W2079735306","https://openalex.org/W2089525884","https://openalex.org/W2113449668","https://openalex.org/W2131774270","https://openalex.org/W2169518199","https://openalex.org/W2201263372","https://openalex.org/W2563343938","https://openalex.org/W2612210564","https://openalex.org/W2790625295","https://openalex.org/W2924382816","https://openalex.org/W2964121744","https://openalex.org/W2966169983","https://openalex.org/W3191549510","https://openalex.org/W4283723254","https://openalex.org/W4307055381","https://openalex.org/W4313598793","https://openalex.org/W4385834332","https://openalex.org/W4387141402","https://openalex.org/W4389305267","https://openalex.org/W4400980345"],"related_works":[],"abstract_inverted_index":{"Accurate":[0],"Remaining":[1],"Useful":[2],"Life":[3],"(RUL)":[4],"prediction":[5,45,53,186,211],"for":[6,12,168],"Lithium-Ion":[7],"Battery":[8,20],"(LIB)":[9],"is":[10,99,248],"essential":[11],"ensuring":[13],"the":[14,65,118,137,145,161,169,175,185,195,206,227,233,244,269],"reliability":[15],"and":[16,33,67,87,107,147,166,178,183,190,198,219,240],"safety":[17],"of":[18,71,92,188,243,261,271],"a":[19,39,52,57,78,124],"Management":[21],"System":[22],"(BMS).":[23],"The":[24,74,156,201,259],"non-linear":[25,66],"degradation":[26,69,105,254],"patterns,":[27],"caused":[28],"by":[29,115],"diverse":[30],"operating":[31],"conditions":[32],"complex":[34,68],"internal":[35],"chemical":[36],"reactions,":[37],"are":[38,164,264],"key":[40],"factor":[41],"that":[42,81,152,205,232],"makes":[43],"accurate":[44],"difficult.":[46],"In":[47],"this":[48,262],"study,":[49],"we":[50,181],"propose":[51],"model":[54,76,208],"based":[55],"on":[56],"Recurrent":[58],"Neural":[59],"Network":[60],"(RNN)":[61],"to":[62,101,226,266,268],"effectively":[63],"learn":[64],"characteristics":[70],"these":[72],"batteries.":[73],"proposed":[75],"features":[77,89],"dual-branch":[79],"architecture":[80,98],"simultaneously":[82],"utilizes":[83,237],"both":[84,238],"temporal":[85,113],"patterns":[86,255],"statistical":[88,141],"from":[90,151,160],"State":[91],"Health":[93],"(SOH)":[94],"sequence":[95,120,153],"data.":[96],"This":[97,230],"designed":[100],"concurrently":[102],"capture":[103],"long-term":[104],"trends":[106],"local":[108],"fluctuations.":[109],"One":[110],"branch":[111,139],"learns":[112],"dependencies":[114],"directly":[116],"taking":[117],"SOH":[119,245],"as":[121,144,154],"input":[122],"through":[123],"Long":[125,131],"Short-Term":[126,132],"Memory":[127,133],"(LSTM)":[128],"or":[129],"Bidirectional":[130],"(Bi-LSTM)":[134],"network,":[135],"while":[136],"other":[138],"receives":[140],"features,":[142],"such":[143],"mean":[146],"standard":[148],"deviation,":[149],"extracted":[150,159],"input.":[155],"feature":[157],"vectors":[158],"two":[162],"branches":[163],"combined":[165],"used":[167],"final":[170],"RUL":[171],"prediction.":[172],"To":[173],"validate":[174],"model's":[176],"performance":[177,187],"structural":[179],"effectiveness,":[180],"compared":[182,225],"analyzed":[184],"LSTM":[189,228],"Bi-LSTM":[191,207],"models":[192],"applied":[193],"under":[194],"same":[196],"dataset":[197],"dualbranch":[199],"architecture.":[200],"experimental":[202],"results":[203],"indicated":[204],"demonstrated":[209],"superior":[210],"accuracy,":[212],"achieving":[213],"lower":[214],"Mean":[215,221],"Absolute":[216],"Error":[217,223],"(MAE)":[218],"Root":[220],"Square":[222],"(RMSE)":[224],"model.":[229],"suggests":[231],"bidirectional":[234],"architecture,":[235],"which":[236],"past":[239],"future":[241],"contexts":[242],"time-series":[246],"data,":[247],"more":[249,272],"effective":[250],"in":[251],"learning":[252],"battery":[253,274],"with":[256],"greater":[257],"precision.":[258],"findings":[260],"study":[263],"expected":[265],"contribute":[267],"development":[270],"reliable":[273],"management":[275],"strategies.":[276]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-04-02T00:00:00"}