{"id":"https://openalex.org/W4379983498","doi":"https://doi.org/10.1109/icit58465.2023.10143124","title":"Li-ion Battery Health State Estimation with Two Feed Forward Neural Networks","display_name":"Li-ion Battery Health State Estimation with Two Feed Forward Neural Networks","publication_year":2023,"publication_date":"2023-04-04","ids":{"openalex":"https://openalex.org/W4379983498","doi":"https://doi.org/10.1109/icit58465.2023.10143124"},"language":"en","primary_location":{"id":"doi:10.1109/icit58465.2023.10143124","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icit58465.2023.10143124","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE International Conference on Industrial Technology (ICIT)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5100446508","display_name":"Jung-Hwan Lee","orcid":"https://orcid.org/0000-0002-4100-2315"},"institutions":[{"id":"https://openalex.org/I4210127843","display_name":"First Automotive Works (China)","ror":"https://ror.org/0353t4m91","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210127843"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Junghwan Lee","raw_affiliation_strings":["R&D Institute, FAW Group,Changchun,Jilin,China,130013"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"R&D Institute, FAW Group,Changchun,Jilin,China,130013","institution_ids":["https://openalex.org/I4210127843"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5100446508"],"corresponding_institution_ids":["https://openalex.org/I4210127843"],"apc_list":null,"apc_paid":null,"fwci":1.7419,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.85114937,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10663","display_name":"Advanced Battery Technologies Research","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9545000195503235,"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/T10876","display_name":"Fault Detection and Control Systems","score":0.9452000260353088,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems 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/hyperparameter","display_name":"Hyperparameter","score":0.8845200538635254},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8053563833236694},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.7253515720367432},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.687138557434082},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.6156652569770813},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5250251889228821},{"id":"https://openalex.org/keywords/battery","display_name":"Battery (electricity)","score":0.508841335773468},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.505805253982544},{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.4296965003013611},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4209819436073303},{"id":"https://openalex.org/keywords/state-of-health","display_name":"State of health","score":0.4138116240501404},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.35513800382614136},{"id":"https://openalex.org/keywords/power","display_name":"Power (physics)","score":0.10502055287361145},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.08873721957206726}],"concepts":[{"id":"https://openalex.org/C8642999","wikidata":"https://www.wikidata.org/wiki/Q4171168","display_name":"Hyperparameter","level":2,"score":0.8845200538635254},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8053563833236694},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.7253515720367432},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.687138557434082},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.6156652569770813},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5250251889228821},{"id":"https://openalex.org/C555008776","wikidata":"https://www.wikidata.org/wiki/Q267298","display_name":"Battery (electricity)","level":3,"score":0.508841335773468},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.505805253982544},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.4296965003013611},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4209819436073303},{"id":"https://openalex.org/C2777294910","wikidata":"https://www.wikidata.org/wiki/Q4050070","display_name":"State of health","level":4,"score":0.4138116240501404},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.35513800382614136},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.10502055287361145},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.08873721957206726},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icit58465.2023.10143124","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icit58465.2023.10143124","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE International Conference on Industrial Technology (ICIT)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.44999998807907104,"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W1836465849","https://openalex.org/W2070188654","https://openalex.org/W2523246573","https://openalex.org/W2626017178","https://openalex.org/W2785523195","https://openalex.org/W2969495951","https://openalex.org/W3016108562","https://openalex.org/W3045004532","https://openalex.org/W3102566467","https://openalex.org/W3173142043","https://openalex.org/W3191950921","https://openalex.org/W3203261904","https://openalex.org/W3214094275","https://openalex.org/W3217750961","https://openalex.org/W4200394082","https://openalex.org/W4210776654","https://openalex.org/W4211116959","https://openalex.org/W4211217734","https://openalex.org/W4226205752","https://openalex.org/W4283791586","https://openalex.org/W4290981288","https://openalex.org/W4295957082","https://openalex.org/W4312659804","https://openalex.org/W6727249380","https://openalex.org/W6747620207"],"related_works":["https://openalex.org/W2964006806","https://openalex.org/W2991588090","https://openalex.org/W3213052590","https://openalex.org/W2995074223","https://openalex.org/W176011693","https://openalex.org/W4376058722","https://openalex.org/W3034877844","https://openalex.org/W2068485218","https://openalex.org/W3107320758","https://openalex.org/W4225996487"],"abstract_inverted_index":{"Deep":[0],"neural":[1,64,75],"network":[2],"(DNN)":[3],"architectures":[4,44],"have":[5,26],"been":[6,28],"extensively":[7],"studied":[8],"for":[9,66,81,97,153,174],"estimating":[10],"the":[11,19,39,54,59,83,99,106,114,155],"state-of-health":[12],"(SOH)":[13],"of":[14,21,41,56,61,85,101,108,148,158,168],"Li-ion":[15],"batteries":[16],"(LIBs),":[17],"but":[18],"effects":[20,107],"hyperparameters":[22,57,109],"on":[23,58,110,192],"their":[24],"performance":[25,60],"not":[27],"thoroughly":[29],"analyzed.":[30],"As":[31],"a":[32,86,93,143,172],"result,":[33],"it":[34],"is":[35],"difficult":[36],"to":[37,46,127],"compare":[38,163],"effectiveness":[40],"different":[42],"DNN":[43],"and":[45,92,117,131,186],"extend":[47],"or":[48],"adapt":[49],"previous":[50],"works.":[51],"We":[52,104,161],"investigate":[53],"impact":[55],"feed":[62],"forward":[63],"networks":[65],"LIB":[67],"SOH":[68,84,100],"estimation.":[69],"Specifically,":[70],"we":[71,125],"propose":[72],"two":[73],"feed-forward":[74],"networks:":[76],"an":[77],"accuracy":[78,91,140],"model":[79,95,141],"optimized":[80,96],"predicting":[82,98,154],"single":[87],"cell":[88],"with":[89,166],"high":[90],"generalized":[94],"various":[102,111],"cells.":[103,160],"demonstrate":[105],"models":[112],"using":[113],"National":[115],"Aeronautics":[116],"Space":[118],"Administration":[119],"(NASA)":[120],"prognostic":[121],"battery":[122],"dataset,":[123],"which":[124],"cleanse":[126],"eliminate":[128],"anomalous":[129],"data":[130],"analyze":[132],"its":[133,151],"features.":[134],"Our":[135,177],"experiments":[136],"show":[137],"that":[138],"our":[139,164],"achieves":[142],"root":[144],"mean":[145],"square":[146],"error":[147],"0.33%,":[149],"demonstrating":[150],"efficacy":[152],"health":[156],"state":[157],"individual":[159],"also":[162],"results":[165],"those":[167],"other":[169],"studies,":[170],"establishing":[171],"benchmark":[173],"future":[175],"research.":[176],"open":[178],"resources,":[179],"including":[180],"code,":[181],"cleaned":[182],"data,":[183],"feature":[184],"analysis,":[185],"experimental":[187],"reports,":[188],"are":[189],"publicly":[190],"available":[191],"GitHub":[193],"(https://github.com/jhrrlee/batterystates).":[194]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}