{"id":"https://openalex.org/W4206158236","doi":"https://doi.org/10.5573/ieiespc.2021.10.6.496","title":"Study of Battery State-of-charge Estimation with kNN Machine Learning Method","display_name":"Study of Battery State-of-charge Estimation with kNN Machine Learning Method","publication_year":2021,"publication_date":"2021-12-31","ids":{"openalex":"https://openalex.org/W4206158236","doi":"https://doi.org/10.5573/ieiespc.2021.10.6.496"},"language":"en","primary_location":{"id":"doi:10.5573/ieiespc.2021.10.6.496","doi":"https://doi.org/10.5573/ieiespc.2021.10.6.496","is_oa":false,"landing_page_url":"https://doi.org/10.5573/ieiespc.2021.10.6.496","pdf_url":null,"source":{"id":"https://openalex.org/S2765077331","display_name":"IEIE Transactions on Smart Processing and Computing","issn_l":"2287-5255","issn":["2287-5255"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","kci-registered","poland-20","russia-white-list-4"],"host_organization":"https://openalex.org/P4366305964","host_organization_name":"Institute of Electronics Engineers of Korea","host_organization_lineage":["https://openalex.org/P4366305964"],"host_organization_lineage_names":["Institute of Electronics Engineers of Korea"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEIE Transactions on Smart Processing &amp; Computing","raw_type":"journal-article"},"volume":null,"type":"article","is_traditional":true,"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/A5087643737","display_name":"Teressa Talluri","orcid":"https://orcid.org/0000-0003-1168-7664","observed_orcids":["https://orcid.org/0000-0003-1168-7664"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Teressa Talluri","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102478728","display_name":"Hee Tae Chung","orcid":"https://orcid.org/0009-0001-2945-1337","observed_orcids":["https://orcid.org/0009-0001-2945-1337"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hee Tae Chung","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5070040269","display_name":"Kyoojae Shin","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kyoojae Shin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.1759,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.47772512,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":88,"max":97},"biblio":{"volume":"10","issue":"6","first_page":"496","last_page":"504"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10663","display_name":"Battery Modeling and Management","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":"Battery Modeling and Management","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/T10018","display_name":"Advancements in Battery Materials","score":0.0,"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/T10808","display_name":"Electric and Hybrid Vehicle Technologies","score":0.0,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/state-of-charge-estimation","display_name":"state of charge estimation","score":0.8579999804496765},{"id":"https://openalex.org/keywords/lithium-polymer-batteries","display_name":"lithium polymer batteries","score":0.8410000205039978},{"id":"https://openalex.org/keywords/k-nearest-neighbors","display_name":"k-nearest neighbors","score":0.8889999985694885},{"id":"https://openalex.org/keywords/electric-vehicles","display_name":"electric vehicles","score":0.8489999771118164},{"id":"https://openalex.org/keywords/depth-of-discharge","display_name":"depth of discharge","score":0.8640000224113464},{"id":"https://openalex.org/keywords/battery-degradation","display_name":"battery degradation","score":0.6899999976158142},{"id":"https://openalex.org/keywords/charging-and-discharging-cycles","display_name":"charging and discharging cycles","score":0.734000027179718},{"id":"https://openalex.org/keywords/battery-safety","display_name":"battery safety","score":0.39100000262260437}],"concepts":[{"id":"https://openalex.org/C555008776","wikidata":"https://www.wikidata.org/wiki/Q267298","display_name":"Battery (electricity)","level":3,"score":0.8149150609970093},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7530900239944458},{"id":"https://openalex.org/C2776582896","wikidata":"https://www.wikidata.org/wiki/Q5368536","display_name":"State of charge","level":4,"score":0.6990740895271301},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.6746752262115479},{"id":"https://openalex.org/C165801399","wikidata":"https://www.wikidata.org/wiki/Q25428","display_name":"Voltage","level":2,"score":0.41945961117744446},{"id":"https://openalex.org/C44154836","wikidata":"https://www.wikidata.org/wiki/Q45045","display_name":"Simulation","level":1,"score":0.3801470994949341},{"id":"https://openalex.org/C171146098","wikidata":"https://www.wikidata.org/wiki/Q124192","display_name":"Automotive engineering","level":1,"score":0.3661730885505676},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3320724070072174},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.1840776801109314},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.12482854723930359},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.10877478122711182},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0977596640586853},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.06833600997924805},{"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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.5573/ieiespc.2021.10.6.496","doi":"https://doi.org/10.5573/ieiespc.2021.10.6.496","is_oa":false,"landing_page_url":"https://doi.org/10.5573/ieiespc.2021.10.6.496","pdf_url":null,"source":{"id":"https://openalex.org/S2765077331","display_name":"IEIE Transactions on Smart Processing and Computing","issn_l":"2287-5255","issn":["2287-5255"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","kci-registered","poland-20","russia-white-list-4"],"host_organization":"https://openalex.org/P4366305964","host_organization_name":"Institute of Electronics Engineers of Korea","host_organization_lineage":["https://openalex.org/P4366305964"],"host_organization_lineage_names":["Institute of Electronics Engineers of Korea"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEIE Transactions on Smart Processing &amp; Computing","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/7","display_name":"Affordable and clean energy","score":0.75},{"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure","score":0.5378999710083008}],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4313824564","https://openalex.org/W3183525731","https://openalex.org/W3012309884","https://openalex.org/W4392153358","https://openalex.org/W4403723814","https://openalex.org/W7117691535","https://openalex.org/W4402883410","https://openalex.org/W4403289556","https://openalex.org/W4409803221","https://openalex.org/W7140338516"],"abstract_inverted_index":{"Electric":[0],"vehicles":[1],"have":[2,17],"high":[3,19,27],"demand":[4],"due":[5,24],"to":[6,25,40,62,72,146],"their":[7,26],"ecofriendly":[8],"nature.":[9],"From":[10],"this":[11,97],"point":[12],"of":[13,36,49,53,59,101,174],"view,":[14],"lithium":[15,103],"batteries":[16],"gained":[18],"attention":[20],"in":[21,70,86,93,108],"recent":[22],"days":[23],"efficiency":[28],"and":[29,56,113,118,124,176],"long":[30],"life":[31,58,76],"time.":[32],"Hence,":[33],"it":[34],"is":[35,82],"the":[37,42,47,74,94,99,121,128,140,148,154],"utmost":[38],"importance":[39],"evaluate":[41],"battery":[43,61,64,75,91,105],"characteristics,":[44],"such":[45,138],"as":[46,139,181,183],"state":[48],"charge":[50],"(SOC),":[51],"depth":[52],"discharge":[54],"(DOD),":[55],"remaining":[57],"a":[60,88,102,109,156],"ensure":[63],"safety.":[65],"These":[66],"parameters":[67],"were":[68,116],"derived":[69],"order":[71],"estimate":[73,147],"time":[77,125],"before":[78],"degradation.":[79],"This":[80,131],"estimation":[81],"very":[83],"much":[84],"required":[85],"making":[87],"decision":[89],"about":[90],"usage":[92],"future.":[95],"In":[96],"study,":[98],"SOC":[100,149],"polymer":[104],"was":[106,158,163],"evaluated":[107],"real-time":[110],"experiment.":[111],"Charging":[112],"discharging":[114],"cycles":[115],"done,":[117],"we":[119],"obtained":[120],"voltage,":[122],"current,":[123],"data":[126,133],"from":[127],"experimental":[129,132,166],"result.":[130],"trained":[134],"machine":[135],"learning":[136],"methods":[137],"kNN":[141],"(k":[142],"Nearest":[143],"Neighbor)":[144],"method":[145],"more":[150],"precisely.":[151],"After":[152],"training":[153],"model,":[155],"test":[157],"done.":[159],"The":[160,168],"proposed":[161],"estimator":[162],"calibrated":[164],"by":[165],"data.":[167],"results":[169],"are":[170],"satisfactory":[171],"with":[172],"accuracy":[173],"98%":[175],"mean":[177],"absolute":[178],"error":[179],"(MAE)":[180],"low":[182],"0.74[%].":[184]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1}],"updated_date":"2026-11-04T10:54:05.112534","created_date":"2022-01-26T00:00:00"}
