{"id":"https://openalex.org/W4285813027","doi":"https://doi.org/10.1109/iv51971.2022.9827179","title":"Predicting real life electric vehicle fast charging session duration using neural networks","display_name":"Predicting real life electric vehicle fast charging session duration using neural networks","publication_year":2022,"publication_date":"2022-06-05","ids":{"openalex":"https://openalex.org/W4285813027","doi":"https://doi.org/10.1109/iv51971.2022.9827179"},"language":"en","primary_location":{"id":"doi:10.1109/iv51971.2022.9827179","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iv51971.2022.9827179","pdf_url":null,"source":{"id":"https://openalex.org/S4363605370","display_name":"2022 IEEE Intelligent Vehicles Symposium (IV)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE Intelligent Vehicles Symposium (IV)","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/A5090198878","display_name":"Anthony Desch\u00eanes","orcid":"https://orcid.org/0000-0002-6670-6837"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Anthony Deschenes","raw_affiliation_strings":["Universit&#x00E9; Laval,CRISI Research Consortium for Industry 4.0 System Engineering,Qu&#x00E9;bec,Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Universit&#x00E9; Laval,CRISI Research Consortium for Industry 4.0 System Engineering,Qu&#x00E9;bec,Canada","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5083689259","display_name":"Jonathan Gaudreault","orcid":"https://orcid.org/0000-0001-5493-8836"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jonathan Gaudreault","raw_affiliation_strings":["Universit&#x00E9; Laval,CRISI Research Consortium for Industry 4.0 System Engineering,Qu&#x00E9;bec,Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Universit&#x00E9; Laval,CRISI Research Consortium for Industry 4.0 System Engineering,Qu&#x00E9;bec,Canada","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5046366918","display_name":"Claude-Guy Quimper","orcid":"https://orcid.org/0000-0002-5899-0217"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Claude-Guy Quimper","raw_affiliation_strings":["Universit&#x00E9; Laval,CRISI Research Consortium for Industry 4.0 System Engineering,Qu&#x00E9;bec,Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Universit&#x00E9; Laval,CRISI Research Consortium for Industry 4.0 System Engineering,Qu&#x00E9;bec,Canada","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.9168,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.70307475,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"1327","last_page":"1332"},"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/T10768","display_name":"Electric Vehicles and Infrastructure","score":0.9998999834060669,"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":0.9900000095367432,"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/electric-vehicle","display_name":"Electric vehicle","score":0.6976322531700134},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6692548394203186},{"id":"https://openalex.org/keywords/hyperparameter","display_name":"Hyperparameter","score":0.6301625370979309},{"id":"https://openalex.org/keywords/battery","display_name":"Battery (electricity)","score":0.6039746999740601},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5390488505363464},{"id":"https://openalex.org/keywords/voltage","display_name":"Voltage","score":0.5265624523162842},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.43436968326568604},{"id":"https://openalex.org/keywords/range","display_name":"Range (aeronautics)","score":0.432412326335907},{"id":"https://openalex.org/keywords/simulation","display_name":"Simulation","score":0.3854571282863617},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.33545756340026855},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.3325190842151642},{"id":"https://openalex.org/keywords/electrical-engineering","display_name":"Electrical engineering","score":0.11512616276741028}],"concepts":[{"id":"https://openalex.org/C2776422217","wikidata":"https://www.wikidata.org/wiki/Q13629441","display_name":"Electric vehicle","level":3,"score":0.6976322531700134},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6692548394203186},{"id":"https://openalex.org/C8642999","wikidata":"https://www.wikidata.org/wiki/Q4171168","display_name":"Hyperparameter","level":2,"score":0.6301625370979309},{"id":"https://openalex.org/C555008776","wikidata":"https://www.wikidata.org/wiki/Q267298","display_name":"Battery (electricity)","level":3,"score":0.6039746999740601},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5390488505363464},{"id":"https://openalex.org/C165801399","wikidata":"https://www.wikidata.org/wiki/Q25428","display_name":"Voltage","level":2,"score":0.5265624523162842},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.43436968326568604},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.432412326335907},{"id":"https://openalex.org/C44154836","wikidata":"https://www.wikidata.org/wiki/Q45045","display_name":"Simulation","level":1,"score":0.3854571282863617},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.33545756340026855},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.3325190842151642},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.11512616276741028},{"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/C146978453","wikidata":"https://www.wikidata.org/wiki/Q3798668","display_name":"Aerospace engineering","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/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iv51971.2022.9827179","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iv51971.2022.9827179","pdf_url":null,"source":{"id":"https://openalex.org/S4363605370","display_name":"2022 IEEE Intelligent Vehicles Symposium (IV)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE Intelligent Vehicles Symposium (IV)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Responsible consumption and production","id":"https://metadata.un.org/sdg/12","score":0.5299999713897705}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":30,"referenced_works":["https://openalex.org/W2101234009","https://openalex.org/W2131241448","https://openalex.org/W2135900825","https://openalex.org/W2148143831","https://openalex.org/W2194775991","https://openalex.org/W2325312983","https://openalex.org/W2336603544","https://openalex.org/W2593704234","https://openalex.org/W2597868224","https://openalex.org/W2787278293","https://openalex.org/W2793858781","https://openalex.org/W2900253857","https://openalex.org/W2905126357","https://openalex.org/W2955489327","https://openalex.org/W2970532630","https://openalex.org/W3011974631","https://openalex.org/W3022336262","https://openalex.org/W3035035250","https://openalex.org/W3086833944","https://openalex.org/W3087579330","https://openalex.org/W3113030305","https://openalex.org/W4255390387","https://openalex.org/W4297779039","https://openalex.org/W4297790878","https://openalex.org/W6675354045","https://openalex.org/W6678911119","https://openalex.org/W6734989823","https://openalex.org/W6738897456","https://openalex.org/W6755310813","https://openalex.org/W6779681028"],"related_works":["https://openalex.org/W2140186469","https://openalex.org/W4280563792","https://openalex.org/W4318719684","https://openalex.org/W4318559728","https://openalex.org/W3183136280","https://openalex.org/W2775233965","https://openalex.org/W3114716045","https://openalex.org/W4360995913","https://openalex.org/W2609418570","https://openalex.org/W3114025147"],"abstract_inverted_index":{"Predicting":[0],"the":[1,19,22,79,87,95,103,115,124,128,150,169,193],"time":[2,100],"needed":[3],"to":[4,11,18,142,167,191],"charge":[5],"an":[6],"electric":[7,48,88,104,116,194],"vehicle":[8,125,175,195],"from":[9,43],"X%":[10],"Y%":[12],"is":[13,153,181],"a":[14,134,182,186],"difficult":[15],"task":[16],"due":[17],"nonlinearity":[20],"of":[21,47,86,91,149],"charging":[23,41,99,129],"process":[24],"and":[25,32,65,71,110,127,139,162],"other":[26],"external":[27,80],"factors":[28],"such":[29,60],"as":[30,61],"temperature":[31],"battery":[33,82],"degradation.":[34],"Using":[35],"28,000":[36],"real-life":[37],"level":[38],"3":[39],"fast":[40],"sessions":[42],"15":[44],"different":[45],"types":[46],"vehicles,":[49],"we":[50],"train":[51],"models":[52,59,75,119,159],"for":[53,173],"this":[54],"task.":[55],"We":[56,132],"compare":[57],"learning":[58],"random":[62],"forest,":[63],"linear":[64],"seconddegree":[66],"regressions,":[67,70],"support":[68],"vector":[69],"neural":[72,151,183,188],"networks.":[73],"The":[74,118,147,177],"take":[76,121],"into":[77,122],"consideration":[78,123],"temperature,":[81],"capacity,":[83],"nominal":[84],"capacity":[85],"vehicle,":[89,105],"number":[90],"charges":[92],"made":[93],"during":[94],"same":[96],"day,":[97],"maximum":[98,108,111],"allowed":[101],"by":[102,114],"target":[106],"voltage,":[107],"voltage":[109],"current":[112],"asked":[113],"vehicle.":[117],"also":[120],"type":[126],"station":[130],"type.":[131,196],"use":[133],"data":[135],"augmentation":[136],"technique":[137],"(SMOTE)":[138],"hyperparameters":[140],"optimization":[141],"enhance":[143],"our":[144],"model":[145,172,180],"performances.":[146],"structure":[148],"networks":[152],"optimized":[154],"using":[155],"Bayesian":[156],"optimization.":[157],"All":[158],"are":[160],"trained":[161],"statistically":[163],"compared":[164],"in":[165],"order":[166],"find":[168],"overall":[170,178],"best":[171,179],"all":[174],"types.":[176],"network":[184,189],"with":[185],"sub":[187],"pre-trained":[190],"predict":[192]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2023,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
