{"id":"https://openalex.org/W4415180690","doi":"https://doi.org/10.23919/ecc65951.2025.11186914","title":"Machine learning-based online adaptive prediction for electric vehicle energy consumption","display_name":"Machine learning-based online adaptive prediction for electric vehicle energy consumption","publication_year":2025,"publication_date":"2025-06-24","ids":{"openalex":"https://openalex.org/W4415180690","doi":"https://doi.org/10.23919/ecc65951.2025.11186914"},"language":"en","primary_location":{"id":"doi:10.23919/ecc65951.2025.11186914","is_oa":false,"landing_page_url":"https://doi.org/10.23919/ecc65951.2025.11186914","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 European Control Conference (ECC)","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/A5103248994","display_name":"Qingbo Zhu","orcid":"https://orcid.org/0009-0007-1890-7793"},"institutions":[{"id":"https://openalex.org/I66862912","display_name":"Chalmers University of Technology","ror":"https://ror.org/040wg7k59","country_code":"SE","type":"education","lineage":["https://openalex.org/I66862912"]}],"countries":["SE"],"is_corresponding":true,"raw_author_name":"Qingbo Zhu","raw_affiliation_strings":["Chalmers University of Technology,Department of Architecture and Civil Engineering and the Department of Electrical Engineering,Gothenburg,Sweden,41296"],"affiliations":[{"raw_affiliation_string":"Chalmers University of Technology,Department of Architecture and Civil Engineering and the Department of Electrical Engineering,Gothenburg,Sweden,41296","institution_ids":["https://openalex.org/I66862912"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5017097810","display_name":"Yicun Huang","orcid":"https://orcid.org/0000-0003-2644-3603"},"institutions":[{"id":"https://openalex.org/I66862912","display_name":"Chalmers University of Technology","ror":"https://ror.org/040wg7k59","country_code":"SE","type":"education","lineage":["https://openalex.org/I66862912"]}],"countries":["SE"],"is_corresponding":false,"raw_author_name":"Yicun Huang","raw_affiliation_strings":["Chalmers University of Technology,Department of Electrical Engineering,Gothenburg,Sweden,41296"],"affiliations":[{"raw_affiliation_string":"Chalmers University of Technology,Department of Electrical Engineering,Gothenburg,Sweden,41296","institution_ids":["https://openalex.org/I66862912"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5080441849","display_name":"Chih Feng Lee","orcid":"https://orcid.org/0000-0003-4337-3723"},"institutions":[{"id":"https://openalex.org/I4210116026","display_name":"Polestar Technologies (United States)","ror":"https://ror.org/02cvrf195","country_code":"US","type":"company","lineage":["https://openalex.org/I4210116026"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Chih Feng Lee","raw_affiliation_strings":["Polestar Performance AB,Gothenburg,Sweden,41878"],"affiliations":[{"raw_affiliation_string":"Polestar Performance AB,Gothenburg,Sweden,41878","institution_ids":["https://openalex.org/I4210116026"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100346901","display_name":"Peng Liu","orcid":"https://orcid.org/0000-0003-3796-0980"},"institutions":[{"id":"https://openalex.org/I4210105239","display_name":"National Institute of Education Sciences","ror":"https://ror.org/01dfm1a30","country_code":"CN","type":"education","lineage":["https://openalex.org/I1327237609","https://openalex.org/I4210105239","https://openalex.org/I4210127390"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Peng Liu","raw_affiliation_strings":["National Engineering Research Center of Electric Vehicles, Beijing Institute of Technology,Beijing,China,100081"],"affiliations":[{"raw_affiliation_string":"National Engineering Research Center of Electric Vehicles, Beijing Institute of Technology,Beijing,China,100081","institution_ids":["https://openalex.org/I4210105239"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5080552363","display_name":"Jin Zhang","orcid":"https://orcid.org/0000-0002-6691-5612"},"institutions":[{"id":"https://openalex.org/I4210105239","display_name":"National Institute of Education Sciences","ror":"https://ror.org/01dfm1a30","country_code":"CN","type":"education","lineage":["https://openalex.org/I1327237609","https://openalex.org/I4210105239","https://openalex.org/I4210127390"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jin Zhang","raw_affiliation_strings":["National Engineering Research Center of Electric Vehicles, Beijing Institute of Technology,Beijing,China,100081"],"affiliations":[{"raw_affiliation_string":"National Engineering Research Center of Electric Vehicles, Beijing Institute of Technology,Beijing,China,100081","institution_ids":["https://openalex.org/I4210105239"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5050466193","display_name":"Torsten Wik","orcid":"https://orcid.org/0000-0002-5234-8426"},"institutions":[{"id":"https://openalex.org/I66862912","display_name":"Chalmers University of Technology","ror":"https://ror.org/040wg7k59","country_code":"SE","type":"education","lineage":["https://openalex.org/I66862912"]}],"countries":["SE"],"is_corresponding":false,"raw_author_name":"Torsten Wik","raw_affiliation_strings":["Chalmers University of Technology,Department of Electrical Engineering,Gothenburg,Sweden,41296"],"affiliations":[{"raw_affiliation_string":"Chalmers University of Technology,Department of Electrical Engineering,Gothenburg,Sweden,41296","institution_ids":["https://openalex.org/I66862912"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5103248994"],"corresponding_institution_ids":["https://openalex.org/I66862912"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.30964613,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"2569","last_page":"2574"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10768","display_name":"Electric Vehicles and Infrastructure","score":0.9668999910354614,"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"}},"topics":[{"id":"https://openalex.org/T10768","display_name":"Electric Vehicles and Infrastructure","score":0.9668999910354614,"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/T11052","display_name":"Energy Load and Power Forecasting","score":0.9480999708175659,"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/T12095","display_name":"Vehicle emissions and performance","score":0.9424999952316284,"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":0.651199996471405},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5946999788284302},{"id":"https://openalex.org/keywords/energy-consumption","display_name":"Energy consumption","score":0.5504000186920166},{"id":"https://openalex.org/keywords/range","display_name":"Range (aeronautics)","score":0.5210999846458435},{"id":"https://openalex.org/keywords/energy","display_name":"Energy (signal processing)","score":0.48730000853538513},{"id":"https://openalex.org/keywords/quantile","display_name":"Quantile","score":0.37560001015663147},{"id":"https://openalex.org/keywords/quantile-regression","display_name":"Quantile regression","score":0.36390000581741333},{"id":"https://openalex.org/keywords/point","display_name":"Point (geometry)","score":0.3537999987602234},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.3249000012874603}],"concepts":[{"id":"https://openalex.org/C2776422217","wikidata":"https://www.wikidata.org/wiki/Q13629441","display_name":"Electric vehicle","level":3,"score":0.651199996471405},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6004999876022339},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5946999788284302},{"id":"https://openalex.org/C2780165032","wikidata":"https://www.wikidata.org/wiki/Q16869822","display_name":"Energy consumption","level":2,"score":0.5504000186920166},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.5210999846458435},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.48730000853538513},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4032000005245209},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4016000032424927},{"id":"https://openalex.org/C118671147","wikidata":"https://www.wikidata.org/wiki/Q578714","display_name":"Quantile","level":2,"score":0.37560001015663147},{"id":"https://openalex.org/C63817138","wikidata":"https://www.wikidata.org/wiki/Q3455889","display_name":"Quantile regression","level":2,"score":0.36390000581741333},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.3537999987602234},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.3249000012874603},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.3237999975681305},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.32359999418258667},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.323199987411499},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.3203999996185303},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3147999942302704},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.3109000027179718},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.30309998989105225},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.29170000553131104},{"id":"https://openalex.org/C2777851325","wikidata":"https://www.wikidata.org/wiki/Q7094102","display_name":"Online model","level":2,"score":0.2847000062465668},{"id":"https://openalex.org/C29592376","wikidata":"https://www.wikidata.org/wiki/Q206799","display_name":"Electric energy","level":3,"score":0.28439998626708984},{"id":"https://openalex.org/C120936955","wikidata":"https://www.wikidata.org/wiki/Q2155640","display_name":"Empirical research","level":2,"score":0.28369998931884766},{"id":"https://openalex.org/C2742236","wikidata":"https://www.wikidata.org/wiki/Q924713","display_name":"Efficient energy use","level":2,"score":0.2689000070095062},{"id":"https://openalex.org/C167085575","wikidata":"https://www.wikidata.org/wiki/Q6803654","display_name":"Mean squared prediction error","level":2,"score":0.2671000063419342},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.266400009393692},{"id":"https://openalex.org/C2779027077","wikidata":"https://www.wikidata.org/wiki/Q29954","display_name":"Electric energy consumption","level":4,"score":0.26269999146461487},{"id":"https://openalex.org/C30772137","wikidata":"https://www.wikidata.org/wiki/Q5164762","display_name":"Consumption (sociology)","level":2,"score":0.2605000138282776},{"id":"https://openalex.org/C196921405","wikidata":"https://www.wikidata.org/wiki/Q786431","display_name":"Online algorithm","level":2,"score":0.2547000050544739}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.23919/ecc65951.2025.11186914","is_oa":false,"landing_page_url":"https://doi.org/10.23919/ecc65951.2025.11186914","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 European Control Conference (ECC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W1996905516","https://openalex.org/W2016087887","https://openalex.org/W2136658108","https://openalex.org/W2260322208","https://openalex.org/W2338377012","https://openalex.org/W2422072729","https://openalex.org/W2473930607","https://openalex.org/W2603621654","https://openalex.org/W2912271129","https://openalex.org/W3037516875","https://openalex.org/W3083790941","https://openalex.org/W3111921477","https://openalex.org/W3153555717","https://openalex.org/W3160775840","https://openalex.org/W3177202111","https://openalex.org/W3181487872","https://openalex.org/W4241653265","https://openalex.org/W4283389764"],"related_works":[],"abstract_inverted_index":{"Precisely":[0],"forecasting":[1],"the":[2,11,21,38,142,149,158,163],"energy":[3,31],"consumption":[4],"of":[5,40,72,110,127,144,166],"electric":[6,112],"vehicles":[7],"not":[8],"only":[9],"alleviates":[10],"anxiety":[12],"associated":[13],"with":[14,139],"driving":[15],"range":[16],"but":[17],"also":[18,90],"serves":[19],"as":[20],"foundation":[22],"for":[23],"progressive":[24],"advancements,":[25],"including":[26],"optimizing":[27],"charging":[28],"strategy":[29],"and":[30,57,76,89,102,161],"utilization.":[32],"The":[33,97,114],"main":[34],"challenge":[35],"lies":[36],"in":[37,86,133,156],"inaccuracy":[39],"current":[41],"methods,":[42],"whether":[43],"they":[44],"are":[45],"empirical":[46],"models,":[47,49],"physics-based":[48],"or":[50],"data-driven":[51],"models.":[52],"Based":[53],"on":[54,94],"newly":[55],"constructed":[56],"engineered":[58],"physics-informed":[59],"features,":[60],"this":[61],"paper":[62],"introduces":[63],"a":[64,70,108,134,153],"machine":[65],"learning-based":[66],"prediction":[67,95,167],"framework,":[68],"employing":[69],"synergy":[71],"offline":[73],"global":[74,116],"models":[75,146],"vehicle-based":[77],"online":[78,130,150],"adaptation.":[79],"This":[80],"combination":[81],"aims":[82],"to":[83,137],"elevate":[84],"accuracy":[85],"point":[87],"predictions":[88],"provide":[91],"valuable":[92],"information":[93],"uncertainties.":[96],"developed":[98],"framework":[99],"is":[100],"trained":[101],"extensively":[103],"tested":[104],"using":[105],"data":[106],"from":[107],"fleet":[109],"real-world":[111],"vehicles.":[113],"leading":[115],"model,":[117],"quantile":[118],"regression":[119],"neural":[120],"network":[121],"(QRNN),":[122],"demonstrates":[123],"an":[124],"average":[125,164],"error":[126],"6.30%.":[128],"Subsequent":[129],"adaptation":[131],"results":[132],"notable":[135],"reduction":[136],"5.04%,":[138],"both":[140],"surpassing":[141],"performance":[143],"existing":[145],"significantly.":[147],"Concurrently,":[148],"QRNN":[151],"exhibits":[152],"strong":[154],"capability":[155],"enhancing":[157],"coverage":[159],"probability":[160],"decreasing":[162],"width":[165],"intervals.":[168]},"counts_by_year":[],"updated_date":"2026-03-07T16:01:11.037858","created_date":"2025-10-15T00:00:00"}
