{"id":"https://openalex.org/W4319990533","doi":"https://doi.org/10.1109/tvt.2023.3241647","title":"Supplementary Learning Control for Energy Management Strategy of Hybrid Electric Vehicles at Scale","display_name":"Supplementary Learning Control for Energy Management Strategy of Hybrid Electric Vehicles at Scale","publication_year":2023,"publication_date":"2023-02-02","ids":{"openalex":"https://openalex.org/W4319990533","doi":"https://doi.org/10.1109/tvt.2023.3241647"},"language":"en","primary_location":{"id":"doi:10.1109/tvt.2023.3241647","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tvt.2023.3241647","pdf_url":null,"source":{"id":"https://openalex.org/S10936095","display_name":"IEEE Transactions on Vehicular Technology","issn_l":"0018-9545","issn":["0018-9545","1939-9359"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Vehicular Technology","raw_type":"journal-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/A5024439487","display_name":"Yue Hu","orcid":"https://orcid.org/0000-0001-6961-8405"},"institutions":[{"id":"https://openalex.org/I4210153393","display_name":"Geely (China)","ror":"https://ror.org/0446d5v35","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210153393"]},{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yue Hu","raw_affiliation_strings":["Geely Research Institute, Zhejiang Geely Holding Group, Ningbo, China","Department of Automation, Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Geely Research Institute, Zhejiang Geely Holding Group, Ningbo, China","institution_ids":["https://openalex.org/I4210153393"]},{"raw_affiliation_string":"Department of Automation, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5051089032","display_name":"Hui Xu","orcid":"https://orcid.org/0000-0002-2823-5915"},"institutions":[{"id":"https://openalex.org/I4210153393","display_name":"Geely (China)","ror":"https://ror.org/0446d5v35","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210153393"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hui Xu","raw_affiliation_strings":["Geely Research Institute, Zhejiang Geely Holding Group, Ningbo, China"],"affiliations":[{"raw_affiliation_string":"Geely Research Institute, Zhejiang Geely Holding Group, Ningbo, China","institution_ids":["https://openalex.org/I4210153393"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5104274609","display_name":"Zhonglin Jiang","orcid":null},"institutions":[{"id":"https://openalex.org/I4210153393","display_name":"Geely (China)","ror":"https://ror.org/0446d5v35","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210153393"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhonglin Jiang","raw_affiliation_strings":["Geely Research Institute, Zhejiang Geely Holding Group, Ningbo, China"],"affiliations":[{"raw_affiliation_string":"Geely Research Institute, Zhejiang Geely Holding Group, Ningbo, China","institution_ids":["https://openalex.org/I4210153393"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5000135673","display_name":"Xinyu Zheng","orcid":"https://orcid.org/0000-0001-8425-3379"},"institutions":[{"id":"https://openalex.org/I4210153393","display_name":"Geely (China)","ror":"https://ror.org/0446d5v35","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210153393"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xinyu Zheng","raw_affiliation_strings":["Geely Research Institute, Zhejiang Geely Holding Group, Ningbo, China"],"affiliations":[{"raw_affiliation_string":"Geely Research Institute, Zhejiang Geely Holding Group, Ningbo, China","institution_ids":["https://openalex.org/I4210153393"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5029002447","display_name":"Jianfeng Zhang","orcid":"https://orcid.org/0000-0003-1686-9010"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jianfeng Zhang","raw_affiliation_strings":["Research Institute of China Evergrande New Energy Vehicle Group Limited, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"Research Institute of China Evergrande New Energy Vehicle Group Limited, Shanghai, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100693811","display_name":"Wenhui Fan","orcid":"https://orcid.org/0000-0002-0040-5759"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenhui Fan","raw_affiliation_strings":["Department of Automation, Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Department of Automation, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5079564314","display_name":"Kun Deng","orcid":"https://orcid.org/0000-0002-8707-1113"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kun Deng","raw_affiliation_strings":["Black Sesame Technologies Company, Ltd., Shanghai, China"],"affiliations":[{"raw_affiliation_string":"Black Sesame Technologies Company, Ltd., Shanghai, China","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5106480328","display_name":"Kun Xu","orcid":"https://orcid.org/0000-0003-1112-209X"},"institutions":[{"id":"https://openalex.org/I4210145761","display_name":"Shenzhen Institutes of Advanced Technology","ror":"https://ror.org/04gh4er46","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210145761"]},{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"funder","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210104064","display_name":"Shenzhen Academy of Robotics","ror":"https://ror.org/01h027j09","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210104064"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kun Xu","raw_affiliation_strings":["Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China","SIAT Branch, Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China","institution_ids":["https://openalex.org/I4210145761","https://openalex.org/I19820366"]},{"raw_affiliation_string":"SIAT Branch, Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen, China","institution_ids":["https://openalex.org/I4210104064"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":8,"corresponding_author_ids":["https://openalex.org/A5024439487"],"corresponding_institution_ids":["https://openalex.org/I4210153393","https://openalex.org/I99065089"],"apc_list":null,"apc_paid":null,"fwci":1.6101,"has_fulltext":false,"cited_by_count":14,"citation_normalized_percentile":{"value":0.82084498,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":100},"biblio":{"volume":"72","issue":"6","first_page":"7290","last_page":"7303"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10808","display_name":"Electric and Hybrid Vehicle Technologies","score":0.9998000264167786,"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/T10808","display_name":"Electric and Hybrid Vehicle Technologies","score":0.9998000264167786,"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":0.9994999766349792,"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/T10663","display_name":"Advanced Battery Technologies Research","score":0.9965000152587891,"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/reinforcement-learning","display_name":"Reinforcement learning","score":0.7860766649246216},{"id":"https://openalex.org/keywords/cloud-computing","display_name":"Cloud computing","score":0.6607854962348938},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.574289858341217},{"id":"https://openalex.org/keywords/electric-vehicle","display_name":"Electric vehicle","score":0.5362880229949951},{"id":"https://openalex.org/keywords/controller","display_name":"Controller (irrigation)","score":0.5342653393745422},{"id":"https://openalex.org/keywords/energy-management","display_name":"Energy management","score":0.523308515548706},{"id":"https://openalex.org/keywords/q-learning","display_name":"Q-learning","score":0.518638551235199},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.49668723344802856},{"id":"https://openalex.org/keywords/energy-management-system","display_name":"Energy management system","score":0.4506032466888428},{"id":"https://openalex.org/keywords/control-engineering","display_name":"Control engineering","score":0.41579577326774597},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.41481924057006836},{"id":"https://openalex.org/keywords/rule-based-system","display_name":"Rule-based system","score":0.4131196439266205},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.3749001920223236},{"id":"https://openalex.org/keywords/energy","display_name":"Energy (signal processing)","score":0.36687278747558594},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.36544984579086304},{"id":"https://openalex.org/keywords/automotive-engineering","display_name":"Automotive engineering","score":0.362140953540802},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.35716432332992554},{"id":"https://openalex.org/keywords/power","display_name":"Power (physics)","score":0.08216768503189087},{"id":"https://openalex.org/keywords/operating-system","display_name":"Operating system","score":0.07033303380012512}],"concepts":[{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.7860766649246216},{"id":"https://openalex.org/C79974875","wikidata":"https://www.wikidata.org/wiki/Q483639","display_name":"Cloud computing","level":2,"score":0.6607854962348938},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.574289858341217},{"id":"https://openalex.org/C2776422217","wikidata":"https://www.wikidata.org/wiki/Q13629441","display_name":"Electric vehicle","level":3,"score":0.5362880229949951},{"id":"https://openalex.org/C203479927","wikidata":"https://www.wikidata.org/wiki/Q5165939","display_name":"Controller (irrigation)","level":2,"score":0.5342653393745422},{"id":"https://openalex.org/C7817414","wikidata":"https://www.wikidata.org/wiki/Q1779504","display_name":"Energy management","level":3,"score":0.523308515548706},{"id":"https://openalex.org/C188116033","wikidata":"https://www.wikidata.org/wiki/Q2664563","display_name":"Q-learning","level":3,"score":0.518638551235199},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.49668723344802856},{"id":"https://openalex.org/C2781260460","wikidata":"https://www.wikidata.org/wiki/Q6139999","display_name":"Energy management system","level":4,"score":0.4506032466888428},{"id":"https://openalex.org/C133731056","wikidata":"https://www.wikidata.org/wiki/Q4917288","display_name":"Control engineering","level":1,"score":0.41579577326774597},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.41481924057006836},{"id":"https://openalex.org/C149271511","wikidata":"https://www.wikidata.org/wiki/Q1417149","display_name":"Rule-based system","level":2,"score":0.4131196439266205},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.3749001920223236},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.36687278747558594},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.36544984579086304},{"id":"https://openalex.org/C171146098","wikidata":"https://www.wikidata.org/wiki/Q124192","display_name":"Automotive engineering","level":1,"score":0.362140953540802},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.35716432332992554},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.08216768503189087},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.07033303380012512},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C6557445","wikidata":"https://www.wikidata.org/wiki/Q173113","display_name":"Agronomy","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","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/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tvt.2023.3241647","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tvt.2023.3241647","pdf_url":null,"source":{"id":"https://openalex.org/S10936095","display_name":"IEEE Transactions on Vehicular Technology","issn_l":"0018-9545","issn":["0018-9545","1939-9359"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Vehicular Technology","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.8899999856948853,"display_name":"Affordable and clean energy","id":"https://metadata.un.org/sdg/7"}],"awards":[{"id":"https://openalex.org/G1005674401","display_name":null,"funder_award_id":"62073311","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5045679718","display_name":null,"funder_award_id":"2022M712825","funder_id":"https://openalex.org/F4320321543","funder_display_name":"China Postdoctoral Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320321543","display_name":"China Postdoctoral Science Foundation","ror":"https://ror.org/0426zh255"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":36,"referenced_works":["https://openalex.org/W1948225962","https://openalex.org/W1987813263","https://openalex.org/W2010393174","https://openalex.org/W2044734585","https://openalex.org/W2050532354","https://openalex.org/W2174005552","https://openalex.org/W2424242071","https://openalex.org/W2435418089","https://openalex.org/W2761873684","https://openalex.org/W2765498735","https://openalex.org/W2781585732","https://openalex.org/W2786928559","https://openalex.org/W2787938642","https://openalex.org/W2793435266","https://openalex.org/W2918206979","https://openalex.org/W2920406591","https://openalex.org/W2936616423","https://openalex.org/W2947693385","https://openalex.org/W2970566041","https://openalex.org/W2980516483","https://openalex.org/W3009193590","https://openalex.org/W3016533673","https://openalex.org/W3033556475","https://openalex.org/W3033705788","https://openalex.org/W3044345870","https://openalex.org/W3048206271","https://openalex.org/W3048493514","https://openalex.org/W3090358476","https://openalex.org/W3107153805","https://openalex.org/W3107489687","https://openalex.org/W3197248161","https://openalex.org/W4250965498","https://openalex.org/W6744838376","https://openalex.org/W6747387971","https://openalex.org/W6748554570","https://openalex.org/W6748839928"],"related_works":["https://openalex.org/W2749199243","https://openalex.org/W2333017163","https://openalex.org/W2133545517","https://openalex.org/W2352231626","https://openalex.org/W2964681094","https://openalex.org/W2577786578","https://openalex.org/W2896932934","https://openalex.org/W1984852161","https://openalex.org/W2782741053","https://openalex.org/W1996876120"],"abstract_inverted_index":{"Deep":[0],"reinforcement":[1],"learning":[2,49],"(DRL)":[3],"based":[4,52],"energy":[5,19],"management":[6],"strategy":[7],"(EMS)":[8],"for":[9,57,66,70,113,169,187],"hybrid":[10],"electric":[11],"vehicles":[12],"(HEVs)":[13],"has":[14],"shown":[15],"its":[16,38,129],"effectiveness":[17],"in":[18,124,151,176],"saving.":[20],"However,":[21],"there":[22],"is":[23,111,183],"a":[24,47,108,138,184],"huge":[25],"gap":[26],"between":[27],"simulation":[28,195],"and":[29,40,142,158],"the":[30,71,76,81,85,94,99,105,114,144,147,152,160,166,188],"real":[31,197],"application":[32],"of":[33,96,122,156,165],"DRL-based":[34,115,189],"methods":[35],"due":[36],"to":[37,55,80,98,103,137,146,196],"uncertainty":[39,95],"low":[41],"converge":[42,106],"speed.":[43],"This":[44],"paper":[45],"proposes":[46],"supplementary":[48],"controller":[50],"(SLC)":[51],"on":[53],"DRL":[54],"compensate":[56],"an":[58,67,89],"existing":[59,82],"rule-based":[60,77,83],"EMS.":[61,78],"The":[62,120,149,163,179],"proposed":[63,167,180],"SLC":[64,86,117,123,168,182],"searches":[65],"optimal":[68,90],"solution":[69,91],"action":[72],"which":[73],"works":[74],"alongside":[75],"Due":[79],"EMS,":[84],"can":[87],"get":[88],"while":[92],"reducing":[93],"algorithm":[97],"system.":[100],"In":[101],"order":[102],"improve":[104],"speed,":[107],"distributed":[109],"architecture":[110],"designed":[112],"HEV":[116,170,181,190],"at":[118,172],"scale.":[119],"actor":[121],"each":[125],"vehicle":[126],"interacts":[127],"with":[128],"own":[130],"driving":[131],"cycle":[132],"by":[133],"searching":[134],"actions":[135],"according":[136],"shared":[139],"neural":[140,161],"network,":[141],"sends":[143],"experience":[145,157],"cloud.":[148],"learner":[150],"cloud":[153],"replays":[154],"samples":[155],"updates":[159],"network.":[162],"performance":[164],"EMS":[171,191],"scale":[173],"was":[174],"demonstrated":[175],"different":[177],"conditions.":[178],"good":[185],"step":[186],"method":[192],"transferring":[193],"from":[194],"application.":[198]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":7},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
