{"id":"https://openalex.org/W4409917160","doi":"https://doi.org/10.1109/tie.2025.3561773","title":"Two-Stage Power Scheduling for More Electric Aircraft: A Hybrid Deep Reinforcement Learning-Based Strategy","display_name":"Two-Stage Power Scheduling for More Electric Aircraft: A Hybrid Deep Reinforcement Learning-Based Strategy","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4409917160","doi":"https://doi.org/10.1109/tie.2025.3561773"},"language":"en","primary_location":{"id":"doi:10.1109/tie.2025.3561773","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tie.2025.3561773","pdf_url":null,"source":{"id":"https://openalex.org/S58031724","display_name":"IEEE Transactions on Industrial Electronics","issn_l":"0278-0046","issn":["0278-0046","1557-9948"],"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 Industrial Electronics","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/A5102731505","display_name":"Hao Yang","orcid":"https://orcid.org/0000-0002-4506-5524"},"institutions":[{"id":"https://openalex.org/I55712492","display_name":"Zhejiang University of Technology","ror":"https://ror.org/02djqfd08","country_code":"CN","type":"education","lineage":["https://openalex.org/I55712492"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Hao Yang","raw_affiliation_strings":["Department of Automation, Zhejiang University of Technology, Hangzhou, China"],"affiliations":[{"raw_affiliation_string":"Department of Automation, Zhejiang University of Technology, Hangzhou, China","institution_ids":["https://openalex.org/I55712492"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5046792888","display_name":"Shan Liu","orcid":"https://orcid.org/0000-0003-0854-8065"},"institutions":[{"id":"https://openalex.org/I55712492","display_name":"Zhejiang University of Technology","ror":"https://ror.org/02djqfd08","country_code":"CN","type":"education","lineage":["https://openalex.org/I55712492"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shan Liu","raw_affiliation_strings":["Department of Automation, Zhejiang University of Technology, Hangzhou, China"],"affiliations":[{"raw_affiliation_string":"Department of Automation, Zhejiang University of Technology, Hangzhou, China","institution_ids":["https://openalex.org/I55712492"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068826373","display_name":"Fanghong Guo","orcid":"https://orcid.org/0000-0003-1721-266X"},"institutions":[{"id":"https://openalex.org/I55712492","display_name":"Zhejiang University of Technology","ror":"https://ror.org/02djqfd08","country_code":"CN","type":"education","lineage":["https://openalex.org/I55712492"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fanghong Guo","raw_affiliation_strings":["Department of Automation, Zhejiang University of Technology, Hangzhou, China"],"affiliations":[{"raw_affiliation_string":"Department of Automation, Zhejiang University of Technology, Hangzhou, China","institution_ids":["https://openalex.org/I55712492"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5000725118","display_name":"Xiang Wu","orcid":"https://orcid.org/0000-0003-4845-3159"},"institutions":[{"id":"https://openalex.org/I55712492","display_name":"Zhejiang University of Technology","ror":"https://ror.org/02djqfd08","country_code":"CN","type":"education","lineage":["https://openalex.org/I55712492"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiang Wu","raw_affiliation_strings":["Department of Automation, Zhejiang University of Technology, Hangzhou, China"],"affiliations":[{"raw_affiliation_string":"Department of Automation, Zhejiang University of Technology, Hangzhou, China","institution_ids":["https://openalex.org/I55712492"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5102731505"],"corresponding_institution_ids":["https://openalex.org/I55712492"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.07178994,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"11"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10768","display_name":"Electric Vehicles and Infrastructure","score":0.9923999905586243,"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":0.9923999905586243,"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":0.9812999963760376,"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/T10808","display_name":"Electric and Hybrid Vehicle Technologies","score":0.977400004863739,"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":0.7575026750564575},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5581727027893066},{"id":"https://openalex.org/keywords/scheduling","display_name":"Scheduling (production processes)","score":0.46987563371658325},{"id":"https://openalex.org/keywords/stage","display_name":"Stage (stratigraphy)","score":0.4373289942741394},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.34136074781417847},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.32507094740867615},{"id":"https://openalex.org/keywords/automotive-engineering","display_name":"Automotive engineering","score":0.3218587040901184},{"id":"https://openalex.org/keywords/operations-management","display_name":"Operations management","score":0.08325329422950745}],"concepts":[{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.7575026750564575},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5581727027893066},{"id":"https://openalex.org/C206729178","wikidata":"https://www.wikidata.org/wiki/Q2271896","display_name":"Scheduling (production processes)","level":2,"score":0.46987563371658325},{"id":"https://openalex.org/C146357865","wikidata":"https://www.wikidata.org/wiki/Q1123245","display_name":"Stage (stratigraphy)","level":2,"score":0.4373289942741394},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.34136074781417847},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.32507094740867615},{"id":"https://openalex.org/C171146098","wikidata":"https://www.wikidata.org/wiki/Q124192","display_name":"Automotive engineering","level":1,"score":0.3218587040901184},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"score":0.08325329422950745},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tie.2025.3561773","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tie.2025.3561773","pdf_url":null,"source":{"id":"https://openalex.org/S58031724","display_name":"IEEE Transactions on Industrial Electronics","issn_l":"0278-0046","issn":["0278-0046","1557-9948"],"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 Industrial Electronics","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4306904969","https://openalex.org/W2138720691","https://openalex.org/W4362501864","https://openalex.org/W4380318855","https://openalex.org/W3084456289","https://openalex.org/W2024136090","https://openalex.org/W4391331176","https://openalex.org/W2031695474","https://openalex.org/W2586732548","https://openalex.org/W2964765435"],"abstract_inverted_index":{"To":[0],"ensure":[1],"both":[2],"safety":[3],"and":[4,154,182,188],"economic":[5],"feasibility":[6],"in":[7,17,106,163],"the":[8,12,59,63,72,79,90,107,112,120,124,150,156,164,179,186,191],"course":[9],"of":[10,14,61,69,115,190],"airworthiness,":[11],"problem":[13,39,65,81,126],"power":[15],"scheduling":[16],"more":[18],"electric":[19],"aircraft":[20],"(MEA)":[21],"is":[22,75,101],"conventionally":[23],"represented":[24],"as":[25,82],"a":[26,47,83,131,141,174],"mixed-integer":[27],"quadratic":[28],"programming":[29],"(MIQP)":[30],"problem.":[31],"However,":[32],"using":[33,140],"typical":[34],"solvers":[35,152],"to":[36,77,103,130],"solve":[37],"this":[38],"could":[40],"be":[41,128,137],"time":[42],"consuming.":[43],"This":[44],"article":[45],"proposes":[46],"two-stage":[48],"deep":[49,93],"reinforcement":[50],"learning":[51],"(DRL)-based":[52],"hybrid":[53],"optimization":[54],"algorithm":[55,100],"that":[56],"significantly":[57],"enhances":[58],"speed":[60,166],"solving":[62],"MIQP":[64,80,125],"by":[66],"an":[67,160],"order":[68],"magnitude.":[70],"Specifically,":[71],"DRL":[73],"technique":[74],"employed":[76],"formulate":[78],"model-free":[84],"Markov":[85],"decision":[86],"process":[87],"(MDP).":[88],"Furthermore,":[89],"dueling":[91],"double":[92],"Q-network":[94],"with":[95,144,149,167],"priority":[96],"experience":[97],"replay":[98],"(PER-D3QN)":[99],"utilized":[102],"train":[104],"agents":[105],"first":[108],"stage,":[109],"thus":[110],"facilitating":[111],"efficient":[113],"exploration":[114],"optimal":[116,176],"integer":[117,122],"variables.":[118],"Using":[119],"obtained":[121],"variables,":[123],"can":[127,136],"reduced":[129],"standard":[132],"QP":[133],"problem,":[134],"which":[135],"solved":[138],"quickly":[139],"solver":[142],"equipped":[143],"numerical":[145,180],"optimizing":[146],"algorithms.":[147],"Compared":[148],"commercial":[151],"Gurobi":[153],"PER-D3QN,":[155],"proposed":[157,192],"method":[158],"achieves":[159],"order-of-magnitude":[161],"speedup":[162],"solution":[165],"lower":[168],"training":[169],"costs":[170],"while":[171],"still":[172],"guaranteeing":[173],"globally":[175],"solution.":[177],"Finally,":[178],"simulations":[181],"hardware-in-the-loop":[183],"experiment":[184],"confirm":[185],"effectiveness":[187],"superiority":[189],"method.":[193]},"counts_by_year":[],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}