{"id":"https://openalex.org/W3210265823","doi":"https://doi.org/10.1109/isse51541.2021.9582501","title":"Multiobjective Design Optimization of Hybrid Electric Rail Vehicle Powertrains","display_name":"Multiobjective Design Optimization of Hybrid Electric Rail Vehicle Powertrains","publication_year":2021,"publication_date":"2021-09-13","ids":{"openalex":"https://openalex.org/W3210265823","doi":"https://doi.org/10.1109/isse51541.2021.9582501","mag":"3210265823"},"language":"en","primary_location":{"id":"doi:10.1109/isse51541.2021.9582501","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isse51541.2021.9582501","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Symposium on Systems Engineering (ISSE)","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/A5091145366","display_name":"Herbert Palm","orcid":"https://orcid.org/0000-0003-4721-1414"},"institutions":[{"id":"https://openalex.org/I174004417","display_name":"Munich University of Applied Sciences","ror":"https://ror.org/012k1v959","country_code":"DE","type":"education","lineage":["https://openalex.org/I174004417"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Herbert Palm","raw_affiliation_strings":["University of Applied Sciences Munich Systems Engineering, Munich, Germany"],"affiliations":[{"raw_affiliation_string":"University of Applied Sciences Munich Systems Engineering, Munich, Germany","institution_ids":["https://openalex.org/I174004417"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5056138288","display_name":"Fabian Rang","orcid":null},"institutions":[{"id":"https://openalex.org/I174004417","display_name":"Munich University of Applied Sciences","ror":"https://ror.org/012k1v959","country_code":"DE","type":"education","lineage":["https://openalex.org/I174004417"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Fabian Rang","raw_affiliation_strings":["University of Applied Sciences Munich Systems Engineering, Munich, Germany"],"affiliations":[{"raw_affiliation_string":"University of Applied Sciences Munich Systems Engineering, Munich, Germany","institution_ids":["https://openalex.org/I174004417"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5071394525","display_name":"Florian Mueller","orcid":"https://orcid.org/0000-0001-6472-3476"},"institutions":[{"id":"https://openalex.org/I62916508","display_name":"Technical University of Munich","ror":"https://ror.org/02kkvpp62","country_code":"DE","type":"education","lineage":["https://openalex.org/I62916508"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Florian Mueller","raw_affiliation_strings":["Technical University of Munich, Munich, Germany"],"affiliations":[{"raw_affiliation_string":"Technical University of Munich, Munich, Germany","institution_ids":["https://openalex.org/I62916508"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5087533448","display_name":"Markus Guerster","orcid":"https://orcid.org/0000-0001-5549-8481"},"institutions":[{"id":"https://openalex.org/I63966007","display_name":"Massachusetts Institute of Technology","ror":"https://ror.org/042nb2s44","country_code":"US","type":"education","lineage":["https://openalex.org/I63966007"]},{"id":"https://openalex.org/I4210116219","display_name":"Engineering Systems (United States)","ror":"https://ror.org/02qg60849","country_code":"US","type":"company","lineage":["https://openalex.org/I4210116219"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Markus Guerster","raw_affiliation_strings":["Massachusetts Institute of Technology Engineering Systems Laboratory Cambridge, MA, USA"],"affiliations":[{"raw_affiliation_string":"Massachusetts Institute of Technology Engineering Systems Laboratory Cambridge, MA, USA","institution_ids":["https://openalex.org/I63966007","https://openalex.org/I4210116219"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5091145366"],"corresponding_institution_ids":["https://openalex.org/I174004417"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.19727114,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"2","issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10848","display_name":"Advanced Multi-Objective Optimization Algorithms","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10848","display_name":"Advanced Multi-Objective Optimization Algorithms","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12095","display_name":"Vehicle emissions and performance","score":0.9973000288009644,"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/T14156","display_name":"Engineering Applied Research","score":0.9900000095367432,"subfield":{"id":"https://openalex.org/subfields/2205","display_name":"Civil and Structural 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/powertrain","display_name":"Powertrain","score":0.8825670480728149},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5804368257522583},{"id":"https://openalex.org/keywords/pareto-principle","display_name":"Pareto principle","score":0.4775836765766144},{"id":"https://openalex.org/keywords/genetic-algorithm","display_name":"Genetic algorithm","score":0.4756588339805603},{"id":"https://openalex.org/keywords/toolbox","display_name":"Toolbox","score":0.46280473470687866},{"id":"https://openalex.org/keywords/multi-objective-optimization","display_name":"Multi-objective optimization","score":0.45290806889533997},{"id":"https://openalex.org/keywords/electric-vehicle","display_name":"Electric vehicle","score":0.4240075945854187},{"id":"https://openalex.org/keywords/automotive-engineering","display_name":"Automotive engineering","score":0.38926810026168823},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.2793771028518677},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.13421261310577393},{"id":"https://openalex.org/keywords/power","display_name":"Power (physics)","score":0.08798697590827942}],"concepts":[{"id":"https://openalex.org/C76047896","wikidata":"https://www.wikidata.org/wiki/Q1786258","display_name":"Powertrain","level":3,"score":0.8825670480728149},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5804368257522583},{"id":"https://openalex.org/C137635306","wikidata":"https://www.wikidata.org/wiki/Q182667","display_name":"Pareto principle","level":2,"score":0.4775836765766144},{"id":"https://openalex.org/C8880873","wikidata":"https://www.wikidata.org/wiki/Q187787","display_name":"Genetic algorithm","level":2,"score":0.4756588339805603},{"id":"https://openalex.org/C2777655017","wikidata":"https://www.wikidata.org/wiki/Q1501161","display_name":"Toolbox","level":2,"score":0.46280473470687866},{"id":"https://openalex.org/C68781425","wikidata":"https://www.wikidata.org/wiki/Q2052203","display_name":"Multi-objective optimization","level":2,"score":0.45290806889533997},{"id":"https://openalex.org/C2776422217","wikidata":"https://www.wikidata.org/wiki/Q13629441","display_name":"Electric vehicle","level":3,"score":0.4240075945854187},{"id":"https://openalex.org/C171146098","wikidata":"https://www.wikidata.org/wiki/Q124192","display_name":"Automotive engineering","level":1,"score":0.38926810026168823},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.2793771028518677},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.13421261310577393},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.08798697590827942},{"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/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"score":0.0},{"id":"https://openalex.org/C97355855","wikidata":"https://www.wikidata.org/wiki/Q11473","display_name":"Thermodynamics","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","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/C144171764","wikidata":"https://www.wikidata.org/wiki/Q48103","display_name":"Torque","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/isse51541.2021.9582501","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isse51541.2021.9582501","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Symposium on Systems Engineering (ISSE)","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":45,"referenced_works":["https://openalex.org/W62429925","https://openalex.org/W1518025915","https://openalex.org/W1563612452","https://openalex.org/W1585939719","https://openalex.org/W1746819321","https://openalex.org/W1966864823","https://openalex.org/W1989750313","https://openalex.org/W1992176375","https://openalex.org/W2031946900","https://openalex.org/W2034374737","https://openalex.org/W2065273135","https://openalex.org/W2087817842","https://openalex.org/W2091934641","https://openalex.org/W2100830227","https://openalex.org/W2102201073","https://openalex.org/W2113233198","https://openalex.org/W2116661285","https://openalex.org/W2117502083","https://openalex.org/W2125899728","https://openalex.org/W2126105956","https://openalex.org/W2140066605","https://openalex.org/W2158078248","https://openalex.org/W2198098822","https://openalex.org/W2211280768","https://openalex.org/W2286250368","https://openalex.org/W2396034031","https://openalex.org/W2740683467","https://openalex.org/W2753011923","https://openalex.org/W2802620382","https://openalex.org/W2803389929","https://openalex.org/W2919571621","https://openalex.org/W2947970253","https://openalex.org/W2963958779","https://openalex.org/W2971407917","https://openalex.org/W3030878622","https://openalex.org/W4211049957","https://openalex.org/W4249796148","https://openalex.org/W4250845939","https://openalex.org/W4255916097","https://openalex.org/W4388249735","https://openalex.org/W6629804754","https://openalex.org/W6635097213","https://openalex.org/W6688109388","https://openalex.org/W6767470347","https://openalex.org/W7052850726"],"related_works":["https://openalex.org/W2109769812","https://openalex.org/W2164776125","https://openalex.org/W2953137217","https://openalex.org/W2120892143","https://openalex.org/W2136582587","https://openalex.org/W2137409234","https://openalex.org/W2063991943","https://openalex.org/W2162365590","https://openalex.org/W2901375575","https://openalex.org/W99982270"],"abstract_inverted_index":{"Technologies":[0],"for":[1,22,122,188],"rail":[2,197],"vehicle":[3,198],"powertrain":[4,43,75,199],"systems":[5],"are":[6,105,128,146],"in":[7],"a":[8,155],"phase":[9],"of":[10,57,91,132,137,143,163,172,195,206],"disruptive":[11],"transition.":[12],"While":[13],"existing":[14],"electrification":[15],"approaches":[16],"fail":[17],"to":[18,31,34,48,87,183],"meet":[19],"cost":[20],"targets":[21],"many":[23],"application":[24],"scenarios,":[25],"established":[26],"cost-efficient":[27],"diesel":[28],"vehicles":[29],"start":[30],"fall":[32],"victim":[33],"CO":[35],"<inf":[36],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[37],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">2</inf>":[38],"regulations.":[39],"Innovative":[40],"hybrid":[41,93],"electric":[42],"technologies":[44,200],"have":[45],"the":[46,69,81,89,120,160,193],"potential":[47,90],"stop":[49],"this":[50,61,173],"predicament":[51],"but":[52],"suffer":[53],"from":[54],"low":[55],"degrees":[56],"engineering":[58],"experience.":[59],"Within":[60],"VUCA":[62],"(volatile,":[63],"uncertain,":[64],"complex,":[65],"and":[66,159,181,190],"ambiguous)":[67],"environment,":[68],"paper":[70],"on":[71,113],"hand":[72],"reduces":[73],"railway":[74,176],"decision":[76,207],"making":[77],"risks":[78],"by":[79,107],"applying":[80,108],"Hyper":[82],"Space":[83],"Exploration":[84],"(HSE)":[85],"approach":[86],"quantity":[88],"innovative":[92,196],"diesel-electric":[94],"alternatives":[95],"during":[96],"design":[97],"phase.":[98],"Thereby":[99],"stated":[100],"multicriterial":[101],"Pareto-optimal":[102],"target":[103],"trade-offs":[104],"identified":[106],"multiobjective":[109],"optimization":[110,162],"(MOO)":[111],"algorithms":[112,135,145,187],"virtual":[114],"prototypes.":[115],"As":[116],"underlying":[117],"simulations":[118],"with":[119],"Toolbox":[121],"Optimal":[123],"Railway":[124],"Propulsion":[125],"Architectures":[126],"(TORPA)":[127],"computationally":[129],"expensive,":[130],"efficiency":[131],"MOO":[133],"search":[134,144,186],"is":[136],"crucial":[138],"importance.":[139],"Accordingly,":[140],"three":[141],"types":[142],"compared":[147],"concerning":[148],"their":[149,204],"performance:":[150],"Latin":[151],"Hypercube":[152],"Sampling":[153],"(LHS),":[154],"Genetic":[156],"Algorithm":[157],"(NSGA2),":[158],"Surrogate":[161],"Computationally":[164],"Expensive":[165],"Multiobjective":[166],"Problems":[167],"(SOCEMO)":[168],"approach.":[169],"The":[170],"outcome":[171],"work":[174],"enables":[175],"product":[177],"managers,":[178],"system":[179],"architects,":[180],"operators":[182],"choose":[184],"appropriate":[185],"effectively":[189],"efficiently":[191],"quantifying":[192],"potentials":[194],"and,":[201],"thereby,":[202],"reduce":[203],"risk":[205],"making.":[208]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}