{"id":"https://openalex.org/W2744612526","doi":"https://doi.org/10.1109/icphm.2017.7998338","title":"Research on electric vehicle (EV) driving range prediction method based on PSO-LSSVM","display_name":"Research on electric vehicle (EV) driving range prediction method based on PSO-LSSVM","publication_year":2017,"publication_date":"2017-06-01","ids":{"openalex":"https://openalex.org/W2744612526","doi":"https://doi.org/10.1109/icphm.2017.7998338","mag":"2744612526"},"language":"en","primary_location":{"id":"doi:10.1109/icphm.2017.7998338","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icphm.2017.7998338","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Conference on Prognostics and Health Management (ICPHM)","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/A5029221427","display_name":"Zhuo Wang","orcid":"https://orcid.org/0000-0002-2735-6969"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhuo Wang","raw_affiliation_strings":["School of Reliability and System Engineering, Beihang University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Reliability and System Engineering, Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100455445","display_name":"Xiaohong Wang","orcid":"https://orcid.org/0000-0002-1540-6674"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiao-Hong Wang","raw_affiliation_strings":["School of Reliability and System Engineering, Beihang University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Reliability and System Engineering, Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100666777","display_name":"Lizhi Wang","orcid":"https://orcid.org/0000-0001-6663-0265"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Li-Zhi Wang","raw_affiliation_strings":["School of Reliability and System Engineering, Beihang University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Reliability and System Engineering, Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5047958834","display_name":"Xiao-Fen Hu","orcid":null},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiao-Fen Hu","raw_affiliation_strings":["School of Reliability and System Engineering, Beihang University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Reliability and System Engineering, Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5110471584","display_name":"Wenhui Fan","orcid":null},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wen-Hui Fan","raw_affiliation_strings":["School of Reliability and System Engineering, Beihang University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Reliability and System Engineering, Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I82880672"],"apc_list":null,"apc_paid":null,"fwci":0.6065,"has_fulltext":false,"cited_by_count":17,"citation_normalized_percentile":{"value":0.59946994,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"260","last_page":"265"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10663","display_name":"Advanced Battery Technologies Research","score":0.9988999962806702,"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":0.9988999962806702,"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":0.9975000023841858,"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":0.9961000084877014,"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/particle-swarm-optimization","display_name":"Particle swarm optimization","score":0.7803086042404175},{"id":"https://openalex.org/keywords/range","display_name":"Range (aeronautics)","score":0.6928271055221558},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.535662055015564},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.5189654231071472},{"id":"https://openalex.org/keywords/reliability","display_name":"Reliability (semiconductor)","score":0.5083362460136414},{"id":"https://openalex.org/keywords/electric-vehicle","display_name":"Electric vehicle","score":0.49207156896591187},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4838792085647583},{"id":"https://openalex.org/keywords/stability","display_name":"Stability (learning theory)","score":0.4515085816383362},{"id":"https://openalex.org/keywords/automotive-engineering","display_name":"Automotive engineering","score":0.34579965472221375},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3375222682952881},{"id":"https://openalex.org/keywords/simulation","display_name":"Simulation","score":0.3247097134590149},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3208456039428711},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.2848949432373047},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.18937578797340393},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.07684525847434998},{"id":"https://openalex.org/keywords/aerospace-engineering","display_name":"Aerospace engineering","score":0.07329240441322327}],"concepts":[{"id":"https://openalex.org/C85617194","wikidata":"https://www.wikidata.org/wiki/Q2072794","display_name":"Particle swarm optimization","level":2,"score":0.7803086042404175},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.6928271055221558},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.535662055015564},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.5189654231071472},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.5083362460136414},{"id":"https://openalex.org/C2776422217","wikidata":"https://www.wikidata.org/wiki/Q13629441","display_name":"Electric vehicle","level":3,"score":0.49207156896591187},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4838792085647583},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.4515085816383362},{"id":"https://openalex.org/C171146098","wikidata":"https://www.wikidata.org/wiki/Q124192","display_name":"Automotive engineering","level":1,"score":0.34579965472221375},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3375222682952881},{"id":"https://openalex.org/C44154836","wikidata":"https://www.wikidata.org/wiki/Q45045","display_name":"Simulation","level":1,"score":0.3247097134590149},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3208456039428711},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.2848949432373047},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.18937578797340393},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.07684525847434998},{"id":"https://openalex.org/C146978453","wikidata":"https://www.wikidata.org/wiki/Q3798668","display_name":"Aerospace engineering","level":1,"score":0.07329240441322327},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","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},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icphm.2017.7998338","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icphm.2017.7998338","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Conference on Prognostics and Health Management (ICPHM)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Affordable and clean energy","id":"https://metadata.un.org/sdg/7","score":0.6100000143051147}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W1458733518","https://openalex.org/W1596717185","https://openalex.org/W1996062600","https://openalex.org/W2027872902","https://openalex.org/W2045317586","https://openalex.org/W2054675725","https://openalex.org/W2057472606","https://openalex.org/W2057814586","https://openalex.org/W2087754676","https://openalex.org/W2106176713","https://openalex.org/W2106488883","https://openalex.org/W2129317318","https://openalex.org/W2150199594","https://openalex.org/W2152195021","https://openalex.org/W2157906137","https://openalex.org/W2253735108","https://openalex.org/W2276751812","https://openalex.org/W2410644873","https://openalex.org/W2413581040","https://openalex.org/W2462241752"],"related_works":["https://openalex.org/W3162204513","https://openalex.org/W2090763504","https://openalex.org/W2371138613","https://openalex.org/W2048963458","https://openalex.org/W43109613","https://openalex.org/W2359952343","https://openalex.org/W148178222","https://openalex.org/W2239445980","https://openalex.org/W2104657898","https://openalex.org/W2381411913"],"abstract_inverted_index":{"Electric":[0],"vehicle":[1],"(EV)":[2],"driving":[3,94,140,163],"range":[4],"directly":[5,72],"reflects":[6],"EVs'":[7,157],"performance,":[8],"safety,":[9],"reliability":[10],"and":[11,31,58,108,114,119,133,154,160],"economy.":[12],"EV":[13,93,139],"has":[14,128],"gained":[15],"wide":[16],"attention":[17],"in":[18],"recent":[19],"years.":[20],"However,":[21],"most":[22],"of":[23,80,83,99,111],"researches":[24],"are":[25,86],"carried":[26],"out":[27],"under":[28],"ideal":[29],"conditions":[30,159],"the":[32,97,126,143,162],"existing":[33],"methods":[34],"have":[35],"numerous":[36],"drawbacks.":[37],"This":[38],"paper":[39],"presents":[40],"a":[41,47,129,148],"novel":[42],"prediction":[43],"method":[44],"based":[45],"on":[46],"least":[48],"squares":[49],"support":[50],"vector":[51],"machine":[52],"(LSSVM)":[53],"model":[54,90,101,127],"with":[55],"parameters":[56,67,106],"\u03b3":[57],"\u03c32optimized":[59],"by":[60,73,104],"particle":[61],"swarm":[62],"optimization":[63],"(PSO).":[64],"The":[65,122],"main":[66],"which":[68],"cannot":[69],"be":[70],"obtained":[71],"drivers":[74,151],"such":[75],"as":[76],"days,":[77],"temperature,":[78],"depth":[79],"discharge":[81],"(DOD)":[82],"battery":[84],"pack":[85],"used":[87],"for":[88,150],"training":[89,112],"to":[91,137,152],"predict":[92,138,161],"range.":[95,141,164],"Furthermore,":[96],"performance":[98,136],"PSO-LSSVM":[100],"is":[102,117],"illustrated":[103],"statistical":[105],"(RE":[107],"AARE).":[109],"AARE":[110],"data":[113,116],"testing":[115],"1.99%":[118],"5.99%":[120],"respectively.":[121],"results":[123,144],"suggest":[124],"that":[125],"stability,":[130],"generalization":[131],"ability":[132],"reliable":[134],"predictive":[135],"Meanwhile,":[142],"can":[145],"also":[146],"provide":[147],"guidance":[149],"grasp":[153],"manage":[155],"their":[156],"health":[158]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":1},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}