{"id":"https://openalex.org/W7161147524","doi":"https://doi.org/10.48550/arxiv.2605.13200","title":"A Hybrid Tucker-LSTM Tensor Network Model for SOC Prediction in Electric Vehicles","display_name":"A Hybrid Tucker-LSTM Tensor Network Model for SOC Prediction in Electric Vehicles","publication_year":2026,"publication_date":"2026-05-13","ids":{"openalex":"https://openalex.org/W7161147524","doi":"https://doi.org/10.48550/arxiv.2605.13200"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.13200","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.13200","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2605.13200","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5136165370","display_name":"Han Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Han","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136146341","display_name":"Ying Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Ying","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5136134017","display_name":"Bing Hui Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Bing","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10663","display_name":"Advanced Battery Technologies Research","score":0.8891000151634216,"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":0.8891000151634216,"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":0.0210999995470047,"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/T12303","display_name":"Tensor decomposition and applications","score":0.013899999670684338,"subfield":{"id":"https://openalex.org/subfields/2605","display_name":"Computational Mathematics"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.6923999786376953},{"id":"https://openalex.org/keywords/battery","display_name":"Battery (electricity)","score":0.6614000201225281},{"id":"https://openalex.org/keywords/state-of-charge","display_name":"State of charge","score":0.6327000260353088},{"id":"https://openalex.org/keywords/tensor-decomposition","display_name":"Tensor decomposition","score":0.5843999981880188},{"id":"https://openalex.org/keywords/tensor","display_name":"Tensor (intrinsic definition)","score":0.5656999945640564},{"id":"https://openalex.org/keywords/curse-of-dimensionality","display_name":"Curse of dimensionality","score":0.5587000250816345},{"id":"https://openalex.org/keywords/decomposition","display_name":"Decomposition","score":0.5569999814033508},{"id":"https://openalex.org/keywords/tucker-decomposition","display_name":"Tucker decomposition","score":0.49380001425743103}],"concepts":[{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.6923999786376953},{"id":"https://openalex.org/C555008776","wikidata":"https://www.wikidata.org/wiki/Q267298","display_name":"Battery (electricity)","level":3,"score":0.6614000201225281},{"id":"https://openalex.org/C2776582896","wikidata":"https://www.wikidata.org/wiki/Q5368536","display_name":"State of charge","level":4,"score":0.6327000260353088},{"id":"https://openalex.org/C2986737658","wikidata":"https://www.wikidata.org/wiki/Q30103009","display_name":"Tensor decomposition","level":3,"score":0.5843999981880188},{"id":"https://openalex.org/C155281189","wikidata":"https://www.wikidata.org/wiki/Q3518150","display_name":"Tensor (intrinsic definition)","level":2,"score":0.5656999945640564},{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.5587000250816345},{"id":"https://openalex.org/C124681953","wikidata":"https://www.wikidata.org/wiki/Q339062","display_name":"Decomposition","level":2,"score":0.5569999814033508},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5521000027656555},{"id":"https://openalex.org/C42704193","wikidata":"https://www.wikidata.org/wiki/Q7851097","display_name":"Tucker decomposition","level":4,"score":0.49380001425743103},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.41690000891685486},{"id":"https://openalex.org/C2776422217","wikidata":"https://www.wikidata.org/wiki/Q13629441","display_name":"Electric vehicle","level":3,"score":0.41179999709129333},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.36309999227523804},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.3366999924182892},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.31290000677108765},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.3125},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.30329999327659607},{"id":"https://openalex.org/C47446073","wikidata":"https://www.wikidata.org/wiki/Q5165890","display_name":"Control theory (sociology)","level":3,"score":0.2888999879360199},{"id":"https://openalex.org/C48103436","wikidata":"https://www.wikidata.org/wiki/Q599031","display_name":"State (computer science)","level":2,"score":0.28029999136924744},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.2793000042438507},{"id":"https://openalex.org/C42355184","wikidata":"https://www.wikidata.org/wiki/Q1361088","display_name":"Matrix decomposition","level":3,"score":0.2750999927520752},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.26420000195503235},{"id":"https://openalex.org/C44154836","wikidata":"https://www.wikidata.org/wiki/Q45045","display_name":"Simulation","level":1,"score":0.2637999951839447},{"id":"https://openalex.org/C167928553","wikidata":"https://www.wikidata.org/wiki/Q1376021","display_name":"Estimation theory","level":2,"score":0.25760000944137573}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.13200","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.13200","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.13200","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.13200","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/7","display_name":"Affordable and clean energy","score":0.4533229470252991}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Accurate":[0],"state":[1],"of":[2,10,167],"charge":[3,76],"estimation":[4],"is":[5,18,88,110],"critical":[6],"for":[7,70,178],"the":[8,96,116,151],"success":[9],"electric":[11,182],"vehicle":[12,183],"battery":[13,39,161,184],"management":[14],"strategies,":[15],"but":[16],"it":[17],"well":[19,164],"known":[20],"that":[21,31,41,156],"conventional":[22],"estimators":[23],"suffer":[24],"from":[25,139,146],"two":[26],"fundamental":[27],"shortcomings:":[28],"cumulative":[29],"errors":[30],"grow":[32],"over":[33],"time":[34],"and":[35,83,143],"reliance":[36],"on":[37,118],"simplified":[38],"models":[40],"do":[42],"not":[43],"reflect":[44],"real":[45],"world":[46],"dynamics.":[47],"Therefore,":[48],"this":[49,170],"paper":[50,171],"presents":[51],"a":[52,101,175],"novel":[53],"hybrid":[54],"approach":[55],"combining":[56],"Tucker":[57,86,112],"tensor":[58,157],"decomposition":[59,87,158],"with":[60,105,121],"LSTM":[61,114],"networks,":[62],"using":[63],"full":[64],"-":[65,113],"lifecycle":[66],"EV":[67],"field":[68],"data":[69,162],"SOC":[71],"prediction.":[72],"The":[73,108],"inputs":[74],"are":[75],"status,":[77],"mileage,":[78],"voltage,":[79],"current,":[80],"cell":[81],"differentials,":[82],"temporal":[84,97],"features.":[85],"skillfully":[89],"used":[90],"to":[91,127,135,141,148],"reduce":[92],"dimensionality":[93],"while":[94],"maintaining":[95],"structure,":[98],"hence":[99],"allowing":[100],"direct,":[102],"fair":[103],"comparison":[104],"standard":[106],"LSTM.":[107],"result":[109],"unequivocal:":[111],"outperforms":[115],"baseline":[117],"all":[119],"metrics,":[120],"MSE":[122],"dropping":[123],"70.5\\%":[124],"(from":[125,133],"21.07":[126],"6.22":[128],"),":[129],"MAE":[130],"improving":[131],"48.7\\%":[132],"3.37\\%":[134],"1.73\\%),":[136],"RMSE":[137],"falling":[138],"4.59\\%":[140],"2.49\\%,":[142],"$R^2$":[144],"rising":[145],"0.918":[147],"0.976.":[149],"Since":[150],"experimental":[152],"results":[153],"demonstrably":[154],"demonstrate":[155],"compresses":[159],"high-dimensional":[160],"very":[163],"without":[165],"loss":[166],"predictive":[168],"fidelity,":[169],"naturally":[172],"opens":[173],"up":[174],"new":[176],"direction":[177],"tensor-based":[179],"analytics":[180],"in":[181],"management.":[185]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-15T00:00:00"}