{"id":"https://openalex.org/W7140473731","doi":"https://doi.org/10.48550/arxiv.2603.24475","title":"Conformalized Transfer Learning for Li-ion Battery State of Health Forecasting under Manufacturing and Usage Variability","display_name":"Conformalized Transfer Learning for Li-ion Battery State of Health Forecasting under Manufacturing and Usage Variability","publication_year":2026,"publication_date":"2026-03-25","ids":{"openalex":"https://openalex.org/W7140473731","doi":"https://doi.org/10.48550/arxiv.2603.24475"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.24475","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.24475","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2603.24475","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5083838421","display_name":"Samuel Filgueira da Silva","orcid":"https://orcid.org/0000-0001-7768-0161"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"da Silva, Samuel Filgueira","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5071842614","display_name":"Mehmet Fatih Ozkan","orcid":"https://orcid.org/0000-0002-6506-553X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ozkan, Mehmet Fatih","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5056201336","display_name":"Faissal El Idrissi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Idrissi, Faissal El","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5075875954","display_name":"Marcello Canova","orcid":"https://orcid.org/0000-0003-1846-8894"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Canova, Marcello","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5083838421"],"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":0.9923999905586243,"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":0.9923999905586243,"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/T10220","display_name":"Machine Fault Diagnosis Techniques","score":0.0019000000320374966,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems 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":0.0006000000284984708,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/transfer-of-learning","display_name":"Transfer of learning","score":0.6407999992370605},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.6197999715805054},{"id":"https://openalex.org/keywords/battery","display_name":"Battery (electricity)","score":0.6116999983787537},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.5293999910354614},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4749999940395355},{"id":"https://openalex.org/keywords/concept-drift","display_name":"Concept drift","score":0.4336000084877014},{"id":"https://openalex.org/keywords/transfer","display_name":"Transfer (computing)","score":0.3953999876976013},{"id":"https://openalex.org/keywords/feature-engineering","display_name":"Feature engineering","score":0.3849000036716461},{"id":"https://openalex.org/keywords/state","display_name":"State (computer science)","score":0.3822999894618988}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6507999897003174},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.6407999992370605},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.6197999715805054},{"id":"https://openalex.org/C555008776","wikidata":"https://www.wikidata.org/wiki/Q267298","display_name":"Battery (electricity)","level":3,"score":0.6116999983787537},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5364000201225281},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.5293999910354614},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4997999966144562},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4749999940395355},{"id":"https://openalex.org/C60777511","wikidata":"https://www.wikidata.org/wiki/Q3045002","display_name":"Concept drift","level":3,"score":0.4336000084877014},{"id":"https://openalex.org/C2776175482","wikidata":"https://www.wikidata.org/wiki/Q1195816","display_name":"Transfer (computing)","level":2,"score":0.3953999876976013},{"id":"https://openalex.org/C2778827112","wikidata":"https://www.wikidata.org/wiki/Q22245680","display_name":"Feature engineering","level":3,"score":0.3849000036716461},{"id":"https://openalex.org/C48103436","wikidata":"https://www.wikidata.org/wiki/Q599031","display_name":"State (computer science)","level":2,"score":0.3822999894618988},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.38179999589920044},{"id":"https://openalex.org/C28006648","wikidata":"https://www.wikidata.org/wiki/Q6934509","display_name":"Multi-task learning","level":3,"score":0.36970001459121704},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.3490000069141388},{"id":"https://openalex.org/C200601418","wikidata":"https://www.wikidata.org/wiki/Q2193887","display_name":"Reliability engineering","level":1,"score":0.3483000099658966},{"id":"https://openalex.org/C153701036","wikidata":"https://www.wikidata.org/wiki/Q659974","display_name":"Trustworthiness","level":2,"score":0.3264999985694885},{"id":"https://openalex.org/C2989104859","wikidata":"https://www.wikidata.org/wiki/Q267298","display_name":"Battery capacity","level":4,"score":0.322299987077713},{"id":"https://openalex.org/C122282355","wikidata":"https://www.wikidata.org/wiki/Q7246855","display_name":"Probabilistic forecasting","level":3,"score":0.3215999901294708},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.29899999499320984},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.2888000011444092},{"id":"https://openalex.org/C77075516","wikidata":"https://www.wikidata.org/wiki/Q6027324","display_name":"Inductive transfer","level":5,"score":0.2761000096797943},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2759999930858612},{"id":"https://openalex.org/C149441793","wikidata":"https://www.wikidata.org/wiki/Q200726","display_name":"Probability distribution","level":2,"score":0.267300009727478},{"id":"https://openalex.org/C2777294910","wikidata":"https://www.wikidata.org/wiki/Q4050070","display_name":"State of health","level":4,"score":0.25760000944137573},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.2563000023365021}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.24475","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.24475","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.24475","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.24475","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","score":0.42292410135269165,"display_name":"Industry, innovation and infrastructure"}],"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],"forecasting":[1],"of":[2,13,128],"state-of-health":[3],"(SOH)":[4],"is":[5,54,81],"essential":[6],"for":[7],"ensuring":[8],"safe":[9],"and":[10,71,96,106,126],"reliable":[11],"operation":[12],"lithium-ion":[14],"cells.":[15,133],"However,":[16],"existing":[17],"models":[18],"calibrated":[19],"on":[20,83],"laboratory":[21],"tests":[22],"at":[23],"specific":[24],"conditions":[25],"often":[26],"fail":[27],"to":[28,30,36,89,109],"generalize":[29],"new":[31],"cells":[32],"that":[33],"differ":[34],"due":[35],"small":[37],"manufacturing":[38,95],"variations":[39],"or":[40],"operate":[41],"under":[42],"different":[43],"conditions.":[44,98],"To":[45],"address":[46],"this":[47],"challenge,":[48],"an":[49],"uncertainty-aware":[50],"transfer":[51],"learning":[52],"framework":[53,121],"proposed,":[55],"combining":[56],"a":[57,84],"Long":[58],"Short-Term":[59],"Memory":[60],"(LSTM)":[61],"model":[62,80],"with":[63],"domain":[64,111],"adaptation":[65],"via":[66],"Maximum":[67],"Mean":[68],"Discrepancy":[69],"(MMD)":[70],"uncertainty":[72],"quantification":[73],"through":[74],"Conformal":[75],"Prediction":[76],"(CP).":[77],"The":[78],"LSTM":[79],"trained":[82],"virtual":[85],"battery":[86],"dataset":[87],"designed":[88],"capture":[90],"real-world":[91],"variability":[92],"in":[93],"electrode":[94],"operating":[97],"MMD":[99],"aligns":[100],"latent":[101],"feature":[102],"distributions":[103],"between":[104],"simulated":[105],"target":[107],"domains":[108],"mitigate":[110],"shift,":[112],"while":[113],"CP":[114],"provides":[115],"calibrated,":[116],"distribution-free":[117],"prediction":[118],"intervals.":[119],"This":[120],"improves":[122],"both":[123],"the":[124],"generalization":[125],"trustworthiness":[127],"SOH":[129],"forecasts":[130],"across":[131],"heterogeneous":[132]},"counts_by_year":[],"updated_date":"2026-05-01T08:36:08.643496","created_date":"2026-03-27T00:00:00"}
