{"id":"https://openalex.org/W4281768135","doi":"https://doi.org/10.1145/3529399.3529402","title":"Exploratory study of battery aging analysis with machine learning models to complete multi-physical ones for more adaptable systems","display_name":"Exploratory study of battery aging analysis with machine learning models to complete multi-physical ones for more adaptable systems","publication_year":2022,"publication_date":"2022-03-11","ids":{"openalex":"https://openalex.org/W4281768135","doi":"https://doi.org/10.1145/3529399.3529402"},"language":"en","primary_location":{"id":"doi:10.1145/3529399.3529402","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3529399.3529402","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 7th International Conference on Machine Learning Technologies (ICMLT)","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/A5058311083","display_name":"Leo Challier","orcid":null},"institutions":[{"id":"https://openalex.org/I4210136798","display_name":"Capgemini (France)","ror":"https://ror.org/03qx9tc91","country_code":"FR","type":"company","lineage":["https://openalex.org/I4210136798"]}],"countries":["FR"],"is_corresponding":true,"raw_author_name":"Leo Challier","raw_affiliation_strings":["Research department, Capgemini Engineering, France"],"affiliations":[{"raw_affiliation_string":"Research department, Capgemini Engineering, France","institution_ids":["https://openalex.org/I4210136798"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5041004197","display_name":"Genevieve Ndour","orcid":null},"institutions":[{"id":"https://openalex.org/I4210136798","display_name":"Capgemini (France)","ror":"https://ror.org/03qx9tc91","country_code":"FR","type":"company","lineage":["https://openalex.org/I4210136798"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Genevieve Ndour","raw_affiliation_strings":["Research department, Capgemini Engineering, France"],"affiliations":[{"raw_affiliation_string":"Research department, Capgemini Engineering, France","institution_ids":["https://openalex.org/I4210136798"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5078108095","display_name":"Martin Garrigos","orcid":null},"institutions":[{"id":"https://openalex.org/I4210136798","display_name":"Capgemini (France)","ror":"https://ror.org/03qx9tc91","country_code":"FR","type":"company","lineage":["https://openalex.org/I4210136798"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Martin Garrigos","raw_affiliation_strings":["Research department, Capgemini Engineering, France"],"affiliations":[{"raw_affiliation_string":"Research department, Capgemini Engineering, France","institution_ids":["https://openalex.org/I4210136798"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5047542801","display_name":"Karim Chouchane","orcid":"https://orcid.org/0000-0002-2349-255X"},"institutions":[{"id":"https://openalex.org/I4210136798","display_name":"Capgemini (France)","ror":"https://ror.org/03qx9tc91","country_code":"FR","type":"company","lineage":["https://openalex.org/I4210136798"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Karim Chouchane","raw_affiliation_strings":["Research department, Capgemini Engineering, France"],"affiliations":[{"raw_affiliation_string":"Research department, Capgemini Engineering, France","institution_ids":["https://openalex.org/I4210136798"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5027777463","display_name":"Cl\u00e9ment Conand","orcid":null},"institutions":[{"id":"https://openalex.org/I4210136798","display_name":"Capgemini (France)","ror":"https://ror.org/03qx9tc91","country_code":"FR","type":"company","lineage":["https://openalex.org/I4210136798"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Clement Conand","raw_affiliation_strings":["Research department, Capgemini Engineering, France"],"affiliations":[{"raw_affiliation_string":"Research department, Capgemini Engineering, France","institution_ids":["https://openalex.org/I4210136798"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5026498023","display_name":"Valerie Lavaste","orcid":null},"institutions":[{"id":"https://openalex.org/I4210136798","display_name":"Capgemini (France)","ror":"https://ror.org/03qx9tc91","country_code":"FR","type":"company","lineage":["https://openalex.org/I4210136798"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Valerie Lavaste","raw_affiliation_strings":["Research department, Capgemini Engineering, France"],"affiliations":[{"raw_affiliation_string":"Research department, Capgemini Engineering, France","institution_ids":["https://openalex.org/I4210136798"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5011171950","display_name":"Charlotte Alliod","orcid":null},"institutions":[{"id":"https://openalex.org/I4210136798","display_name":"Capgemini (France)","ror":"https://ror.org/03qx9tc91","country_code":"FR","type":"company","lineage":["https://openalex.org/I4210136798"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Charlotte Alliod","raw_affiliation_strings":["Research department, Capgemini Engineering, France"],"affiliations":[{"raw_affiliation_string":"Research department, Capgemini Engineering, France","institution_ids":["https://openalex.org/I4210136798"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5058311083"],"corresponding_institution_ids":["https://openalex.org/I4210136798"],"apc_list":null,"apc_paid":null,"fwci":0.1567,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.42873465,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":96},"biblio":{"volume":"37","issue":null,"first_page":"12","last_page":"16"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10663","display_name":"Advanced Battery Technologies Research","score":1.0,"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":1.0,"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/T10780","display_name":"Reliability and Maintenance Optimization","score":0.9944000244140625,"subfield":{"id":"https://openalex.org/subfields/2213","display_name":"Safety, Risk, Reliability and Quality"},"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/T10876","display_name":"Fault Detection and Control Systems","score":0.9933000206947327,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6526477336883545},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.6511135101318359},{"id":"https://openalex.org/keywords/battery","display_name":"Battery (electricity)","score":0.581924319267273},{"id":"https://openalex.org/keywords/state-of-health","display_name":"State of health","score":0.5764877200126648},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.45842233300209045},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.41789355874061584},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4154568910598755},{"id":"https://openalex.org/keywords/reliability-engineering","display_name":"Reliability engineering","score":0.32925254106521606},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.2258165180683136},{"id":"https://openalex.org/keywords/power","display_name":"Power (physics)","score":0.1539228856563568}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6526477336883545},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.6511135101318359},{"id":"https://openalex.org/C555008776","wikidata":"https://www.wikidata.org/wiki/Q267298","display_name":"Battery (electricity)","level":3,"score":0.581924319267273},{"id":"https://openalex.org/C2777294910","wikidata":"https://www.wikidata.org/wiki/Q4050070","display_name":"State of health","level":4,"score":0.5764877200126648},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.45842233300209045},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.41789355874061584},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4154568910598755},{"id":"https://openalex.org/C200601418","wikidata":"https://www.wikidata.org/wiki/Q2193887","display_name":"Reliability engineering","level":1,"score":0.32925254106521606},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.2258165180683136},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.1539228856563568},{"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/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3529399.3529402","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3529399.3529402","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 7th International Conference on Machine Learning Technologies (ICMLT)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/7","score":0.8799999952316284,"display_name":"Affordable and clean energy"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320310998","display_name":"Capgemini","ror":"https://ror.org/03qx9tc91"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W1015042224","https://openalex.org/W1670175697","https://openalex.org/W1981460882","https://openalex.org/W2038012452","https://openalex.org/W2055873761","https://openalex.org/W2064175677","https://openalex.org/W2071280205","https://openalex.org/W2130424487","https://openalex.org/W2337713278","https://openalex.org/W2345979296","https://openalex.org/W2551714397","https://openalex.org/W2597586889","https://openalex.org/W2599868532","https://openalex.org/W2755467298","https://openalex.org/W2759525351","https://openalex.org/W2781564233","https://openalex.org/W2781930323","https://openalex.org/W2791494814","https://openalex.org/W2889171365","https://openalex.org/W2895466770","https://openalex.org/W2903220204","https://openalex.org/W2912511381","https://openalex.org/W3006296580","https://openalex.org/W3007217854","https://openalex.org/W3009652674","https://openalex.org/W3092917246","https://openalex.org/W3156011754"],"related_works":["https://openalex.org/W2991588090","https://openalex.org/W3213052590","https://openalex.org/W2995074223","https://openalex.org/W176011693","https://openalex.org/W2068485218","https://openalex.org/W4376058722","https://openalex.org/W3034877844","https://openalex.org/W4225996487","https://openalex.org/W4200404188","https://openalex.org/W3091635603"],"abstract_inverted_index":{"Electric":[0],"vehicles":[1],"represent":[2],"one":[3],"of":[4,10,61,81,149,163,189],"the":[5,14,58,92,155,182],"most":[6,80],"future":[7],"proof":[8],"means":[9],"transport":[11],"to":[12,35,39,47,56,64,99,119,128,186],"face":[13],"energy":[15,38],"challenge,":[16],"as":[17],"they":[18],"are":[19,27,83],"less":[20],"polluting":[21],"and":[22,123,144,151,173],"economically":[23],"viable.":[24],"Their":[25],"batteries":[26,62,73],"based":[28,84],"on":[29,85],"lithium-ion":[30],"technology.":[31],"Indeed,":[32],"in":[33,126],"addition":[34],"its":[36,139],"high":[37],"mass":[40],"ratio,":[41],"self-discharge":[42],"is":[43,98,135],"relatively":[44],"low":[45],"compared":[46],"other":[48],"batteries.":[49],"However,":[50],"significant":[51],"work":[52],"hasn't":[53],"been":[54,70],"done":[55],"understand":[57],"aging":[59],"sequences":[60],"related":[63],"their":[65],"use.":[66],"Several":[67],"solutions":[68],"have":[69],"proposed":[71],"for":[72,109],"State":[74],"Of":[75],"Health":[76],"(SOH)":[77],"diagnosis":[78],"but":[79],"them":[82],"multi-physics":[86],"methods":[87,172],"that":[88,178],"do":[89],"not":[90],"consider":[91],"system's":[93],"operational":[94],"condition.":[95],"Our":[96],"contribution":[97],"make":[100],"an":[101],"exploratory":[102],"study":[103],"by":[104],"applying":[105],"Machine":[106],"Learning":[107],"models":[108],"battery":[110,134,156],"SOH":[111,140],"prediction":[112],"from":[113,166],"readily":[114],"available":[115],"values.":[116],"We":[117,158,176],"decided":[118],"test":[120],"several":[121],"supervised":[122],"unsupervised":[124],"approaches":[125],"order":[127],"train":[129],"a":[130,160,187],"binary":[131],"classifier.":[132],"A":[133],"considered":[136],"used":[137,159],"when":[138],"drops":[141],"below":[142],"80%,":[143],"normal":[145],"otherwise.":[146],"Features":[147],"consists":[148],"voltage":[150],"current":[152],"measured":[153],"at":[154],"output.":[157],"wide":[161],"range":[162],"algorithms,":[164],"ranging":[165],"neuron":[167],"networks,":[168],"probabilistic":[169],"models,":[170],"ensemblist":[171,179],"clustering":[174],"algorithms.":[175],"concluded":[177],"algorithms":[180],"present":[181],"best":[183],"performances:":[184],"up":[185],"precision":[188],"84":[190],"%.":[191]},"counts_by_year":[{"year":2024,"cited_by_count":2}],"updated_date":"2026-04-21T08:09:41.155169","created_date":"2025-10-10T00:00:00"}
