{"id":"https://openalex.org/W4313492206","doi":"https://doi.org/10.1109/icsc57768.2022.9993852","title":"Cross-Entropy based Feature Selection for Lithium-ion Battery State of Health Estimation","display_name":"Cross-Entropy based Feature Selection for Lithium-ion Battery State of Health Estimation","publication_year":2022,"publication_date":"2022-11-23","ids":{"openalex":"https://openalex.org/W4313492206","doi":"https://doi.org/10.1109/icsc57768.2022.9993852"},"language":"en","primary_location":{"id":"doi:10.1109/icsc57768.2022.9993852","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/icsc57768.2022.9993852","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 10th International Conference on Systems and Control (ICSC)","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/A5029547618","display_name":"Ji Wu","orcid":"https://orcid.org/0000-0003-3320-3704"},"institutions":[{"id":"https://openalex.org/I16365422","display_name":"Hefei University of Technology","ror":"https://ror.org/02czkny70","country_code":"CN","type":"education","lineage":["https://openalex.org/I16365422"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ji Wu","raw_affiliation_strings":["School of Automotive and Transportation Engineering, Hefei University of Technology,Hefei,Anhui,China,230009"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Automotive and Transportation Engineering, Hefei University of Technology,Hefei,Anhui,China,230009","institution_ids":["https://openalex.org/I16365422"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5057249367","display_name":"Zhen Cheng","orcid":"https://orcid.org/0000-0002-7655-9930"},"institutions":[{"id":"https://openalex.org/I16365422","display_name":"Hefei University of Technology","ror":"https://ror.org/02czkny70","country_code":"CN","type":"education","lineage":["https://openalex.org/I16365422"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhen Cheng","raw_affiliation_strings":["School of Automotive and Transportation Engineering, Hefei University of Technology,Hefei,Anhui,China,230009"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Automotive and Transportation Engineering, Hefei University of Technology,Hefei,Anhui,China,230009","institution_ids":["https://openalex.org/I16365422"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100335974","display_name":"Li Wang","orcid":"https://orcid.org/0000-0002-2272-577X"},"institutions":[{"id":"https://openalex.org/I16365422","display_name":"Hefei University of Technology","ror":"https://ror.org/02czkny70","country_code":"CN","type":"education","lineage":["https://openalex.org/I16365422"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Li Wang","raw_affiliation_strings":["School of Automotive and Transportation Engineering, Hefei University of Technology,Hefei,Anhui,China,230009"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Automotive and Transportation Engineering, Hefei University of Technology,Hefei,Anhui,China,230009","institution_ids":["https://openalex.org/I16365422"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I16365422"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.16150993,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"168","last_page":"173"},"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/T10876","display_name":"Fault Detection and Control Systems","score":0.9922000169754028,"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/T10780","display_name":"Reliability and Maintenance Optimization","score":0.9793000221252441,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.7172150015830994},{"id":"https://openalex.org/keywords/cross-entropy","display_name":"Cross entropy","score":0.6880935430526733},{"id":"https://openalex.org/keywords/state-of-health","display_name":"State of health","score":0.5774223208427429},{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.5381923317909241},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5257759094238281},{"id":"https://openalex.org/keywords/entropy-estimation","display_name":"Entropy estimation","score":0.47812190651893616},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4535674452781677},{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.4529981017112732},{"id":"https://openalex.org/keywords/correlation-coefficient","display_name":"Correlation coefficient","score":0.44979336857795715},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.43942829966545105},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.4367794692516327},{"id":"https://openalex.org/keywords/cross-validation","display_name":"Cross-validation","score":0.4343832731246948},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4302396774291992},{"id":"https://openalex.org/keywords/battery","display_name":"Battery (electricity)","score":0.33716902136802673},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.25570595264434814},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.2317960560321808},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.22289106249809265},{"id":"https://openalex.org/keywords/chemistry","display_name":"Chemistry","score":0.11149942874908447},{"id":"https://openalex.org/keywords/power","display_name":"Power (physics)","score":0.10455498099327087},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.07383182644844055}],"concepts":[{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.7172150015830994},{"id":"https://openalex.org/C167981619","wikidata":"https://www.wikidata.org/wiki/Q1685498","display_name":"Cross entropy","level":3,"score":0.6880935430526733},{"id":"https://openalex.org/C2777294910","wikidata":"https://www.wikidata.org/wiki/Q4050070","display_name":"State of health","level":4,"score":0.5774223208427429},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.5381923317909241},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5257759094238281},{"id":"https://openalex.org/C95546049","wikidata":"https://www.wikidata.org/wiki/Q1345207","display_name":"Entropy estimation","level":3,"score":0.47812190651893616},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4535674452781677},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.4529981017112732},{"id":"https://openalex.org/C2780092901","wikidata":"https://www.wikidata.org/wiki/Q3433612","display_name":"Correlation coefficient","level":2,"score":0.44979336857795715},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.43942829966545105},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.4367794692516327},{"id":"https://openalex.org/C27181475","wikidata":"https://www.wikidata.org/wiki/Q541014","display_name":"Cross-validation","level":2,"score":0.4343832731246948},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4302396774291992},{"id":"https://openalex.org/C555008776","wikidata":"https://www.wikidata.org/wiki/Q267298","display_name":"Battery (electricity)","level":3,"score":0.33716902136802673},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.25570595264434814},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.2317960560321808},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.22289106249809265},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.11149942874908447},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.10455498099327087},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.07383182644844055},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","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},{"id":"https://openalex.org/C147597530","wikidata":"https://www.wikidata.org/wiki/Q369472","display_name":"Computational chemistry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icsc57768.2022.9993852","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/icsc57768.2022.9993852","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 10th International Conference on Systems and Control (ICSC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G4318783310","display_name":null,"funder_award_id":"JZ2021HGTB0076","funder_id":"https://openalex.org/F4320335787","funder_display_name":"Fundamental Research Funds for the Central Universities"},{"id":"https://openalex.org/G486167925","display_name":null,"funder_award_id":"61903114","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320335787","display_name":"Fundamental Research Funds for the Central Universities","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W2078279667","https://openalex.org/W2133989913","https://openalex.org/W2342265232","https://openalex.org/W2501990625","https://openalex.org/W2767663538","https://openalex.org/W2921358399","https://openalex.org/W3025145154","https://openalex.org/W3062552330","https://openalex.org/W3100042739","https://openalex.org/W3157622060","https://openalex.org/W3172708099","https://openalex.org/W3203177729","https://openalex.org/W4200390501","https://openalex.org/W4210592941","https://openalex.org/W4239181501","https://openalex.org/W4285266158"],"related_works":["https://openalex.org/W3205034193","https://openalex.org/W2025158506","https://openalex.org/W3082640823","https://openalex.org/W4387210440","https://openalex.org/W1547215736","https://openalex.org/W4390933643","https://openalex.org/W4313492206","https://openalex.org/W4298531029","https://openalex.org/W1531408481","https://openalex.org/W3121902971"],"abstract_inverted_index":{"State":[0],"of":[1,7,11,28,43],"health":[2],"(SOH)":[3],"estimation":[4,20,42,133],"is":[5,22,52,75],"one":[6],"the":[8,26,44,64,78,82,108,123],"essential":[9],"functions":[10],"an":[12,92],"electric":[13],"vehicle":[14],"battery":[15],"management":[16],"system.":[17],"An":[18],"accurate":[19],"result":[21],"conducive":[23],"to":[24],"extending":[25],"life":[27],"lithium-ion":[29,45],"batteries":[30],"and":[31,35,68,72,86,116],"ensuring":[32],"vehicles'":[33],"safe":[34],"reliable":[36],"operation.":[37],"Based":[38],"on":[39,63,128],"data-driven":[40],"SOH":[41,132],"battery,":[46],"a":[47],"cross-entropy":[48,129],"feature":[49,74,103,124],"selection":[50,125],"method":[51,126],"proposed":[53],"in":[54],"this":[55],"work.":[56],"Firstly,":[57],"51":[58],"features":[59,90],"are":[60,98,105],"extracted":[61],"based":[62,127],"partial":[65],"charging":[66],"voltage":[67],"incremental":[69],"capacity":[70],"curves,":[71],"each":[73],"evaluated":[76],"by":[77],"Pearson":[79],"correlation":[80,84],"coefficient,":[81,85],"Kendall":[83],"cross-entropy,":[87],"respectively.":[88],"Then,":[89],"with":[91],"absolute":[93],"value":[94],"greater":[95],"than":[96],"0.9":[97],"picked":[99],"out.":[100],"Finally,":[101],"selected":[102],"sets":[104],"compared":[106],"via":[107],"back":[109],"propagation":[110],"neural":[111],"network,":[112],"Gaussian":[113],"process":[114],"regression,":[115],"random":[117],"forest.":[118],"Experimental":[119],"results":[120],"show":[121],"that":[122],"achieves":[130],"better":[131],"accuracy.":[134]},"counts_by_year":[],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2025-10-10T00:00:00"}