{"id":"https://openalex.org/W4387971515","doi":"https://doi.org/10.3390/computers12110219","title":"A CNN-GRU Approach to the Accurate Prediction of Batteries\u2019 Remaining Useful Life from Charging Profiles","display_name":"A CNN-GRU Approach to the Accurate Prediction of Batteries\u2019 Remaining Useful Life from Charging Profiles","publication_year":2023,"publication_date":"2023-10-27","ids":{"openalex":"https://openalex.org/W4387971515","doi":"https://doi.org/10.3390/computers12110219"},"language":"en","primary_location":{"id":"doi:10.3390/computers12110219","is_oa":true,"landing_page_url":"https://doi.org/10.3390/computers12110219","pdf_url":"https://www.mdpi.com/2073-431X/12/11/219/pdf?version=1698404408","source":{"id":"https://openalex.org/S4210228075","display_name":"Computers","issn_l":"2073-431X","issn":["2073-431X"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computers","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/2073-431X/12/11/219/pdf?version=1698404408","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5049418440","display_name":"Sadiqa Jafari","orcid":"https://orcid.org/0000-0001-9308-1062"},"institutions":[{"id":"https://openalex.org/I83202590","display_name":"Jeju National University","ror":"https://ror.org/05hnb4n85","country_code":"KR","type":"education","lineage":["https://openalex.org/I83202590"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Sadiqa Jafari","raw_affiliation_strings":["Department of Electronic Engineering, Institute of Information Science & Technology, Jeju National University, Jeju 63243, Republic of Korea"],"raw_orcid":"https://orcid.org/0000-0001-9308-1062","affiliations":[{"raw_affiliation_string":"Department of Electronic Engineering, Institute of Information Science & Technology, Jeju National University, Jeju 63243, Republic of Korea","institution_ids":["https://openalex.org/I83202590"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5110809957","display_name":"Yung-Cheol Byun","orcid":"https://orcid.org/0000-0003-1107-9941"},"institutions":[{"id":"https://openalex.org/I83202590","display_name":"Jeju National University","ror":"https://ror.org/05hnb4n85","country_code":"KR","type":"education","lineage":["https://openalex.org/I83202590"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Yung-Cheol Byun","raw_affiliation_strings":["Department of Computer Engineering, Major of Electronic Engineering, Institute of Information Science & Technology, Jeju National University, Jeju 63243, Republic of Korea"],"raw_orcid":"https://orcid.org/0000-0003-1107-9941","affiliations":[{"raw_affiliation_string":"Department of Computer Engineering, Major of Electronic Engineering, Institute of Information Science & Technology, Jeju National University, Jeju 63243, Republic of Korea","institution_ids":["https://openalex.org/I83202590"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5110809957"],"corresponding_institution_ids":["https://openalex.org/I83202590"],"apc_list":{"value":1600,"currency":"CHF","value_usd":1782},"apc_paid":{"value":1600,"currency":"CHF","value_usd":1782},"fwci":3.0975,"has_fulltext":true,"cited_by_count":43,"citation_normalized_percentile":{"value":0.92610718,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":100},"biblio":{"volume":"12","issue":"11","first_page":"219","last_page":"219"},"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/T10018","display_name":"Advancements in Battery Materials","score":0.9991999864578247,"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/T10780","display_name":"Reliability and Maintenance Optimization","score":0.9891999959945679,"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/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7221802473068237},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.7081283926963806},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5853182673454285},{"id":"https://openalex.org/keywords/reliability","display_name":"Reliability (semiconductor)","score":0.5369325280189514},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5233665704727173},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5107295513153076},{"id":"https://openalex.org/keywords/mean-squared-prediction-error","display_name":"Mean squared prediction error","score":0.4782247245311737},{"id":"https://openalex.org/keywords/mean-absolute-error","display_name":"Mean absolute error","score":0.4341590404510498},{"id":"https://openalex.org/keywords/battery","display_name":"Battery (electricity)","score":0.4296235740184784},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4120827913284302},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.41192543506622314},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4071568548679352},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3739595413208008},{"id":"https://openalex.org/keywords/power","display_name":"Power (physics)","score":0.12339770793914795},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.08357506990432739},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.07469788193702698}],"concepts":[{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7221802473068237},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.7081283926963806},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5853182673454285},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.5369325280189514},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5233665704727173},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5107295513153076},{"id":"https://openalex.org/C167085575","wikidata":"https://www.wikidata.org/wiki/Q6803654","display_name":"Mean squared prediction error","level":2,"score":0.4782247245311737},{"id":"https://openalex.org/C188154048","wikidata":"https://www.wikidata.org/wiki/Q6803609","display_name":"Mean absolute error","level":3,"score":0.4341590404510498},{"id":"https://openalex.org/C555008776","wikidata":"https://www.wikidata.org/wiki/Q267298","display_name":"Battery (electricity)","level":3,"score":0.4296235740184784},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4120827913284302},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.41192543506622314},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4071568548679352},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3739595413208008},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.12339770793914795},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.08357506990432739},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.07469788193702698},{"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},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.3390/computers12110219","is_oa":true,"landing_page_url":"https://doi.org/10.3390/computers12110219","pdf_url":"https://www.mdpi.com/2073-431X/12/11/219/pdf?version=1698404408","source":{"id":"https://openalex.org/S4210228075","display_name":"Computers","issn_l":"2073-431X","issn":["2073-431X"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computers","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:48d546fe09644630a5b6502e7772c4c2","is_oa":true,"landing_page_url":"https://doaj.org/article/48d546fe09644630a5b6502e7772c4c2","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Computers, Vol 12, Iss 11, p 219 (2023)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.3390/computers12110219","is_oa":true,"landing_page_url":"https://doi.org/10.3390/computers12110219","pdf_url":"https://www.mdpi.com/2073-431X/12/11/219/pdf?version=1698404408","source":{"id":"https://openalex.org/S4210228075","display_name":"Computers","issn_l":"2073-431X","issn":["2073-431X"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computers","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Life below water","id":"https://metadata.un.org/sdg/14","score":0.6499999761581421}],"awards":[],"funders":[{"id":"https://openalex.org/F4320320671","display_name":"National Research Foundation","ror":"https://ror.org/05s0g1g46"},{"id":"https://openalex.org/F4320321681","display_name":"Ministry of Trade, Industry and Energy","ror":"https://ror.org/008nkqk13"},{"id":"https://openalex.org/F4320322120","display_name":"National Research Foundation of Korea","ror":"https://ror.org/013aysd81"},{"id":"https://openalex.org/F4320322724","display_name":"Ministry of Education, India","ror":"https://ror.org/048xjjh50"}],"has_content":{"grobid_xml":false,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4387971515.pdf"},"referenced_works_count":58,"referenced_works":["https://openalex.org/W1628954589","https://openalex.org/W1981952896","https://openalex.org/W2071280205","https://openalex.org/W2109539992","https://openalex.org/W2111072639","https://openalex.org/W2115233498","https://openalex.org/W2617137613","https://openalex.org/W2768606939","https://openalex.org/W2781930323","https://openalex.org/W2795701208","https://openalex.org/W2808997338","https://openalex.org/W2809859787","https://openalex.org/W2901594529","https://openalex.org/W2901949610","https://openalex.org/W2920879126","https://openalex.org/W2934286352","https://openalex.org/W2944851425","https://openalex.org/W2947678382","https://openalex.org/W2964437884","https://openalex.org/W2965233728","https://openalex.org/W2987934294","https://openalex.org/W3001479960","https://openalex.org/W3013372615","https://openalex.org/W3042828663","https://openalex.org/W3083853617","https://openalex.org/W3167145731","https://openalex.org/W3171724330","https://openalex.org/W3172280330","https://openalex.org/W3187538253","https://openalex.org/W3199081584","https://openalex.org/W3207981445","https://openalex.org/W3210458149","https://openalex.org/W3213777593","https://openalex.org/W4200453228","https://openalex.org/W4205303226","https://openalex.org/W4206481364","https://openalex.org/W4225528732","https://openalex.org/W4281554194","https://openalex.org/W4285128407","https://openalex.org/W4295046711","https://openalex.org/W4295777687","https://openalex.org/W4307047016","https://openalex.org/W4310154258","https://openalex.org/W4312754166","https://openalex.org/W4313598793","https://openalex.org/W4320520392","https://openalex.org/W4320802876","https://openalex.org/W4327951474","https://openalex.org/W4362670131","https://openalex.org/W4363651729","https://openalex.org/W4364856178","https://openalex.org/W4365801643","https://openalex.org/W4376481070","https://openalex.org/W4385342469","https://openalex.org/W4386325692","https://openalex.org/W4388720042","https://openalex.org/W6676316913","https://openalex.org/W6852515295"],"related_works":["https://openalex.org/W4247388746","https://openalex.org/W2314720829","https://openalex.org/W3178576217","https://openalex.org/W4221063543","https://openalex.org/W4285794683","https://openalex.org/W4286256617","https://openalex.org/W4385195237","https://openalex.org/W4362673848","https://openalex.org/W4319441652","https://openalex.org/W2621951291"],"abstract_inverted_index":{"Predicting":[0],"the":[1,12,22,59,114,173,184,205,210,215],"remaining":[2],"useful":[3],"life":[4],"(RUL)":[5],"is":[6,55],"a":[7,79,151],"pivotal":[8],"step":[9],"in":[10,69,154,213],"ensuring":[11],"reliability":[13],"of":[14,26,61,82,116,177,186,209,217],"lithium-ion":[15],"batteries":[16],"(LIBs).":[17],"In":[18],"order":[19],"to":[20,56,105,128,135,141,192,203],"enhance":[21],"precision":[23],"and":[24,46,92,144,165,189],"stability":[25],"battery":[27],"RUL":[28,62,216],"prediction,":[29],"this":[30,95],"study":[31,198],"introduces":[32],"an":[33],"innovative":[34],"hybrid":[35],"deep":[36],"learning":[37],"model":[38,67,127,149,212],"that":[39],"seamlessly":[40],"integrates":[41],"convolutional":[42],"neural":[43],"network":[44],"(CNN)":[45],"gated":[47],"recurrent":[48],"unit":[49],"(GRU)":[50],"architectures.":[51],"Our":[52,66],"primary":[53],"goal":[54],"significantly":[57],"improve":[58],"accuracy":[60],"predictions":[63],"for":[64],"LIBs.":[65,218],"excels":[68],"its":[70,137],"predictive":[71,175,207],"capabilities":[72,176],"by":[73],"skillfully":[74],"extracting":[75],"intricate":[76],"features":[77],"from":[78,120],"diverse":[80],"array":[81],"data":[83,202],"sources,":[84],"including":[85],"voltage":[86],"(V),":[87],"current":[88],"(I),":[89],"temperature":[90],"(T),":[91],"capacity.":[93],"Within":[94],"novel":[96],"architectural":[97],"design,":[98],"parallel":[99],"CNN":[100],"layers":[101],"are":[102],"meticulously":[103],"crafted":[104],"process":[106],"each":[107],"input":[108],"feature":[109],"individually.":[110],"This":[111,197],"approach":[112],"enables":[113],"extraction":[115],"highly":[117],"pertinent":[118],"information":[119],"multi-channel":[121],"charging":[122],"profiles.":[123],"We":[124],"subjected":[125],"our":[126,147,178],"rigorous":[129],"evaluations":[130],"across":[131],"three":[132],"distinct":[133],"scenarios":[134],"validate":[136],"effectiveness.":[138],"When":[139],"compared":[140],"LSTM,":[142],"GRU,":[143],"CNN-LSTM":[145],"models,":[146],"CNN-GRU":[148,179,211],"showcases":[150],"remarkable":[152],"reduction":[153],"root":[155],"mean":[156,159,162,166],"square":[157,160],"error,":[158,161,164],"absolute":[163,167],"percentage":[168],"error.":[169],"These":[170],"results":[171],"affirm":[172],"superior":[174,194],"model,":[180],"which":[181],"effectively":[182],"harnesses":[183],"strengths":[185],"both":[187],"CNNs":[188],"GRU":[190],"networks":[191],"achieve":[193],"prediction":[195],"accuracy.":[196],"draws":[199],"upon":[200],"NASA":[201],"underscore":[204],"outstanding":[206],"performance":[208],"estimating":[214]},"counts_by_year":[{"year":2026,"cited_by_count":11},{"year":2025,"cited_by_count":19},{"year":2024,"cited_by_count":12},{"year":2023,"cited_by_count":1}],"updated_date":"2026-07-22T07:51:19.307946","created_date":"2025-10-10T00:00:00"}