{"id":"https://openalex.org/W4412188793","doi":"https://doi.org/10.32604/cmc.2025.065741","title":"A Novel Attention-Augmented LSTM (AA-LSTM) Model for Optimized Energy Management in EV Charging Stations","display_name":"A Novel Attention-Augmented LSTM (AA-LSTM) Model for Optimized Energy Management in EV Charging Stations","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4412188793","doi":"https://doi.org/10.32604/cmc.2025.065741"},"language":"en","primary_location":{"id":"doi:10.32604/cmc.2025.065741","is_oa":true,"landing_page_url":"https://doi.org/10.32604/cmc.2025.065741","pdf_url":null,"source":{"id":"https://openalex.org/S4210191605","display_name":"Computers, materials & continua/Computers, materials & continua (Print)","issn_l":"1546-2218","issn":["1546-2218","1546-2226"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computers, Materials &amp; Continua","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://doi.org/10.32604/cmc.2025.065741","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101791975","display_name":"Harendra Singh","orcid":"https://orcid.org/0000-0003-3667-4261"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Harendra Pratap Singh","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5067278816","display_name":"Ishfaq Hussain Rather","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ishfaq Hussain Rather","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5004577872","display_name":"Sushil Kumar","orcid":"https://orcid.org/0000-0003-4481-8878"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sushil Kumar","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5015923093","display_name":"Mohammad Aljaidi","orcid":"https://orcid.org/0000-0001-9486-3533"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mohammad Aljaidi","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5030757255","display_name":"Omprakash Kaiwartya","orcid":"https://orcid.org/0000-0001-9669-8244"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Omprakash Kaiwartya","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5101791975"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.1866708,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"84","issue":"3","first_page":"5577","last_page":"5595"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10768","display_name":"Electric Vehicles and Infrastructure","score":0.9883999824523926,"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"}},"topics":[{"id":"https://openalex.org/T10768","display_name":"Electric Vehicles and Infrastructure","score":0.9883999824523926,"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/T10663","display_name":"Advanced Battery Technologies Research","score":0.9772999882698059,"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/T10808","display_name":"Electric and Hybrid Vehicle Technologies","score":0.9517999887466431,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/energy","display_name":"Energy (signal processing)","score":0.5615844130516052},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5391983985900879},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.40280041098594666},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.3392963707447052},{"id":"https://openalex.org/keywords/environmental-science","display_name":"Environmental science","score":0.33088767528533936},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.1382269263267517},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1366424560546875}],"concepts":[{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.5615844130516052},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5391983985900879},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.40280041098594666},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.3392963707447052},{"id":"https://openalex.org/C39432304","wikidata":"https://www.wikidata.org/wiki/Q188847","display_name":"Environmental science","level":0,"score":0.33088767528533936},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.1382269263267517},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1366424560546875}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.32604/cmc.2025.065741","is_oa":true,"landing_page_url":"https://doi.org/10.32604/cmc.2025.065741","pdf_url":null,"source":{"id":"https://openalex.org/S4210191605","display_name":"Computers, materials & continua/Computers, materials & continua (Print)","issn_l":"1546-2218","issn":["1546-2218","1546-2226"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computers, Materials &amp; Continua","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.32604/cmc.2025.065741","is_oa":true,"landing_page_url":"https://doi.org/10.32604/cmc.2025.065741","pdf_url":null,"source":{"id":"https://openalex.org/S4210191605","display_name":"Computers, materials & continua/Computers, materials & continua (Print)","issn_l":"1546-2218","issn":["1546-2218","1546-2226"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computers, Materials &amp; Continua","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/7","score":0.8600000143051147,"display_name":"Affordable and clean energy"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W2001318598","https://openalex.org/W2047236237","https://openalex.org/W2064675550","https://openalex.org/W2073562378","https://openalex.org/W2114461427","https://openalex.org/W2131730934","https://openalex.org/W2255466643","https://openalex.org/W2466975708","https://openalex.org/W2754252319","https://openalex.org/W2777029077","https://openalex.org/W2919115771","https://openalex.org/W2941184193","https://openalex.org/W2953748503","https://openalex.org/W3122073869","https://openalex.org/W4200302868","https://openalex.org/W4366779614","https://openalex.org/W4385258623","https://openalex.org/W4386933426","https://openalex.org/W4390659196","https://openalex.org/W4391097018","https://openalex.org/W4398775538","https://openalex.org/W4401957754","https://openalex.org/W4402741986","https://openalex.org/W4404041408","https://openalex.org/W4406856991"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W4391913857","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W3204019825"],"abstract_inverted_index":{"Electric":[0],"Vehicles":[1],"(EVs)":[2],"have":[3,110],"emerged":[4],"as":[5,99],"a":[6,178,269,278,298],"cleaner,":[7],"low-carbon,":[8],"and":[9,51,66,77,104,161,209,227,254,259,277,288,311],"environmentally":[10],"friendly":[11],"alternative":[12],"to":[13,29,70,141,193,205,314],"traditional":[14],"internal":[15],"combustion":[16],"engine":[17],"(ICE)":[18],"vehicles.":[19],"With":[20],"the":[21,36,72,81,87,170,196,215,225,230,233,247,251,256,294],"increasing":[22],"adoption":[23],"of":[24,39,74,84,172,217,229,246,275,283],"EVs,":[25],"they":[26],"are":[27,145],"expected":[28],"eventually":[30],"replace":[31],"ICE":[32],"vehicles":[33],"entirely.":[34],"However,":[35,122],"rapid":[37],"growth":[38],"EVs":[40,85],"has":[41,58],"significantly":[42],"increased":[43],"energy":[44,56,67,115,304],"demand,":[45],"posing":[46],"challenges":[47],"for":[48,113,148,302],"power":[49,75],"grids":[50],"infrastructure.":[52],"This":[53],"surge":[54],"in":[55,61,220],"demand":[57,116,305],"driven":[59],"advancements":[60],"developing":[62],"efficient":[63,163],"charging":[64,119],"infrastructure":[65],"management":[68],"solutions":[69],"mitigate":[71],"risks":[73],"outages":[76],"disruptions":[78],"caused":[79],"by":[80],"rising":[82],"number":[83],"on":[86,195],"road.":[88],"To":[89],"address":[90],"these":[91,123,151,173],"challenges,":[92],"various":[93],"deep":[94],"learning":[95],"(DL)":[96],"models,":[97],"such":[98],"Recurrent":[100],"Neural":[101],"Networks":[102],"(RNNs)":[103],"Long":[105,181],"Short-Term":[106,182],"Memory":[107,183],"(LSTM)":[108],"networks,":[109],"been":[111],"employed":[112],"predicting":[114,303],"at":[117,306],"EV":[118],"stations":[120],"(EVCS).":[121],"models":[124,152],"face":[125],"certain":[126],"limitations.":[127],"They":[128],"often":[129],"lack":[130],"interpretability,":[131],"treating":[132],"all":[133],"input":[134,248],"steps":[135],"equally":[136],"without":[137],"assigning":[138],"greater":[139],"importance":[140],"critical":[142,244],"patterns":[143],"that":[144,293],"more":[146,260],"relevant":[147,198],"prediction.":[149],"Additionally,":[150],"process":[153],"data":[154,223],"sequentially,":[155],"which":[156],"makes":[157],"them":[158],"computationally":[159],"slower":[160],"less":[162],"when":[164],"dealing":[165],"with":[166,224],"large":[167],"datasets.":[168],"In":[169],"context":[171],"limitations,":[174],"this":[175],"paper":[176],"introduces":[177],"novel":[179],"Attention-Augmented":[180],"(AA-LSTM)":[184],"model.":[185],"The":[186,239,262],"proposed":[187],"model":[188,235,257,264,296],"integrates":[189],"an":[190],"attention":[191,231,240],"mechanism":[192,241],"focus":[194],"most":[197],"time":[199],"steps,":[200],"thereby":[201],"enhancing":[202],"its":[203,286],"ability":[204],"capture":[206],"long-term":[207],"dependencies":[208],"improve":[210],"prediction":[211],"accuracy.":[212],"By":[213],"combining":[214],"strengths":[216],"LSTM":[218],"networks":[219],"handling":[221],"sequential":[222],"interpretability":[226],"efficiency":[228,312],"mechanism,":[232],"AA-LSTM":[234,263,295],"delivers":[236],"superior":[237],"performance.":[238],"selectively":[242],"prioritizes":[243],"parts":[245],"sequence,":[249],"reducing":[250],"computational":[252],"burden":[253],"making":[255],"faster":[258],"effective.":[261],"achieves":[265],"impressive":[266],"results,":[267],"demonstrating":[268],"Mean":[270,279],"Absolute":[271],"Percentage":[272],"Error":[273,281],"(MAPE)":[274],"3.90%":[276],"Squared":[280],"(MSE)":[282],"0.40,":[284],"highlighting":[285],"accuracy":[287],"reliability.":[289],"These":[290],"results":[291],"suggest":[292],"is":[297],"highly":[299],"promising":[300],"solution":[301],"EVCS,":[307],"offering":[308],"improved":[309],"performance":[310],"compared":[313],"contemporary":[315],"approaches.":[316]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
