Towards a Smarter Battery Management System for Electric Vehicle Applications: A Critical Review of Lithium-Ion Battery State of Charge Estimation

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dc.contributor.authorAli, Muhammad Umairko
dc.contributor.authorZafar, Amadko
dc.contributor.authorNengroo, Sarvar Hussainko
dc.contributor.authorHussain, Sadamko
dc.contributor.authorAlvi, Muhammad Junaidko
dc.contributor.authorKim, Hee-Jeko
dc.date.accessioned2021-06-14T07:50:06Z-
dc.date.available2021-06-14T07:50:06Z-
dc.date.created2021-05-20-
dc.date.created2021-05-20-
dc.date.created2021-05-20-
dc.date.issued2019-02-
dc.identifier.citationENERGIES, v.12, no.3, pp.446-
dc.identifier.issn1996-1073-
dc.identifier.urihttp://hdl.handle.net/10203/285865-
dc.description.abstract<jats:p>Energy storage system (ESS) technology is still the logjam for the electric vehicle (EV) industry. Lithium-ion (Li-ion) batteries have attracted considerable attention in the EV industry owing to their high energy density, lifespan, nominal voltage, power density, and cost. In EVs, a smart battery management system (BMS) is one of the essential components; it not only measures the states of battery accurately, but also ensures safe operation and prolongs the battery life. The accurate estimation of the state of charge (SOC) of a Li-ion battery is a very challenging task because the Li-ion battery is a highly time variant, non-linear, and complex electrochemical system. This paper explains the workings of a Li-ion battery, provides the main features of a smart BMS, and comprehensively reviews its SOC estimation methods. These SOC estimation methods have been classified into four main categories depending on their nature. A critical explanation, including their merits, limitations, and their estimation errors from other studies, is provided. Some recommendations depending on the development of technology are suggested to improve the online estimation.</jats:p>-
dc.languageEnglish-
dc.publisherMDPI-
dc.titleTowards a Smarter Battery Management System for Electric Vehicle Applications: A Critical Review of Lithium-Ion Battery State of Charge Estimation-
dc.typeArticle-
dc.identifier.wosid000460666200114-
dc.identifier.scopusid2-s2.0-85060935068-
dc.type.rimsART-
dc.citation.volume12-
dc.citation.issue3-
dc.citation.beginningpage446-
dc.citation.publicationnameENERGIES-
dc.identifier.doi10.3390/en12030446-
dc.contributor.nonIdAuthorAli, Muhammad Umair-
dc.contributor.nonIdAuthorZafar, Amad-
dc.contributor.nonIdAuthorHussain, Sadam-
dc.contributor.nonIdAuthorAlvi, Muhammad Junaid-
dc.contributor.nonIdAuthorKim, Hee-Je-
dc.description.isOpenAccessN-
dc.type.journalArticleReview-
dc.subject.keywordAuthorbattery management system-
dc.subject.keywordAuthorenergy storage system-
dc.subject.keywordAuthorelectric vehicle-
dc.subject.keywordAuthorlithium-ion battery-
dc.subject.keywordAuthorstate of charge-
dc.subject.keywordPlusOPEN-CIRCUIT-VOLTAGE-
dc.subject.keywordPlusUNSCENTED KALMAN FILTER-
dc.subject.keywordPlusSLIDING MODE OBSERVER-
dc.subject.keywordPlusFUZZY NEURAL-NETWORK-
dc.subject.keywordPlusOF-CHARGE-
dc.subject.keywordPlusLIFEPO4 BATTERY-
dc.subject.keywordPlusONLINE ESTIMATION-
dc.subject.keywordPlusELECTROCHEMICAL MODEL-
dc.subject.keywordPlusHEALTH ESTIMATION-
dc.subject.keywordPlusPARTICLE FILTER-
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