DC Field | Value | Language |
---|---|---|
dc.contributor.author | Ali, Muhammad Umair | ko |
dc.contributor.author | Zafar, Amad | ko |
dc.contributor.author | Nengroo, Sarvar Hussain | ko |
dc.contributor.author | Hussain, Sadam | ko |
dc.contributor.author | Alvi, Muhammad Junaid | ko |
dc.contributor.author | Kim, Hee-Je | ko |
dc.date.accessioned | 2021-06-14T07:50:06Z | - |
dc.date.available | 2021-06-14T07:50:06Z | - |
dc.date.created | 2021-05-20 | - |
dc.date.created | 2021-05-20 | - |
dc.date.created | 2021-05-20 | - |
dc.date.issued | 2019-02 | - |
dc.identifier.citation | ENERGIES, v.12, no.3, pp.446 | - |
dc.identifier.issn | 1996-1073 | - |
dc.identifier.uri | http://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.language | English | - |
dc.publisher | MDPI | - |
dc.title | Towards a Smarter Battery Management System for Electric Vehicle Applications: A Critical Review of Lithium-Ion Battery State of Charge Estimation | - |
dc.type | Article | - |
dc.identifier.wosid | 000460666200114 | - |
dc.identifier.scopusid | 2-s2.0-85060935068 | - |
dc.type.rims | ART | - |
dc.citation.volume | 12 | - |
dc.citation.issue | 3 | - |
dc.citation.beginningpage | 446 | - |
dc.citation.publicationname | ENERGIES | - |
dc.identifier.doi | 10.3390/en12030446 | - |
dc.contributor.nonIdAuthor | Ali, Muhammad Umair | - |
dc.contributor.nonIdAuthor | Zafar, Amad | - |
dc.contributor.nonIdAuthor | Hussain, Sadam | - |
dc.contributor.nonIdAuthor | Alvi, Muhammad Junaid | - |
dc.contributor.nonIdAuthor | Kim, Hee-Je | - |
dc.description.isOpenAccess | N | - |
dc.type.journalArticle | Review | - |
dc.subject.keywordAuthor | battery management system | - |
dc.subject.keywordAuthor | energy storage system | - |
dc.subject.keywordAuthor | electric vehicle | - |
dc.subject.keywordAuthor | lithium-ion battery | - |
dc.subject.keywordAuthor | state of charge | - |
dc.subject.keywordPlus | OPEN-CIRCUIT-VOLTAGE | - |
dc.subject.keywordPlus | UNSCENTED KALMAN FILTER | - |
dc.subject.keywordPlus | SLIDING MODE OBSERVER | - |
dc.subject.keywordPlus | FUZZY NEURAL-NETWORK | - |
dc.subject.keywordPlus | OF-CHARGE | - |
dc.subject.keywordPlus | LIFEPO4 BATTERY | - |
dc.subject.keywordPlus | ONLINE ESTIMATION | - |
dc.subject.keywordPlus | ELECTROCHEMICAL MODEL | - |
dc.subject.keywordPlus | HEALTH ESTIMATION | - |
dc.subject.keywordPlus | PARTICLE FILTER | - |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.