Abstract
Energy storage plays an important role in the adoption of renewable energy to help solve climate change problems. Lithium-ion batteries (LIBs) are an excellent solution for energy storage due to their properties. In order to ensure the safety and efficient operation of LIB systems, battery management systems (BMSs) are required. The current design and functionality of BMSs suffer from a few critical drawbacks including low computational capability and limited data storage. Recently, there has been some effort in researching and developing smart BMSs utilizing the cloud platform. A cloud-based BMS would be able to solve the problems of computational capability and data storage in the current BMSs. It would also lead to more accurate and reliable battery algorithms and allow the development of other complex BMS functions. This study reviews the concept and design of cloud-based smart BMSs and provides some perspectives on their functionality and usability as well as their benefits for future battery applications. The potential division between the local and cloud functions of smart BMSs is also discussed. Cloud-based smart BMSs are expected to improve the reliability and overall performance of LIB systems, contributing to the mass adoption of renewable energy.
Highlights
Since the energy demand is projected to increase significantly in the future, the need for renewable energy is as high as ever nowadays
This study provides some perspectives on the potential design, usage, benefits, and drawbacks of the cloud-based smart battery management systems (BMSs) for future battery applications
With the rise of Lithium-ion batteries (LIBs), BMSs play an important role in ensuring safety and optimizing the operation of battery applications
Summary
Since the energy demand is projected to increase significantly in the future, the need for renewable energy is as high as ever nowadays. There are more functions in BMSs that help the battery perform better and safer, including cell monitoring, battery safety and protection, state of charge (SOC) estimation, state of health (SOH) estimation, cell balancing, thermal management, and charging control [17,18]. Battery models are used in the BMS to predict working voltage, power, and energy capability, estimate SOC and SOH, detect faults, and control battery operation [19] They have a significant role in ensuring reliable performance and safety, improving the usage efficiency of the batteries, and avoiding malfunctions and catastrophic failures. The current and more modern BMS is relatively more complex and includes functions such as cell monitoring, cell balancing, battery safety and protection, state estimation, and thermal management [27,28,31]:. Vmasrsiouucshmasosdleidl-ibnagsemdomdeetohbosdesrvceurrr[e5n1]tl,yaduaspedtivine tuhnesBceMntSedutKilaizlme baanttfielrteyrm[5o2d],ealsndansdtraulcgtuorriatlhamnsalsyuscishaansdslsiedqiunegnmtiaoldreesoibdsuearlvgeern[e5r1a]t,oard[a5p3-] ttioveesutinmscaetentpeadraKmaelmtearsnofrilrteersi[d5u2a],lsantoddsetrteuccttubarattlearnyaflayuslitss.aHndowseeqvueer,nutisailnrgetshideusealmgeetnheordas-, tmoran[5y3f]atuoltesfetiamtuarteespaarreanmoettreerflseocrterdesiinduthaelsetaorldyestteacgteboafttseyrsytefamulftasi.luHroe.wSeovmere, duasitna-gdtrhiveesne mmeetthhooddss,umsianngysifganualtl fperaotcuersessinagre[5n4o,5t5r]eaflnedctmedacinhitnheeleeaarrnlyinsgta[g56e–o5f8s]yhsatveme bfeaeinluprero. pSoosmede dtoatean-dabrilveeenarmlyetahnoddsreulisainbglesfiagunlatldpertoeccetisosninbgu[t54a,t5t5h]eanexdpmenascehionfehliegahrncionmg p[5u6t–in5g8]ehffaovret. bDeueentportohpeodseadtatcooemnpabulteinegaralnydanstdorraeglieablilme fitaautlitodnsetoefcttihoenBbMutSa, tmthoreeerxeplieanbslee oafnhdiagchcucormatepreuatli-ntigmeefffoarut.ltDduieagtonothsiesdaalgtaorciothmmpsutcianngnaontdbestiomrapgleemlimenitteadti.ons of the BMS, more reliable and accurate real-time fault diagnosis algorithms cannot be implemented
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