dc.contributor.authorKandasamy, Nandha
dc.contributor.authorBadrinarayanan, Rajagopalan
dc.contributor.authorKanamarlapudi, Venkata
dc.contributor.authorTseng, King
dc.contributor.authorSoong, Boon Hee
dc.identifier.citationKandasamy, N., Badrinarayanan, R., Kanamarlapudi, V., Tseng, K.,& Soong, B. H. (2017). Performance Analysis of Machine-Learning Approaches for Modeling the Charging/Discharging Profiles of Stationary Battery Systems with Non-Uniform Cell Aging. Batteries, 3(2), 18-.en_US
dc.description.abstractThe number of Stationary Battery Systems (SBS) connected to various power distribution networks across the world has increased drastically. The increase in the integration of renewable energy sources is one of the major contributors to the increase in the number of SBS. SBS are also used in other applications such as peak load management, load-shifting, voltage regulation and power quality improvement. Accurately modeling the charging/discharging characteristics of such SBS at various instances (charging/discharging profile) is vital for many applications. Capacity loss due to the aging of the batteries is an important factor to be considered for estimating the charging/discharging profile of SBS more accurately. Empirical modeling is a common approach used in the literature for estimating capacity loss, which is further used for estimating the charging/discharging profiles of SBS. However, in the case of SBS used for renewable integration and other grid related applications, machine-learning (ML) based models provide extreme flexibility and require minimal resources for implementation. The models can even leverage existing smart meter data to estimate the charging/discharging profile of SBS. In this paper, an analysis on the performance of different ML approaches that can be applied for lithium iron phosphate battery systems and vanadium redox flow battery systems used as SBS is presented for the scenarios where the aging of individual cells is non-uniform.en_US
dc.format.extent15 p.en_US
dc.rightsThis is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).en_US
dc.subjectstationary battery systemsen_US
dc.subjectcharging/discharging profile;en_US
dc.titlePerformance Analysis of Machine-Learning Approaches for Modeling the Charging/Discharging Profiles of Stationary Battery Systems with Non-Uniform Cell Agingen_US
dc.typeJournal Article
dc.contributor.researchEnergy Research Institute @NTUen_US
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.description.versionPublished versionen_US

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