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Performance Analysis of Machine-Learning Approaches for Modeling the Charging/Discharging Profiles of Stationary Battery Systems with Non-Uniform Cell Aging

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School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798, Singapore
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Unit Energy Technology, Vlaamse Instelling voor Technologisch Onderzoek, 2400 Boeretang, Belgium
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Energy Research Institute @ NTU, Nanyang Technological University, Singapore 639798, Singapore
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Engineering Cluster, Singapore Institute of Technology, Singapore 138682, Singapore
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Author to whom correspondence should be addressed.
Academic Editor: Harry E. Hoster
Batteries 2017, 3(2), 18; https://doi.org/10.3390/batteries3020018
Received: 4 April 2017 / Revised: 15 May 2017 / Accepted: 18 May 2017 / Published: 11 June 2017
The 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. View Full-Text
Keywords: stationary battery systems; charging/discharging profile; capacity loss; machine-learning approaches stationary battery systems; charging/discharging profile; capacity loss; machine-learning approaches
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Kandasamy, N.K.; Badrinarayanan, R.; Kanamarlapudi, V.R.K.; Tseng, K.J.; Soong, B.-H. Performance Analysis of Machine-Learning Approaches for Modeling the Charging/Discharging Profiles of Stationary Battery Systems with Non-Uniform Cell Aging. Batteries 2017, 3, 18.

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