Haoyi Zhong, Yongjiang Zhao, Chang Gyoon Lim*
CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1757-1781, 2024, DOI:10.32604/cmes.2024.049208
- 20 May 2024
Abstract This paper addresses the challenge of identifying abnormal states in Lithium-ion Battery (LiB) time series data. As the energy sector increasingly focuses on integrating distributed energy resources, Virtual Power Plants (VPP) have become a vital new framework for energy management. LiBs are key in this context, owing to their high-efficiency energy storage capabilities essential for VPP operations. However, LiBs are prone to various abnormal states like overcharging, over-discharging, and internal short circuits, which impede power transmission efficiency. Traditional methods for detecting such abnormalities in LiB are too broad and lack precision for the dynamic and… More >
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