http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
Yue Jiahui,Xia Xiangyang,Zhang Yuan,Xia Tian 대한전기학회 2023 Journal of Electrical Engineering & Technology Vol.18 No.3
In order to enrich the comprehensive estimation methods for the balance of battery clusters and the aging degree of cells for lithium-ion energy storage power station, this paper proposes a state-of-health estimation and prediction method for the energy storage power station of lithium-ion battery based on information entropy of characteristic data. This method optimizes and processes attribute data based on specific running segments to form a characteristic data set, and adopts the information entropy value to reflect the orderliness of characteristic data to analyze the balance of battery clusters and the aging degree of cells in it. At the same time, the BP neural network is used to predict the information entropy value to achieve short-term prediction of the station’s health state. The feasibility and effectiveness of the health state estimation and prediction method proposed in this paper are demonstrated using actual data collected from the lithium-ion battery testing platform and the energy storage power station.