| Title |
Analysis of Degradation Diagnosis Methods Using ESS Voltage Data |
| Authors |
전주현(Juhyeon Jeon) ; 이건호(Gun-Ho Lee) ; 김지연(Ji-Yeon Kim) ; 최상재(Sang-Jae Choi) ; 송길목(Kil-Mok Shong) |
| DOI |
https://doi.org/10.5370/KIEE.2024.73.12.2514 |
| Keywords |
ESS; Voltage data; Degradation diagnosis; Interquartile range; Z-score; Autoencoder |
| Abstract |
In this paper, methods for diagnosing degradation using ESS voltage data were analyzed. With the domestic and international market size expected to grow, the aim is to diagnose battery degradation before a fire occurs to address the fundamental issues. Voltage data, which can be applied as common data to all ESS sites, was used for statistical analysis, specifically the Interquartile range method and the Z-score method. Analysis of dozens of normal and problematic sites classified them with about 50% accuracy, but since the number of normal sites was much higher, the actual accuracy is presumed to be lower. Feeling the limitations of statistical analysis, additional analysis is being conducted using a deep learning Autoencoder model. Although data processing and preprocessing techniques are still lacking, improvements are being made to prevent ESS fires in advance. |