摘要：Lithium-ion battery energy storage systems have achieved rapid development and are a key part of the achievement of renewable energy transition and the 2030 “Carbon Peak” strategy of China. However, due to the complexity of this electrochemical equipment, the large-scale use of lithium-ion batteries brings severe challenges to the safety of the energy storage system. In this paper, a new method, based simultaneously on the concepts of statistics and density, is proposed for the potential failure prediction of lithium-ion batteries. As there are no strong assumptions about feature independence and sample distribution, and the estimation of the anomaly scores is conducted by integrating several trees on the isolation path, the algorithm has strong adaptability and robustness, simultaneously. For validation, the proposed method was first applied to two artificial datasets, and the results showed that the method was effective in dealing with different types of anomalies. Then, a comprehensive evaluation was carried out on six public datasets, and the proposed method showed a better performance with different criteria when compared to the conventional algorithms. Finally, the potential failure prediction of lithium-ion batteries of a real energy storage system was conducted in this paper. In order to make full use of the time series characteristics, voltage variation during a whole discharge cycle was taken as the representation of the operation condition of the lithium-ion batteries, and three different types of voltage deviation anomalies were successfully detected. The proposed method can be effectively used for the predictive maintenance of energy storage systems.
