面向集成一致性的电池储能系统异常检测：条件驱动的集成平衡表示学习方法

Abstract In the battery energy storage systems (BESS), multiple lithium-ion battery (LIB) cells are consolidated into a LIB module for scalable management. Normally, LIB cells within the same module are deemed to exhibit consistency acting as an ensemble. For the reliable monitoring of LIB cells, it is considerably challenging to capture the overall working status of LIB cells meanwhile maintaining the awareness of the consistency among each cell. Additionally, the nonstationary characteristics of LIB cells arising from charging, discharging, and other behaviors pose more difficulties for anomaly detection . In this study, we propose a condition-driven ensemble balance representation learning and anomaly detection method to address those challenges, introducing the concept of ensemble analysis for the first time in the field of LIB anomaly detection. Specifically, an ensemble balance representation learning strategy is developed for LIB cells, primarily consisting of two aspects. First, a dual-layer health (DLH) feature learning approach is proposed to provide a representation of the status of LIB cells, which considers LIB cell’s operation characteristics and the interaction with others. Second, an ensemble balance component analysis (EBCA) method is designed for DLH features to uncover the inherent balance relationship between LIB cells. This approach allows us to monitor the overall working status of LIB cells within the module while maintaining sensitivity to detecting individual LIB cell anomaly. Further, considering the influence of nonstationary characteristics, we develop a condition-driven mode partition strategy to uncover multiple condition modes from the nonstationary operation process of the LIB cells, where the EBCA model is established for each mode. The effectiveness of the proposed method is demonstrated through real operation processes of LIB cells in a BESS.