Multimodal Data-Driven Abnormal Condition Detection for Energy Storage Batteries Using EAA-Informer

To enhance the operational safety of energy storage batteries under complex conditions, this study proposes a multimodal data-driven abnormal condition identification method based on Enhanced Attention Allocation (EAA) Informer.First, an analysis of thermal experimental data under various abnormal conditions reveals that temperature changes during the early stages are often subtle. As a result, relying on a single temperature parameter is insufficient to accurately reflect the battery state. This requires the integration of multiple parameters into a joint modeling framework to improve identification accuracy. Subsequently, a multimodal temporal feature extraction network based on LSTM-MFM is constructed. This network enables deep modeling of multi-source data—such as voltage, current, temperature, and state of charge—and facilitates cross-modal correlation learning.To further enhance the model's ability to capture both critical time steps and long-range temporal dependencies, an EAA mechanism is introduced for feature optimization. This is followed by an improved Informer network to perform temporal modeling and abnormal condition identification. Experimental results show that the proposed method accurately identifies abnormalities with low false rates, highlighting its potential for energy storage system safety monitoring.