Data-driven strategy: A robust battery anomaly detection method for short circuit fault based on mixed features and autoencoder

The anomaly detection of lithium-ion batteries for short circuit (SC) faults is crucial to ensure the safety of the energy storage system. Compared to the diagnosis fault of packs, individual cell fault diagnosis lacks a reference target, leading to difficulties in effectively detecting whether an abnormality exists. In this paper, a data-driven detection method based on the autoencoder strategy is proposed for early detection of battery faults without pack information. Within, the autoencoder strategy is used to reconstruct the voltage and detect potential faults. Using the generative adversarial network (GAN) framework for model training reduces its overfitting and improves efficiency. In addition, during anomaly detection, due to the lack of battery pack reference, some abnormal voltage changes due to current variations can lead to misdiagnosis. To address this concern, the mixed features input is proposed to reduce the misdiagnosis rate, which incorporates the equivalent circuit model parameters. Experiments demonstrate that the proposed method can accurately detect SC faults, in particular, it can detect some moderate or weak faults within 1.6 h. Compared to other methods, this method has better effectiveness and robustness. The method proposed in this paper is in line with the development trend for big data and opens up new perspectives for the development of energy storage safety technology.