Fault diagnosis of lithium-ion batteries based on voltage dip behavior
Chun Chang, Zhen Zhang, Zile Wang, Aina Tian, Yan Jiang, Tiezhou Wu, Jiuchun Jiang
Abstract
In recent years, the safety accidents of new energy electric vehicles have been increasing due to the failure of lithium-ion batteries. The lithium-ion battery fault diagnosis technology is critical to ensure the safe operation of electric vehicles. In this paper, we proposes a lithium-ion battery fault diagnosis method based on voltage dip behavior. The method first uses the Sparrow Search Algorithm(SSA) to optimize the Variational Modal Decomposition(VMD), then reconstructs multiple dynamic components and extracts the multi-feature parameters of the reconstructed components, and finally uses SSA to optimize Density Based Spatial Clustering of Applications with Noise(DBSCAN) for fault diagnosis. Through verification with real vehicle data and experimental data at different temperatures, this method can be applied to the operating environment of real vehicles at different temperatures, and can quickly and accurately identify abnormal cells.