Adaptive Neural Observer for Short Circuit Fault Estimation of Lithium-Ion Batteries in Electric Vehicles
Yiming Xu, Xiaohua Ge, Weixiang Shen
Abstract
Soft short circuit (SC) fault diagnosis is critical for a battery management system to prevent thermal runaway of lithium-ion batteries in electric vehicles. In this article, an adaptive neural network based observer is developed to estimate soft SC faults based on an augmented battery model subject to unknown nonlinear uncertainties. Rigorous theoretical analysis in terms of estimation error convergence is then provided. Leveraging the designed observer, a delicate diagnosis algorithm is presented to timely detect SC fault occurrences and an iterative updating method is further applied to accurately estimate the SC fault resistance via thresholding. Finally, comprehensive experimental tests and comparative studies are elaborated to validate the effectiveness and superiority of the proposed algorithm.