Model-based co-diagnosis method of internal short circuit fault and capacity for Lithium-ion batteries

Abstract

This thesis proposed a sequential method for co-estimating the internal short cir cuit faults and capacity of lithium-ion batteries with high accuracy. Two equivalent circuit models (ECMs) are used, and the model parameter lookup tables are obtained using the RLS algorithm and open circuit voltage (OCV) calculation formula. After that, the SOC is estimated by applying the extended Kalman filter (EKF) and OCV SOC curve to the ECMs, and the internal short circuit faults and capacity of the bat tery are estimated. Therefore, the maximum error of internal short circuit estimation is 9.67% and capacity estimation error is under 3.15%.The algorithm was developed in MATLAB and was verified by a experimental dataset obtained with dynamic cur rent profiles and constant current. Through the proposed algorithm, it is possible to accurately estimate the internal short circuit and capacity of the battery under various environmental conditions.