Fault diagnosis of Li-Ion batteries using multiple-model adaptive estimation

In this paper a battery fault detection unit is developed using multiple model adaptive estimation technique. Impedance spectroscopy data from Li-ion cell is used along with the equivalent circuit methodology to construct the battery models. Battery faults such as over charge and over discharge cause significant model parameter variation and can be considered as separate models. Kalman filters are used to estimate the parameters of each model and to generate the residual signal. These residuals are used in the multiple model adaptive estimation technique to detect battery faults. Simulation results show that using this method the stated battery faults can be detected in real-time, thus providing an effective way of diagnosing Li-Ion battery failure.