Nonlinear Model Based Fault Detection of Lithium Ion Battery Using Multiple Model Adaptive Estimation

In this paper, an adaptive fault diagnosis technique is used for fault detection in Lithium ion batteries. The monitoring setup consists of multiple models representing the different degree of parameter shift due to over-discharge in the Lithium ion battery. A recursive least square estimator along with equivalent circuit methodology is used to construct the non-linear battery models. Extended Kalman filters are used to generate the estimated terminal voltages for each system. The residuals are further evaluated using the conditional probability evaluation function, to generate probabilities that determine the presence of a particular operational condition. Using experimental data, it is shown that Li-ion battery performance shift due to over-discharge can be accurately detected in real time.