Electrochemical model based fault diagnosis of a lithium ion battery using multiple model adaptive estimation approach

In this paper, we present an innovative approach in detecting fault conditions in a battery in which multiple model adaptive estimation (MMAE) technique is applied using electrochemical model of a Li-Ion cell. This physics based model of Li-ion battery (with LiCoO₂ cathode chemistry) with healthy battery parameters was considered as the reference model. Battery fault conditions such as aging, overcharge, and over discharge cause significant variations of parameters from nominal values and can be considered as separate models. Output error injection based partial differential algebraic equation (PDAE) observers are used to generate the residual voltage signals. These residuals are then used in MMAE algorithm to detect the ongoing fault conditions of the battery. Simulation results show that the fault conditions can be detected and identified accurately which indicates the effectiveness of the proposed battery fault detection method.