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A Multiple Model Adaptive Estimation (MMAE) based approach of fault diagnosis for Li-Ion battery is illustrated in this paper. Electrochemical modelling approach is integrated with MMAE for fault diagnosis. This real physics based model of Li-ion battery (with Li-Co-O2 cathode chemistry) with nominal model parameters is considered as the healthy battery model. Battery fault conditions such as aging, overcharge and over discharge causes 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.

Electrochemical Model Based Fault Diagnosis of Lithium Ion Battery