Fault Diagnosis of Lithium-Ion Battery Pack Based on Hybrid System and Dual Extended Kalman Filter Algorithm

For guaranteeing the performance and safety of battery systems, a valid fault diagnostic method is quite essential. This article presents a systematic fault diagnostic scheme based on a hybrid system for the typical faults of lithium-ion battery packs, including sensor faults and relay faults. The automata are established based on a hybrid system theory to simultaneously capture the continuous dynamics and discrete dynamics of the battery pack. The distributed diagnostic structure is adopted to avoid constructing the global model of the battery pack so that the computation burden can be reduced. A dual extended Kalman filter algorithm is developed to estimate the parameters, terminal voltages, and state of charges (SOC) of each cell in the battery pack. The current, terminal voltage residual, and SOC residual are used to implement the distinguishability analysis. The diagnostic scheme is implemented based on both the observation of the events and the distinguishability analysis of the continuous dynamics. The performance of the diagnostic method is validated for the battery pack that contains two batteries in series connection and two branches in parallel connection through the Federal Urban Driving Schedule driving cycle.