Model Based Thermal Fault Diagnosis of Lithiumion Batteries Using Extended Kalman Filte

This paper proposes a novel thermal fault diagnosis method of lithium-ion battery where Extended Kalman filter (EKF) has been applied. Thermal fault generally occurs due to sudden heat generation in the core or internal parameter variation of the battery. As a result, core temperature as well as surface temperature of the battery has been changed. But it is very difficult to measure battery core temperature directly, so EKF is used to estimate core as well as surface temperature of the battery accurately. To implement EKF, an electro-thermal discretized model of cylindrical lithium-ion battery cell is developed where estimated data by EKF is compared with the measured data. The residual signal is generated which is the difference between the measured and the estimated temperature from battery. Under normal operating condition, the residual signal does not indicate zero mean value, but when thermal fault occurs, residual signal shows non-zero mean value indicating the occurrences of fault. Finally, battery performance is tested under noisy environment by changing variance of the noisy data, and EKF diagnose the thermal fault effectively compared to previous methods.