Health Monitoring of Lithium-Ion Batteries Using Dual Filters

Accurate estimation of a battery’s capacity is critical for determining its state health (SOH) and retirement, as well to ensure reliable operation. In this paper, dual filter architecture using the Kalman (KF) novel sliding innovation (SIF) was implemented estimate charge (SOC) lithium-ion battery. NASA’s Prognostic Center Excellence (PCOE) B005 battery data set selected experiment based on wide use in academia industry. This dataset contains cycling 2 Ah until measured at 1.3 or less. The polarity equivalent circuit model (DP-ECM) modeling. parameter values were estimated least squares (LS) algorithm. Under normal operating conditions, both dual-KF dual-SIF performed similarly terms accuracy. However, an uncertainty case considered where filters subjected rapid changing dynamics by cutting 300 cycles. case, root-mean-square error (RMSE) proposed 0.1233 0.0675, respectively. rapidly faulty shows better convergence robustness disturbances.