Pattern Recognition for Temperature-Dependent State-of-Charge/Capacity Estimation of a Li-ion Cell

Abstract

This paper investigates the application of the dual extended Kalman filter (DEKF) algorithm combined with the pattern recognition based on the Hamming network (HN) to the identification of suitable cell model parameters for improved state-of-charge (SOC)/capacity estimation at different temperatures. The averaged nine discharging/charging voltage-temperature (DCVT) patterns for 10 fresh Li-ion cells were measured at different temperatures, together with the cell parameters, as representative patterns. Through statistical analysis of the characteristic parameters for learning by the HN, the identification of the representative DCVT pattern that most closely matches that of the arbitrary cell to be measured at any temperature. Specifically, a detailed temperature is obtained by the temperature-checking process and is added or reduced according to representative DCVT pattern discriminated. Finally, appropriate model parameters from the proposed approach are determined and used for SOC/capacity estimation in DEKF.