SOC/capacity estimation of a Li-Ion battery based on pattern recognition at different temperature

Differences in electrochemical characteristics among Li-Ion batteries caused by temperature effect result in erroneous state-of-charge (SOC)/capacity estimations when using the existing dual extended Kalman filter (DEKF) algorithm is used. Therefore, this study presents an application of the DEKF based on pattern recognition combined with empirical parameters measurement for high accuracy SOC/capacity estimations at various temperatures. The averaged 9 discharging/charging voltage-temperature (DCVT) patterns for 10 fresh Li-Ion batteries at experimental temperatures are measured as representative patterns, together with battery model parameters such as capacity, open circuit voltage (OCV), and two resistances (R_i, R_Diff). Through statistical analysis, the Hamming network is applied for identification of the representative pattern that matches most closely of the pattern of the arbitrary battery to be measured at any temperature. Based on temperature-checking process, model parameters for representative DCVT pattern can be then applied to estimate SOC/capacity of an arbitrary battery using the DEKF. This avoids the need for repeated parameter measurement.