Discrimination of Li-ion batteries based on Hamming network using discharging–charging voltage pattern recognition for improved state-of-charge estimation

Differences in electrochemical characteristics among Li-ion batteries and factors such as temperature and ageing result in erroneous state-of-charge (SoC) estimation when using the existing extended Kalman filter (EKF) algorithm. This study presents an application of the Hamming neural network to the identification of suitable battery model parameters for improved SoC estimation. The discharging–charging voltage (DCV) patterns of ten fresh Li-ion batteries are measured, together with the battery parameters, as representative patterns. Through statistical analysis, the Hamming network is applied for identification of the representative DCV pattern that matches most closely of the pattern of the arbitrary battery to be measured. Model parameters of the representative battery are then applied to estimate the SoC of the arbitrary battery using the EKF. This avoids the need for repeated parameter measurement. Using model parameters selected by the proposed method, all SoC estimates (off-line and on-line) based on the EKF are within ±5% of the values estimated by ampere-hour counting.