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

作者：Kim Jonghoon^*; Lee Seongjun; Cho B H

来源：Journal of Power Sources, 2011, 196(4): 2227-2240.

DOI：10.1016/j.jpowsour.2010.08.119

• Hamming network
• Pattern recognition
• State of charge (SoC)
• Lumped parameter battery model
• Li ion battery

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 la-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 parame

• 出版日期2011-2-15