The state-of-health diagnosis of Li-Co batteries with fuzzy identification

This paper employs fuzzy identification for diagnosing battery state of health (SOH) to avoid the inconvenience of battery failure. First, this study performs a life-cycle test of 95 Li-Co batteries with five various discharge currents in order to understand the most important factors that affect battery SOH. The batteries are charged with 0.5C constant-current and constant-voltage hybrid charging, and are discharged with 0.2, 0.4, 0.6, 0.8, and 1 C. The experimental results show that the charging time, the voltage difference between the open circuit and with load, and the voltage change of the battery between the voltage under full discharge and the voltage after the rest for 1 minute can be used to accurately diagnose battery SOH. Since each battery is tested with 300 cycles, the total patterns obtained from the experimental results are 95×300. 1,835 patterns are randomly selected for the fuzzy identification. 60 patterns are also randomly selected for verification. The principle of fuzzy identification as it applies to battery-health diagnosis is based on the principle of closest normal distribution. The average error of the good diagnosis is 1.46%, and the diagnosis standard deviation is 2.36%. The average error with the poor diagnosis and the diagnosis standard deviation are 6.45% and 6.83%, respectively. The results show that the proposed method can accurately diagnose battery health, and thus the state of charge can be more precisely predicted.