This paper presents an online method for the estimation of the state of health (SOH) of valve-regulated lead acid (VRLA) batteries. The proposed method is based on the state of charge (SOC) of the battery. The SOC is estimated using the extended Kalman filter and a neural-network model of the battery. Then, the SOH is estimated online based on the relationship between the SOC and the battery open-circuit voltage using fuzzy logic and the recursive least squares method. To obtain the open-circuit voltage while the battery is operating, the reflective charging process is employed. Experimental results show good estimation of the SOH of VRLA batteries.

State of Health (SOH) is an important characteristicin determining the overall profile of a battery. SOH is a measure of the remaining full charge capacity of a battery with respect to its nominal capacity. As continuously charged and discharged and battery ages, the chemical composition starts to degrade. This paper describes a novel online method to determine the SOH of a battery. In the proposed method, the SOH of ValveRegulated Lead-Acid (VRLA) batteries is estimated using the relation between the State of Charge (SOC) and the battery opencircuit terminal voltage (Voc ). This estimation is performed using the least-square method and the fuzzy logic. Experimental results show good estimation of the SOH in relatively short time.

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