Online State-of-Health Estimation of VRLA Batteries Using State of Charge

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.