An incremental updating method for online monitoring state-of-health of VRLA batteries

Abstract

Valve-regulated lead acid (VRLA) batteries are widely used for backup power in the DC power system of transformer substations. Therefore, the state-of-health (SOH) of batteries is critical for maintaining the reliability of the power system. However, due to the unique structure of the VRLA battery, it is difficult to obtain accurate SOH for the short discharge process during battery maintenance. To solve this problem, using the battery equivalent circuit model of the VRLA battery, we propose the online SOH estimation algorithm based on recursive least squares. The method is based on a non-linear model function deduced from the Shepherd battery equivalent model. Moreover, we present a specific scheme for estimating parameters combined with the recursive least squares (RLS) algorithm. The model parameters are then updated using the discharge data from the battery. Finally, we can predict the SOH using the battery model. The experimental results showed that the proposed algorithm could accurately predict the SOH of VRLA batteries using less than two hours of discharge data.