An intelligence-based state of charge prediction for VRLA batteries

Abstract

A battery management system (BMS) has three main functions, voltage monitoring, current discharge monitoring and remaining life monitoring. This paper primarily focuses on remaining life monitoring through the estimation of battery's state of charge (SOC). An Experimental set-up was prepared to measure the Valve-Regulated Lead-Acid (VRLA) battery's SOC under different operating conditions. Backpropagation (BP) neural network to estimate the battery's SOC using the experimental data. The results showed a satisfactory estimation of battery's SOC with a small (4.25%) root mean square perdition error (RMS).