Prediction of Lead-Acid Battery Performance Parameter: An Neural Network Approach

Abstract

In real-time applications life of lead-acid battery are affected by many factors such as state of charge, rate of charging /discharging, temperature and aging. If these factors of battery are frequently encountered thought-out the lifecycle, battery performance degradation is identified. Hence, in this communication a valve regulated lead-acid batteries (VRLA) electrical behavior are  modeled using MATLAB/SIMULINK and the performance parameters related to the  battery such as  internal resistance (R), state of charge (SOC), and capacity under various operating conditions are predicted using artificial neural network (ANN). The relevant simulation results are compared with experimental results. A validation result shows that this model can accurately simulate the dynamic behavior of the lead-acid battery for any different experimental data sets. This paper describes initial feasibility studies as well as current models and makes comparisons between predicted and actual performance.