MAPPING OF ELECTROCHEMISTRY AND NEURAL NETWORK MODEL APPLIED IN STATE OF CHARGE ESTIMATION FOR LEAD ACID BATTERY USED IN ELECTRIC VEHICLE

Analytical models have been developed to diminish test procedures for product realization, but they have only been partially successful in predicting the performance of battery systems consistently. The complex set of interacting physical and chemical processes within battery systems has made the development of analytical models of significant challenge. Advanced simulation tools are needed to be more accurately model battery systems which will reduce the time and cost required for product realization. As an alternative approach begun, the development of cell performance modeling using non-phenomenological models for battery systems were based on artificial neural networks (ANN) using Matlab 7.6.0(R2008b). ANN has been shown to provide a very robust and computationally efficient simulation tool for predicting state of charge for Lead Acid cells under a variety of operating conditions. In this study, the analytical model and the neural network model of lead acid battery for electric vehicle were used to determinate the battery state of charge. A precision comparison between the analytical model and the neural network model has been evaluated. The precise of the neural network model has error less than 0.00045 percent.