Real-time monitoring of battery state of charge using artificial neural networks

The main highlight of this paper is to present the implementation of a battery health augury system using artificial neural networks (ANNs). We were able to predict the amount of charge in the battery only by taking preliminary parameters like voltage, current and ambient temperature of the battery, in the growing field of portable energy storage (ES) technology. An optimal solution was demanded to increase the age of the battery. We were able to predict the state of the battery in terms of charge for its performance. A feed forward neural network (FFNN) and long short-term memory (LSTM) neural nets were used for the implementation whose prediction accuracy is acceptable for this application. An optimal data-driven model for FFNN and LSTM for the prediction which made sure that our neural network (NN) models can be used for the estimation of charge in the battery for any charging cycle based on the preliminary parameters. These results can also help us to extend the implementation by estimating the health condition of the battery and also to be used for real-time monitoring and prediction of the performance characteristics and to improvise various NN methods in these fields for perfect accuracy in predicting the performance.