Backpropagation neural network models for LiFePO4 battery
- Published Online: 21 July 2016
- AIP Conference Proceedings 1755, 090009 (2016);https://doi.org/10.1063/1.4958527
Abstract
Neural Networks have been used in system control, medicine, pattern recognition and business. The backpropagation neural network (BPNN) appear to be most popular and have been widely used in many applications. BPNN is a supervised learning technique for training multilayer feedforward neural networks. The gradient or steepest descent method is used to train a BPNN by adjusting the weights. The purpose of update numerical weights is minimize error of network between target and output. In this paper, focus with BPNN modeling with data battery for training and testing. We used discharge and Urban Dynamometer Driving Schedule (UDDS) as training data and testing data, respectively Architecture of BPNN consist of input layer, hidden layer and output layer. The otherhand, using BPNN has problem to define amount of hidden neurons. In this study, we used current or voltage as input in input layer, one hidden layer with 8 neurons and one output layer. We used Levenberg-Marquardt algorithm to get fast iteration when computation. The experiment used 2200 mAh of LiFePO4 battery. Result of this research show that Mean Squared Error (MSE) value when current as input and voltage as target is 0.021135 with regresion is 0.626. Then MSE value when voltage as input and current as target 0.029925 with regresion is 0.5213. In this study relationship between voltage and current battery is nonlinear.
https://aip.scitation.org/doi/abs/10.1063/1.4958527
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