Estimating a battery state of charge using neural networks
- 1 November 2014
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- Vol. 86, 1-6
Abstract
This paper presents the means for estimating a battery State of Charge (SoC) using neural networks. Several neural networks such as: radial basis function (RBF), feed forward (FF) and nonlinear autoregressive with exogenous (external) input (NARX) are used for curve fitting and predicting features values. The conclusions are drawn after comparing the values obtained from the models and the data obtained from discharging a LiFePO4 (LFP) battery.Keywords
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