Because of the fast technological advances, there is a change in people s habits and needs. There is an increasing dependence on electronic devices such as smartphones, notebooks etc. Building batteries with great energy capacity is one of the current challenges to increase the autonomy of electronic devices. However, an alternative that can help keep electronic devices longer away from sockets is battery swap. There are many studies in the literature involving the sharing of batteries in the context of electric vehicles, but no applications are found in smartphones. An important parameter to be monitored in this context is state of health (SoH). To date, there is no consensus in the literature about the best model for estimating battery SoH due to the lack of well-established methods. Thus, the objective of this dissertation is to build a model to estimate the state of health curve, with a view to classifying the health of lithium-ion batteries, through state of charge curve, for applications involving battery swap aiming to use in smartphones. The proposed model was based on recurrent neural networks. To train and validate the model, a system was built to perform destructive tests, being possible to study the behavior of lithium-ion batteries throughout its useful life. The proposed model was able to estimate the SoH of the batteries studied with good precision, under different charge / discharge parameters. The distinction of the model is low computational complexity, even involving neural network models, and easy-to-measure input parameters are adopted.