SOC Estimation of Li-Po Battery Using Machine Learning and Deep Learning Methods

The aviation industry is one of the most important areas where developing technology contributes. It is important to evaluate many factors for the safe and comfortable flight of unmanned aerial vehicles (UAVs), one of the most popular areas of this industry. One of the most important of these factors is flight time estimation. Battery state of charge (SOC) plays a big role in flight time estimation. In this study, using the data obtained from the tests carried out using a lithium-polymer battery in the electric UAV engine test equipment, the SOC of the battery was estimated using deep learning like as Long-Short Term Memory (LSTM) and machine learning methods like as Support Vector Regression (SVR) and Random Forest (RF). The main reason why these methods are preferred is that they are suitable for time series analysis in the forecasting process, are trained faster, and have generalization abilities. The proposed models were compared among themselves and the simulation results were presented with graphs and tables. When the results are examined, the predicted values and true values are quite compatible. This shows that the proposed methods can be used effectively in SOC estimation.