Battery Temperature Rate of Change Estimation by Using Machine Learning

In work, the process of monitoring of the electric variable on a 14 Ah prismatic LiFePO4 battery has been carried out. The variables monitored include electric current, voltage, energy and internal resistance to be analyzed for its effect on the temperature variable on the battery. An analysis of the relationship between the increase of temperature and the efficiency of energy has also been done. This process succeeded in getting the electrothermal value or heat arising from the electric variable in the battery. In the end, the values obtained would be processed using machine learning with SVM and Random Forest methods.