Machine learning approaches for assessing rechargeable battery state-of-charge in unmanned aircraft vehicle-eVTOL

The long stability of electric vertical take-off and landing (eVTOL) aircraft is majorly associated with energy storage devices like batteries. Lithium-ion battery (LIB) is frequently used battery in most of eVTOL because they have high charge storage capacity, good health of battery and long-life cycles. To maintain the health of battery, the state-of-charge (SoC) and state-of-health (SoH) are the most important parameters. This study demonstrates the SoC evaluation of batteries in eVTOL aircrafts and then forecasts SoC of batteries using different machine learning (ML) approaches such as Support Vector Regression, Random Forest, Linear Regression. The experimental dataset was collected by an open portal at Carnegie Mellon University wherein over 15 million records including a hundred charge/discharge cycles, and several working conditions are available. SoC of batteries was first calculated by using collected batterie's dataset. Input parameters for SoC forecasting by ML models were prepared with different features such as voltage, current, charging/discharging energy and temperature. By feature selection analysis, EDischarge and voltage were found to be the most effective features for SoC of battery. The experimental dataset was first split into 80% of training and 20% of testing and then applied for three ML models (Support Vector Regression, Random Forest, Linear Regression). As compared to other ML models, Random Forest presented the best performance having the lowest error values (RMSE ≈ 0.000985, R2 = 0.9996) due to non-linear relationship between every feature and SoC. The studies suggested that ML approach for battery's SoC forecasting would provide promising methods to manage the health of battery for eVTOL aircraft.