Machine-Learning-Based Accurate Prediction of Vanadium Redox Flow Battery Temperature Rise Under Different Charge–Discharge Conditions

Abstract

Accurate prediction of battery temperature rise is very essential for designing efficient thermal management scheme. In this paper, machine learning (ML)-based prediction of vanadium redox flow battery (VRFB) thermal behavior during charge–discharge operation has been demonstrated for the first time. Considering different currents with a specified electrolyte flow rate, the temperature of a kW scale VRFB system is studied through experiments. Three different ML algorithms; linear regression (LR), support vector regression (SVR), and extreme gradient boost (XGBoost) have been used for prediction. The training and validation of ML algorithms have been done by the practical dataset of a 1 kW 6 kWh VRFB storage under 40 , 45, 50, and 60 A charge–discharge currents and 10 L min⁻¹ of flow rate. A comparative analysis among ML algorithms is done by performance metrics such as correlation coefficient (R²), mean absolute error (MAE), and root mean square error (RMSE). XGBoost shows the highest R² value of around 0.99, which indicates its higher prediction accuracy compared to other ML algorithms used. The ML-based prediction results obtained in this work can be very useful for controlling the VRFB temperature rise during operation and act as an indicator toward further development of an optimized thermal management system.