Comparative investigation of the usability of different machine learning algorithms in the analysis of battery thermal performances of electric vehicles

Abstract

The cooling and thermal management of battery packs, which are the most important components used in electric vehicles (EV), are of critical importance for the efficiency and performance of EV. This study aims to analyze the usability of machine learning algorithms in determining the thermal parameters of the battery thermal management system (BTMS) used in EV and to determine the machine learning algorithm with the highest prediction performance. The prediction performance of three different artificial neural networks developed by using Levenberg-Marquardt, Bayesian Regularization (BR), and Scaled Conjugate Gradient (SCG ) machine learning algorithms have been extensively and comparatively investigated. As input parameters of the models, discharge rate, flow rate, and inlet temperature values were defined and the average temperature of the battery surface and maximum temperature difference on the surface values were estimated. The coefficient of determination values for the Levenberg-Marquardt, BR, and SCG algorithms was calculated as 0.99848, 0.98751, and 0.97592, respectively. The results showed that the machine learning algorithms can determine the thermal parameters of the BTMS of EV with high accuracy. However, it has been observed that the highest prediction accuracy belongs to the Levenberg-Marquardt algorithm.