Abstract
Electric vehicles (EVs) are now having a great interest, not only from researchers or manufacturers but from governments and people. Therefore, research and development for this type of vehicle are very important and even reach the point of necessity. The thermal performance of this type of these vehicles is very important and it needs to be studied because it has a great impact on the efficiency of these vehicles entirely. Therefore, this work extracts the heat of the most vital part of these vehicles, which is the electric motor. Now, the most common installed motor for EVs is the permanent magnet synchronous motor (PMSM). Sometimes it is hardly to install sufficient sensors to measure the temperature of the motor accurately, but through measurements of current and by referring to the specifications of the motor, we can expect the temperatures for this engine. For a large number of experiments, all feasible measurements were taken, and the results were saved to later become the data store required for the prediction process. Several machine learning tools have been used to get the best temperature prediction such as extra-tree, bagging k-nearest neighbors (KNN), voting regressor, random forest, and boosting algorithms.
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Al-Gabalawy, M., Elmetwaly, A.H., Younis, R.A. et al. Temperature prediction for electric vehicles of permanent magnet synchronous motor using robust machine learning tools. J Ambient Intell Human Comput 15, 243–260 (2024). https://doi.org/10.1007/s12652-022-03888-9
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DOI: https://doi.org/10.1007/s12652-022-03888-9