机器学习在电动汽车PMS电机速度和转矩预测中的应用

Debottam Mukherjee, Samrat Chakraborty, Pabitra Kumar Guchhait, Joydeep Bhunia
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引用次数: 6

摘要

永磁同步电机在电动汽车中有着广泛的应用。因此,为了得到满意的结果,需要对转速和转矩进行正确的预测。一个数据集被认为具有环境温度、冷却剂温度、直轴和正交轴电压和电流、轭架温度、转子温度和定子温度的实时数据,用于预测电机的速度和转矩。本数据集来自帕德本大学实验室的试验台。在数据集上应用了各种机器学习模型。结果表明,Fine Tree是预测永磁同步电机转速和转矩的最佳模型,预测转速和转矩的RMSE最低,分别为0.029224和0.052538。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Application of Machine Learning for Speed and Torque Prediction of PMS Motor in Electric Vehicles
Permanent Magnet Synchronous (PMS) motor has huge applications in Electric Vehicles. Therefore, a correct prediction of both speed and torque is required for satisfactory result. A dataset is considered having real time data of ambient temperature, coolant temperature, direct axis and quadrature axis voltage and current, yoke temperature, rotor temperature and stator temperature for prediction of motor speed and torque. This dataset is collected from the test bench of University of Paderbon laboratory. Various machine learning models have been applied on the dataset. The result shows that Fine Tree is the best model for prediction of both speed and torque of the permanent magnet synchronous motor having lowest RMSE of 0.029224 and 0.052538 for prediction of speed and torque respectively.
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