Abstract
he development of cutting-edge motor technologies for electric vehicles (EVs) has been fueled by the global movement towards sustainable mobility. Because of its great efficiency, power density, and flexibility to operate at various speeds, Permanent-Magnet Synchronous Motors (PMSMs) have become the go-to option for small-class EVs. However, the accuracy and applicability of standard mathematical modeling techniques are limited because they frequently miss real-world nonlinearities and parameter fluctuations. In this paper, a data-driven methodology for PMSM modeling that is tailored for small-class EV applications is presented. The suggested method improves the precision, flexibility, and computational efficiency of PMSM models by utilizing real-world operational data. The efficiency map and peak torque characteristics of the WULING MACARON EV's electric drive were defined using regression analysis, which produced low residual errors and high R-squared values of 0.9647 and 0.9789, respectively. The outcomes show that the model can optimize energy use, forecast performance under various circumstances, and assist real-time control applications.
Published Version
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