Fault Monitoring of Electric Vehicle Motor Drive Systems: Effective Application of PLC Technology

Due to the inability of existing methods to complete the transformation of nonlinear drive information, the fault detection rate of motor drive systems is relatively low. This paper proposes a method for fault detection of motor drive systems in new energy vehicles under PLC technology. Wavelet packet analysis is used to obtain the frequency band components of the drive motor, and fault characteristic parameters are extracted by analyzing the changes in each frequency band component. Based on the fault characteristics, under PLC technology, a fusion of kernel methods and principal component analysis is used to transform the nonlinear information of the motor drive fault characteristic parameters into linear information. Finally, the fuzzy kernel clustering method is applied to cluster the linearized information, completing the fault detection of the motor drive system in new energy vehicles. Experimental results show that the proposed method has a low rate of missed and false detections, enhances fault detection rates, and has strong practical application performance.