UKF-based Sensor Fault Diagnosis of PMSM Drives in Electric Vehicles. (December 2017)

• Record Type: Journal Article
• Authors: Zhou, Nana; He, Hongwen; Liu, Zhentong; Zhang, Zheng
• Is Part Of: Energy procedia. Volume 142(2017)
• Page Start: 2276
• Page End: 2283
• Publication Date: 2017-12
• Subjects: Fault diagnosis -- PMSM -- UKF -- Current sensor -- Rotor position sensor; Power resources -- Congresses; Power resources -- Periodicals; Power resources; Conference proceedings; Periodicals; 333.7905
• DOI: 10.1016/j.egypro.2017.12.630
• Languages: English
• ISSNs: 1876-6102

Abstract

Abstract: The reliability of permanent magnet synchronous machine (PMSM) is very important for new energy vehicle, especially for pure electric vehicle which requires a precise operation to achieve high performance. This paper proposes a novel diagnosis scheme that uses three unscented Kalman Filters (UKFs) to detect and isolate current sensor and position sensor faults of PMSM drive system. The PMSM drive model is built in Matlab/Simulink. In the process of fault diagnosis, three UKFs are used in the fault diagnosis process, and each UKF receives different sensor information. All faults can be efficiently isolated by using these UKFs as different faults affect each UKF differently. From the results we got, it is conclude that the proposed methodology could properly handle the nonlinear properties with good robustness and high diagnosis accuracy.