UKF-based Sensor Fault Diagnosis of PMSM Drives in Electric Vehicles
UKF-based Sensor Fault Diagnosis of PMSM Drives in Electric Vehicles
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DOI：
10.1016/j.egypro.2017.12.630
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发表时间：
2017-12
期刊：
影响因子：
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通讯作者：
Nana Zhou;Hongwen He;Zhentong Liu;Zheng Zhang
中科院分区：
文献类型：
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作者：
Nana Zhou;Hongwen He;Zhentong Liu;Zheng Zhang
永磁同步电机（PMSM）的可靠性对于新能源汽车，特别是纯电动汽车的高性能化运行要求非常高。提出了一种新的诊断方案，使用三个无迹卡尔曼滤波器（UKFs）检测和隔离永磁同步电机驱动系统的电流传感器和位置传感器故障。在Matlab/Simulink中建立了永磁同步电机的驱动模型。在故障诊断过程中，三个UKF被用于故障诊断过程中，每个UKF接收不同的传感器信息。由于不同的故障对每个UKF的影响不同，因此使用这些UKF可以有效地隔离所有故障。结果表明，该方法能较好地处理非线性特性，具有较好的鲁棒性和较高的诊断精度。
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.