Phase resistance estimation and monitoring of PMSM used in electrical vehicles

• Published in: 2014 18th International Conference on System Theory, Control and Computing (ICSTCC) pp. 512 - 519
• Main Authors: Mocanu, Razvan, Onea, Alexandru
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.10.2014
• Subjects: Covariance matrices; Estimation; extended Kalman filter; Goertzel filter; Jacobian matrices; Kalman filters; Mathematical model; Monitoring; open phase fault detection; permanent magnet synchronous motor; phase resistance estimation; Resistance; unscented Kalman filter
• DOI: 10.1109/ICSTCC.2014.6982468

Permanent magnet synchronous motors (PMSM) are widely used in electric vehicle application due to their high power density, high efficiency and very good performance at low speeds. Safety concerns in automotive industry require monitoring of the system to ensure a correct and safe operation of the electric vehicle. This paper presents three software methods for estimating the winding electrical resistance and detecting an open phase failure during torque control operation of a PMSM. All three methods can be applied online. Differences between these methods are reflected into computational effort and efficiency. The described methods were numerically simulated and tested in MATLAB. The first two methods monitor the electrical resistance of the stator windings which are estimated through an Extended Kalman Filter (EKF) and an Unscented Kalman Filter (UKF). Hence, the three phase model and the rotating reference frame model are both used as internal models for the proposed Kalman filters and the estimation performance is evaluated in each case. In order to detect an open phase fault, the gradient of the estimated electrical resistance is monitored. The third method which is independent of the motor parameters calculates the Discrete Fourier Transformation (DFT) coefficients of the fundamental frequency in the phase currents signals. Hence, due to the low computational effort and good performance, the Goertzel algorithm is proposed. Conclusions are drawn based on estimation accuracy, time response and complexity of each method.