Improved Fusion of Permanent Magnet Temperature Estimation Techniques for Synchronous Motors Using a Kalman Filter

In this paper, a new temperature observer topology is presented which overcomes the shortcomings of previous ones and achieves a higher accuracy, and a more robust disturbance rejection. It makes use of the Gopinath-style flux observer and combines a lumped-parameter thermal network operating at low speeds and a flux-based permanent magnet temperature observer operating at medium and high speeds. Simulation and experimental results on a 50kW permanent magnet motor show a performance enhancement over standard topologies; particularly, a superior disturbance rejection to voltage estimation errors. A detailed analysis of the optimal controller tuning is also presented. Furthermore, a Kalman filter is incorporated to account for sensor noise and model uncertainties. Experimental results show an effective fusion of independent temperature estimation methods leading to a superior accuracy compared to the previously investigated approaches. Moreover, the Kalman filter-based fusion offers the capability of detecting temperature-related system failures, e.g., cooling circuit malfunctions.