Fusion of direct and indirect temperature estimation techniques for permanent magnet synchronous motors

Permanent magnet synchronous motors (PMSM) are widely used in many industry fields. Maximizing their degree of thermal utilization reduces the motor's weight, volume and cost. For this reason, real-time temperature information is required. However, this cannot be solely obtained from measurement sensors due to costs and constructional drawbacks. Thus, several direct temperature modeling techniques, like lumped-parameter thermal networks (LPTN), and indirect temperature estimation approaches based on accurate electrical motor models detecting temperature-sensitive parameter changes were investigated in the last years. As these methods are typically independent of each other, fusion approaches can be used to increase the conjoint estimation accuracy and robustness. In this contribution a Kalman filter (KF) is successfully combined with a low-order LPTN, an electrical model-based permanent magnet temperature observer and a built-in winding temperature sensor to achieve this goal. Measurement results for a 50 kW PMSM proof the enhanced KF performance compared to the individual estimation techniques.