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Real-time acquisition of accurate machine parameters is of significance toachieving high performance in electric drives, particularly targeted formission-critical applications. Unlike the saturation effects, the temperaturevariations are difficult to predict, thus it is essential to tracktemperature-dependent parameters online. In this paper, a unified framework isdeveloped for online parameter identification of rotating electric machines,premised on the Recursive Prediction Error Method (RPEM). Secondly, theprediction gradient ($\mathbf{\Psi}^T$)-based RPEM is adopted foridentification of the temperature-sensitive parameters, i.e., the permanentmagnet flux linkage ($\Psi_m$) and stator-winding resistance ($R_s$) of theInterior Permanent Magnet Synchronous Machine (IPMSM). Three algorithms,namely, Stochastic Gradient (SGA), Gauss-Newton (GNA), and physicallyinterpretative method (PhyInt) are investigated for the estimation gainscomputation. A speed-dependent gain-scheduling scheme is used to decouple theinter-dependency of $\Psi_m$ and $R_s$. With the aid of offline simulationmethods, the main elements of RPEM such as $\mathbf{\Psi}^T$ are analyzed. Theconcept validation and the choice of the optimal algorithm is made with the useof System-on-Chip (SoC) based Embedded Real-Time Simulator (ERTS).Subsequently, the selected algorithms are validated with the aid of a 3-kW,IPMSM drive where the control and estimation routines are implemented in theSoC-based industrial embedded control system. The experimental results revealthat $\mathbf{\Psi}^T$-based RPEM, in general, can be a versatile technique intemperature-sensitive parameter adaptation both online and offline.

Recursive Prediction Error Gradient-Based Algorithms and Framework to Identify PMSM Parameters Online