  Real-time acquisition of accurate machine parameters is of significance to
achieving high performance in electric drives, particularly targeted for
mission-critical applications. Unlike the saturation effects, the temperature
variations are difficult to predict, thus it is essential to track
temperature-dependent parameters online. In this paper, a unified framework is
developed for online parameter identification of rotating electric machines,
premised on the Recursive Prediction Error Method (RPEM). Secondly, the
prediction gradient ($\mathbf{\Psi}^T$)-based RPEM is adopted for
identification of the temperature-sensitive parameters, i.e., the permanent
magnet flux linkage ($\Psi_m$) and stator-winding resistance ($R_s$) of the
Interior Permanent Magnet Synchronous Machine (IPMSM). Three algorithms,
namely, Stochastic Gradient (SGA), Gauss-Newton (GNA), and physically
interpretative method (PhyInt) are investigated for the estimation gains
computation. A speed-dependent gain-scheduling scheme is used to decouple the
inter-dependency of $\Psi_m$ and $R_s$. With the aid of offline simulation
methods, the main elements of RPEM such as $\mathbf{\Psi}^T$ are analyzed. The
concept validation and the choice of the optimal algorithm is made with the use
of 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 the
SoC-based industrial embedded control system. The experimental results reveal
that $\mathbf{\Psi}^T$-based RPEM, in general, can be a versatile technique in
temperature-sensitive parameter adaptation both online and offline.
