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This paper presents a summary of a Honeywell collaborative IR&D project for development of a high-performance sensorless method for a Permanent Magnet Synchronous Motor (PMSM) based on Extended Kalman Filter (EKF). The parameters of a high speed 90kW industrial PMSM is employed for validating the proposed sensorless method through simulation. The simulation results demonstrate that the sensorless PMSM drive can work satisfactorily over the whole speed range with full torque and speed control accuracy. With the proposed flux linkage estimator, the new sensorless PMSM drive exhibits robustness against machine parameter variations. Good performance is obtained even when the stator resistance is changed by 200%, or the flux linkage is weakened by 20%. Detailed design and modeling of the sensorless PMSM drive are described in this paper.

An accurate air flow rate model is critical for high-quality air-fuel ratio control in Spark-Ignition engines using a Three-Way-Catalyst. Emerging Variable Valve Timing technology complicates cylinder air charge estimation by increasing the number of independent variables. In our previous study (SAE 2004-01-3054), an Artificial Neural Network (ANN) has been used successfully to represent the air flow rate as a function of four independent variables: intake camshaft position, exhaust camshaft position, engine speed and intake manifold pressure. However, in more general terms the air flow rate also depends on ambient temperature and pressure, the latter being largely a function of altitude. With arbitrary cam phasing combinations, the ambient pressure effects in particular can be very complex. In this study, we propose using a separate neural network to compensate the effects of altitude on the air flow rate.