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Industrial applications, especially automotive ones should be robust and cheap. Both properties can be improved by using model based state estimation. Sensor cost can be reduced if some signal values are calculated from the other, already measured signals or the robustness of the system can be increased by supervising the sensors by calculating their measurement value out of the existing signal values. Robustness and redundancy is extremely important considering drive-by-wire technology, where the physical connection between the steering wheel and the wheels of the vehicle is omitted. This paper reports advances in permanent magnet synchronous machine model identification. By measuring machine input voltages, output currents speed and using the least squares optimization method, internal parameters of the machine can be estimated. In the identification stage, the model excitation signals are the current values and the speed of the machine and the response signals are the input voltages. After having a properly identified model, the output currents and electrical torque of the machine can be calculated knowing the input voltages and the speed of the machine. Those current sensors can be either eliminated or supervised by the model based redundant information.