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Transient operating conditions in electrical systems not only have significant impact on the operating behavior of individual components but indirectly affect system and component reliability and life. Specifically, transient loads can cause additional loss in the electrical conduction path consisting of windings, power electronic devices, distribution wires, etc., particularly when loads introduce high peak vs. average power ratios. The additional loss increases the operating temperatures and thermal cycling in the components, which is known to reduce their life and reliability. Further, mechanical stress caused by dynamic loading, which includes load torque cycling and high peak torque loading, increases material fatigue and thus reduces expected service life, particularly on rotating components (shaft, bearings).

The development of electromechanical actuators (EMAs) is the key technology to build an all-electric aircraft. One of the greatest hurdles to replacing all hydraulic actuators on an aircraft with EMAs is the acquisition, transport and rejection of waste heat generated within the EMAs. The absence of hydraulic fluids removes an attractive and effective means of acquiring and transporting the heat. To address thermal management under limited cooling options, accurate spatial and temporal information on heat generation must be obtained and carefully monitored. In military aircraft, the heat loads of EMAs are highly transient and localized. Consequently, a FEA-based thermal model should have high spatial and temporal resolution. This requires tremendous calculation resources if a whole flight mission simulation is needed. A lumped node thermal network is therefore needed which can correctly identify the hot spot locations and can perform the calculations in a much shorter time.

A commercial electromechanical actuator (EMA) is to be dynamically tested with predetermined stroke and load profiles for transient thermal and electric power behavior to validate a numerical model used for aerospace applications. The EMA will follow the stroke profile representative of a real aircraft mission duty cycle. A hydraulic press will exert a corresponding load profile onto the EMA. Specialized hydraulic load control methods must be employed to meet the accuracy requirements. Two of these methods are closed-loop linearization (CLL) and displacement induced disturbance cancellation (DIDC). These methods are implemented along with an external PID compensator, and run in real-time in a series of system identification experiments to observe controller performance.

This paper describes the integrated modeling of a permanent magnet (PM) motor used in an electromechanical actuator (EMA). A nonlinear, lumped-element motor electric model is detailed. The parameters, including nonlinear inductance, rotor flux linkage, and thermal resistances, and capacitances, are tuned using FEM models of a real, commercial motor. The field-oriented control (FOC) scheme and the lumped-element thermal model are also described.

A temperature estimation algorithm (thermal observer) that provides accurate estimates of the thermal states of an electric machine in real time is presented. The thermal observer is designed to be a Kalman filter that combines thermal state predictions from a lumped-parameter thermal model of the electric machine with temperature measurements from a single external temperature sensor. An analysis based on the error covariance matrix of the Kalman filter is presented to guide the selection of the best sensor location. The thermal observer performance is demonstrated using a 3.8 kW permanent-magnet machine. Comparison of the thermal observer estimates and the actual temperatures demonstrate that this approach can provide accurate knowledge of the machine's thermal states despite modeling uncertainty and unknown initial machine thermal states.

Military and commercial aircraft and ground vehicles are increasingly dependent on electrical power. New military, commercial and space vehicles rely on electrical power in whole or in part for such flight critical systems such as flight control and fuel. Digitally controlled power distribution systems as well as the digital controllers within these systems provide an unprecedented, affordable and inherent opportunity to monitor electrically powered vehicle systems health. This digital data provides component operating signatures to the system, thereby enabling advanced diagnostic and prognostic capabilities. These capabilities can be leveraged to ensure a high mission reliability rate as well as reduce life cycle ownership costs.