The aim of this paper is to outline all the steps in a rigorous and simple procedure for system identification of BLDC motor. A practical mathematical model for identification is derived. Frequency domain identification techniques and time domain estimation method are combined to obtain the unknown parameters. The methods in time domain are founded on the least squares approximation method and a disturbance observer. Only the availability of experimental data for rotor speed and armature current are required for identification. The proposed identification method is systematically investigated, and the final identified model is validated by experimental results performed on a typical BLDC motor in UAV.
A power management strategy is a key necessity for power-split electromechanical transmission systems. A model predictive control strategy which is based on finite-horizon optimization and can combine the advantages of instantaneous optimization and global optimization is a good solution for online optimization of the power management. Therefore, a model-predictive-control-based power management strategy is proposed for a two-mode electromechanical transmission. A model predictive control strategy consists of two parts: a predictive model and a receding-horizon optimization algorithm. A predictive model is used for predicting future information on the electromechanical transmission states, and real-time receding-horizon optimization with a finite horizon is adopted for optimal decision making. First, the predictive model, including the battery state and the transmission output torque, which provides a priori knowledge for optimal calculation, is proposed. Then, to ensure optimal operating areas of the engine and the motors, a novel overall efficiency calculation method for the whole powertrain including the engine, the motors, the power-split coupled machine and the battery is proposed and regarded as the optimization objective. The overall efficiency not only is focused on the engine fuel economy but also determines the power loss of the motors, the battery and the planetary gears together, which enhances the fuel economy and the transmission efficiency significantly. Based on the predictive model and receding-horizon optimization, the MPC strategy is established and tested by hardware-in-the-loop simulations under Urban Dynamometer Driving Schedule and New European Driving Cycle conditions. The test results showed that the power management strategy can enhance the fuel economy and proved to be a potential real-time optimization method for power distribution in the electromechanical transmission system; this strategy can provide theoretical support for actual application of electromechanical transmission systems.
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