Sensorless Vector Control of the Charging Process for Flywheel Battery with Artificial Neural Network Observer

The new type of flywheel battery requires control system with compact structure and low manufacturing cost. To meet this requirement, a new method for the sensorless vector control of flywheel battery is proposed in this paper. The advantage of the proposed control system is that it does not need an extra sensor to obtain the flywheel speed and position information. The determination of flywheel position and thereby speed are made by estimating back electromotive force (EMF) using the artificial neural network (ANN) observers. By doing so, the dimensions and cost of the driver system can be reduced. The ANN observers use the instantaneous values of stator voltages and currents and the estimated error of the stator current as their input to output the back EMF components in the alpha-beta reference frame. A simulation model was established by the use of MATLAB/Simulink software to carry out the numerical experiments. The test results demonstrate that the proposed charging control system for flywheel battery has a good control performance and a good robustness. The speed/position estimation precision is high and the error is acceptable for a wide speed range.