On-line control algorithms for an energy-efficient operation of highly exploited electrical drives

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Highly exploited electrical machines are being used together with frequency converters in stationary industrial drive systems as well as in vehicle applications. Energy efficient operation of the drive has a great significance to reduce overall energy consumption. Most machines are being optimized for a single nominal operation point to achieve high rated efficiency at that point. However in many industry applications and particularly in traction applications they operate permanently in dynamic mode with great amounts of partial load and overload at different speeds depending on the drive cycle. To ensure the conversion of electrical energy into mechanical energy with a minimum of loss energy, the drive control has to be adapted as well as possible to the electric machine.The machine parameters that are necessary for an energy efficient control depend on the machine design. They can be determined only with uncertainties in advance and they vary during the operation of the machine. Particularly magnetic saturation and temperature change of the windings and magnets influence them. For this reason an adaptive control algorithm has to be developed that can adapt the reference variables to the changing conditions to ensure optimal flux conditions in the machine. Moreover a design optimization for the whole drive cycle, instead of just one rated point, can achieve a better energy balance of the traction drive.The goal of the research project is to use online optimization algorithms for an energy efficient operation of highly exploited electrical drives with interior permanent magnet synchronous machines and with induction machines. The online algorithms shall lead to a global loss minimal operation. By the use of exact adaptation of the optimal reference currents the goal is to reduce the electrical power losses of the drive by 15 % compared to a standard solution. This corresponds to an increase in energy efficiency of 2 %. It is to be validated experimentally on a test bench in typical drive cycles of hybrid busses in urban areas.