Model predictive overload control of an automotive switched reluctance motor for frequent rapid accelerations

An electric motor can be overloaded shortly to produce a much higher torque than the rated value due to its inherent large thermal capacitance. The state-of-the-art overload strategies are often based on the static characteristics of the motor. The real-time thermal condition is not taken into account, which leaves certain overload potential unused. This paper presents a model predictive overload control strategy for a water-cooled automotive switched reluctance motor. The maximum torque is calculated predictively based on an accurate thermal model with hot-spot temperature estimation. The thermal capacitance of the motor is fully exploited. Due to the predictive nature of the algorithm, the peak torque production is inherently guaranteed over the prediction horizon. The proposed method is experimentally validated. It allows frequent overload operations with up to three times of the rated torque on the test bench.