Accurate Torque Estimation for Induction Motors by Utilizing a Hybrid Machine Learning Approach

Due to the extensive use of induction motors in torque-controlled applications, e.g. in electric vehicles, high torque estimation accuracy is of paramount research importance. Traditionally, the performance of torque estimation highly depends on the accuracy of the observed magnetic flux which is a challenging task due to various non-ideal effects like magnetic saturation, iron losses or skin effect influences. In contrast, a hybrid machine learning observer for torque estimation is presented which also indirectly serves as a stator flux observer, i.e., flux and torque estimation are combined in one approach. To achieve both high estimation accuracy and small model size, not arbitrary neural network topologies are used, but physically-inspired structures based on expert knowledge (hybrid modeling). The main advantage of this method is that the training of the contained neural networks relies solely on recorded torque measurements and no additional flux measurements are required. In the complete operating range, this approach leads to a root mean square torque estimation error of only 1.0 % related to nominal torque. For comparison, observing the magnetic flux with a standard open-loop current model results in a normalized root mean square torque error of 4.6 %.