Review of Machine Learning Applications to the Modeling and Design Optimization of Switched Reluctance Motors

This work presents a comprehensive review of the developments in using Machine Learning (ML)-based algorithms for the modeling and design optimization of switched reluctance motors (SRMs). We reviewed Machine Learning-based numerical and analytical approaches used in modeling SRMs. We showed the difference between the supervised, unsupervised and reinforcement learning algorithms. More focus is placed on supervised learning algorithms as they are the most used algorithms in this area. The supervised learning algorithms studied in this work include the feedforward neural networks, recurrent neural networks, support vector machines, extreme learning machines, and Bayesian networks. This work also discusses several essential aspects of the considered machine learning algorithms, such as core concept, structure, and computational time. It also surveys sample data acquisition methods and data size. Finally, comparisons between the different considered ML-based algorithms are conducted in terms of electric motor type, dataset inputs and outputs, and algorithm's structure and accuracy to provide a summary overview of the ML-based algorithms for SRMs modeling and design.