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Switched reluctance machines (SRMs) have recently attracted more interest in many applications due to the volatile prices of rare-earth permanent magnets (PMs) used in permanent magnet synchronous machines (PMSMs). They also have rugged construction and can operate at high speeds and high temperatures. However, acoustic noise and high torque ripples, in addition to the relatively low torque density, present significant challenges. Geometry and topology optimization are applied to overcome these challenges and enable SRMs to compete with PMSMs. Key geometric design parameters are optimized to minimize various objective functions within geometry optimization. On the other hand, the material distribution in a particular design space within the machine domain may be optimized using topology optimization. We discuss how these techniques are applied to optimize the geometries and topologies of SRMs to enhance machine performance. As optimizing the machine geometry and material distribution at the design phase is of substantial significance, this work offers a comprehensive literature review on the current state of the art and the possible trends in the optimization techniques of SRMs. The paper also reviews different configurations of SRMs and stochastic and deterministic optimization techniques utilized in optimizing different configurations of the machine.

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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.INDEX TERMS Electric machine design, electric machine modeling, machine learning (ML), switched reluctance motor (SRM).