System-Level Optimization in Switched Reluctance Machine Design—Current Trends, Methodologies, and Future Directions

Switched Reluctance Machines (SRMs) are gaining increasing traction within the industrial sector, primarily due to their inherently simple and robust structure. Nevertheless, SRMs are characterized by two major drawbacks: high torque ripple and strong radial forces, both of which render them less suitable for applications requiring smooth operation, such as Electric Vehicles (EVs). To address these limitations, researchers and designers focus on optimizing these critical performance metrics during the design phase. In recent years, the concept of System-Level Design Optimization (SLDOM) has been introduced and applied to SRM drive systems, where both the machine and the controller are simultaneously considered within the optimization framework. This integrated approach has shown significant improvements in mitigating the aforementioned issues. This paper aims to review existing literature concerning SLDOM applied to SRMs, highlighting key methodologies and findings from studies conducted in recent years. Despite its promising outcomes, the adoption of SLDOM remains limited due to its high computational cost and complexity. In response to these challenges, the paper discusses complementary techniques used to enhance the optimization process, such as search space and computational time reduction strategies, along with the associated challenges and potential solutions. Finally, two critical directions for future research are identified, which are expected to influence the development of SLDOM and its application to SRMs in the coming years.