## 2 System-Level Design Optimization
### System-Level Optimization Methods
#### 2.2.1 Single-Level Optimization Method
The choice of GA is well-suited to the multi-objective nature of the problem, and it has the ability to handle the non-linearities inherent in the system more effectively than traditional gradient-based methods. Their objective is to optimize the RMS current, torque ripple, and torque output. Finally, they evaluate the system's performance under both steady-state and dynamic conditions using MATLAB/Simulink. However, the study does not provide details on the optimization process of the motor's geometry, focusing solely on the control optimization. Reference [26] proposes a modeling and optimization framework for an SRM drive system, which utilizes FEA-based method through ANSYS simulation software for steady-state operation and analytical equations to describe the system's dynamic behavior. Optimization is performed using the Particle Swarm Optimization (PSO) algorithm to determine the near-optimal current values that minimize torque ripple and voltage peaks and extract the corresponding current profile. PSO was selected over other algorithms such as GA and Differential Evolution (DE) due to its ability to efficiently explore the solution space while maintaining low computational cost, making it well-suited for this application. The current profiling construction and methodology allow for the dynamic state of the motor to be estimated solely through its static analysis. This approach significantly reduces the time required for holistic optimization. However, there is a slight trade-off in accuracy, as not all transient state phenomena are considered during the dynamic analysis. To account for real-world effects, the authors base the optimization on experimentally validated material data, recognizing deviations such as increased iron loss due to manufacturing. The results show very low torque ripple and an almost square current waveform. Reference [27] focuses on the design of an SRM for aerospace applications by utilizing a field-circuit-coupled FEM and incorporating a loss density function as a constraint for thermal properties. The multi-objective problem is transformed into a single-objective one through the loss density function applied to efficiency. The FEM is employed for the motor, while a field-circuit analysis is used for the inverter, which utilizes the Asymmetric Half-Bridge Converter (AHBC). In the controller, the switching angles \(\theta_{on}\) and \(\theta_{off}\) are considered parameters, and the design parameters are optimized simultaneously with the help of the GA. The results, presented through static analysis, demonstrate improvements, including a slight increase in efficiency and reductions in torque ripple, mass, and loss density. In [28], the authors discuss the design of an SRM for an in-wheel application in EVs by conducting a static analysis using the FEM through MotorSolve and MATLAB. The NSGA-II algorithm is employed as an optimizer to enhance the average torque, torque ripple, and motor efficiency by modifying both motor and controller variables. The results indicate that the initial design objectives were successfully met, ensuring that the motor fulfills the required operational criteria set by the designer, with a slight improvement over the initially defined targets. In the aforementioned studies [24; 25; 26; 27; 28], the authors do not present the computational time required to complete the simulation, and given that the data are extracted using the FEM, the computational time is expected to be substantial. A comprehensive search across the entire design space as defined by the designer based on the selected parameters can be prohibitive when using the FEM for every iteration. One potential solution would be to incorporate a complementary space reduction technique or to impose narrower parameter limits. However, this approach carries the risk that an acceptable solution may not exist within the predefined constraints.