## 2 System-Level Design Optimization
### System-Level Optimization Methods
#### 2.2.2 Multi-Level Optimization Method  
The multi-level optimization method differs from the single-level approach by dividing the process into the following distinct stages: machine level, control level, and system level. Optimization and evaluation are performed independently until the final level, where the entire drive system is evaluated. This staged approach helps distribute the computational workload, preventing excessive strain on the computer at each step [20; 29]. The following steps outline the process of implementing the MLOM:  
Step 1: Definition of the machine- and control-level models and objectives. At each level, specific objectives are defined--for example, efficiency, average torque, torque ripple, mass, and cost for the machine and the settling time, overshoot, and cost for the controller. Additionally, constraints such as the maximum current, minimum efficiency, and search space are specified, along with the optimization algorithms to be employed. Step 2: Optimization of each level independently. In this step, the machine and control levels are optimized separately from one another. First, the optimization process is applied to the machine, followed by the optimization of the controller. 1. Machine Level: Design optimization is carried out for the machine, and the steady-state performance of the machine is evaluated, including efficiency, weight, average torque, and cost. Key machine parameters, such as resistance, flux linkage, and inductance, are then passed on to the next step to be used as inputs at the control level. 2. Control Level: At this level, the optimization of the controller is performed based on the output parameters from the previous level. The dynamic performance of the drive system is then evaluated, focusing on speed overshoot, settling time, and torque ripple. Step 3: The final step is the system level, where the overall drive system is evaluated, and the results are verified. It should be noted that each step concludes and proceeds to the next when the optimization algorithm has achieved the defined objectives. As a result, both the steady-state and dynamic performances are optimized in the final step, ensuring that the methodology follows a sequential logic. However, this sequential logic ensures a near-optimal solution for each individual component; nevertheless, these component-level solutions may not align with the overall near-optimal performance of the system. As a result, this method can easily yield suboptimal solutions compared to single-level design optimization if not thoroughly accounted for, which considers the system holistically from the beginning. Moreover, if the evaluation stage does not yield satisfactory results, the methodology must be restarted from the beginning with different design parameters. This iterative reinitialization highlights the primary distinction between this approach and the single-level design optimization method. Figure 4 shows the MLOM framework, highlighting this sequential logic applied during the optimization and evaluation steps mentioned earlier. In [30], the authors propose a novel approach for the design and optimization of an SRM for EV application using a novel Multi-Objective Design Optimization (MODO) algorithm. Their methodology couples the Kriging surrogate model, constructed in ALTAIR FLUX, with MATLAB/Simulink to simulate motor operation and utilizes the NSGA-II algorithm for optimization. Initially, the algorithm optimizes the geometric characteristics of the motor, selecting the best design that maximizes the average torque. Subsequently, it optimizes the turn-on, \(\theta_{on}\), and turn-off, \(\theta_{off}\), angles. However, the study evaluates only the dynamic performance, arguing that the steady-state condition does not significantly impact the overall system performance.