Data-Driven Multi-Objective Optimization-Based Excitation Selection Method for an Enhanced Control of a Variable Flux Reluctance Machine

This research proposes a novel current reference selection algorithm based on the most critical performance constraints to achieve improved control of a variable flux reluctance machine (VFRM). The proposed strategy defines the VFRM’s torque ripple, efficiency, and power factor, which are the inherent technological challenges in the torque and efficiency profile of this machine class, as a cost function and aims to select the field and armature current combination that satisfies the torque requirement while minimizing the cost function. These cost function components of the VFRM are initially determined using a transient finite element model (FEM), and the machine representation in the electromagnetic domain is then built using regression based on the generated dataset. The paper includes the details of the representation of the machine parameters, such as the design of experiments for dataset generation and comparison of different regression analyses. Finally, the proposed current reference selection algorithm is compared with the conventional strategies, and its performance and limitations within the entire drive operation range are evaluated.