Design and Optimization of Hybrid Excitation Switched Reluctance Motors for Electric Transportation Vehicles
DOI:
https://doi.org/10.13052/2024.ACES.J.400903Keywords:
Finite element analysis (FEA), genetic algorithm, hybrid excitation switched reluctance motor (HESRM), multi-objective optimization, neural networkAbstract
To improve the torque characteristic of hybrid excitation switched reluctance motors (HESRM), the structure and excitation current of HESRM are comprehensively studied. First, a novel structure is introduced to HESRM, in which pole pieces are added to the rotor salient pole to reduce output torque ripple. The effect of different structural parameters in HESRM is studied by finite element model (FEM). To evaluate the torque characteristic of the machine, mean torque and torque are chosen as key evaluation factors of HESRM. To achieve a quick and accurate optimization process, an artificial neural network (ANN) based prediction model is built according to FEM results, in which system structure is employed as input components and evaluate factors are employed as output components. Then, genetic algorithm (GA) is designed for HESRM structure optimization. With improved GA and ANN prediction models, the torque performance of HESRM can be further improved. Finally, experimental and simulation results are given to validate the accuracy of machine design.
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References
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