Machine Learning Enabled Design Automation and Multi-Objective Optimization for Electric Transportation Power Systems

Jackson, Derek; Belakaria, Syrine; Cao, Yue; Doppa, Janardhan Rao; Liu, Xiaonan

doi:10.1109/tte.2021.3113958

Cited by 5 publications

(1 citation statement)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The accuracy and efficiency of SM is closely related to the quality and quantity of the dataset used for training [12][13][14]. For optimal design, the training dataset must be representative of the entire input space to avoid bias and allow the surrogate model to generalize well [15][16][17]. Despite the critical role of dataset selection, comprehensive discussions on optimizing the data selection strategy for efficiency and fast convergence are lacking in past literature on electric machine design.…”

Section: Introductionmentioning

confidence: 99%

Optimal Design of a Surface Permanent Magnet Machine for Electric Power Steering Systems in Electric Vehicle Applications Using a Gaussian Process-Based Approach

Choi,

Jang,

Choi

et al. 2023

Actuators

View full text Add to dashboard Cite

The efficient design optimization of electric machines for electric power steering (EPS) applications poses challenges in meeting demanding performance criteria, including high power density, efficiency, and low vibration. Traditional optimization approaches often fail to find a global solution or suffer from excessive computation time. In response to the limitations of traditional approaches, this paper introduces a novel methodology by incorporating a Gaussian process-based adaptive sampling technique into a surrogate-assisted optimization process using a metaheuristic algorithm. Validation on a 72-slot/8-pole interior permanent magnet (IPM) machine demonstrates the superiority of the proposed approach, showcasing improved exploitation–exploration balance, faster convergence, and enhanced repeatability compared to conventional optimization methods. The proposed design process is then applied to two surface PM (SPM) machine configurations with 9-slot/6-pole and 12-slot/10-pole combinations for EPS applications. The results indicate that the 12-slot/10-pole SPM design surpasses the alternative design in torque density, efficiency, cogging torque, torque ripple, and manufacturability.

show abstract