Tornado Optimization With Pattern Search Method for Optimal Design of IPMSM

Wi, Chang-Hyun; Lim, Dong-Kuk

doi:10.1109/tmag.2021.3086797

Cited by 8 publications

(4 citation statements)

References 9 publications

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“…Since the proposed algorithm distinguishes the types of samples through the results of initial sampling and takes different search strategies depending on the types, it is important to generate samples evenly across the entire problem domain at this stage. Therefore, latin hypercube sampling (LHS), a uniform distribution method that considers the distance between each sample, is used [18]. LHS partitions each dimension (process parameter) into N separate, equal probability parts and then draws samples from each part according to the probability density of the random variables in that part.…”

Section: A Sampling and Sample Classificationmentioning

confidence: 99%

Optimal Design of an IPMSM for HEVs Using Circular Area Movement Optimization With the Pattern Search Method

Lee,

Park,

Lim

2024

IEEE Access

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Section: A Sampling and Sample Classificationmentioning

confidence: 99%

Optimal Design of an IPMSM for HEVs Using Circular Area Movement Optimization With the Pattern Search Method

Lee,

Park,

Lim

2024

IEEE Access

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“…The pattern search optimisation algorithm is used to find a design solution. Pattern search has been widely used to solve multimodal optimisation problems, such as the optimal design of motors [34]. The pattern search begins at the initial point and generates mesh points by applying pattern vectors and adaptive mesh size to the current point during iteration.…”

Section: Optimisation Routinementioning

confidence: 99%

Design and optimisation of energy‐efficient PM‐assisted synchronous reluctance machines for electric vehicles

Shao

Tavernini

Karci

et al. 2023

IET Electric Power Appl

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The design and optimisation of a permanent magnet‐assisted synchronous reluctance (PMaSynR) traction machine is described to improve its energy efficiency over a selection of driving cycles, when installed on a four‐wheel‐drive electrically powered vehicle for urban use, with two on‐board powertrains. The driving cycle‐based optimisation is defined with the objective of minimising motor energy loss under strict size constraints, while maintaining the peak torque and restricting the torque ripple. The key design parameters that exert the most significant influence on the selected performance indicators are identified through a parametric sensitivity analysis. The optimisation brings a motor design that is characterised by an energy loss reduction of 8.2% over the WLTP Class 2 driving cycle and 11.7% over the NEDC and Artemis Urban driving cycles, at the price of a 4.7% peak torque reduction with respect to the baseline machine. Additional analysis, implemented outside the optimisation framework, revealed that different coil turn adjustments would reduce the energy loss along the considered driving cycles. However, under realistic size constraints, the optimal design solutions are the same.

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“…They will be the training set for growing the tree. The remaining data, called ''Out-ofbag (OOB)'', is never used, which will be used in the accuracy test of surrogate models [18]. The ratio of the training dataset to the test dataset across the entire dataset can be adjusted by the user.…”

Section: A Random Forestmentioning

confidence: 99%

A Study on the Optimal Design of PMa-SynRM for Electric Vehicles Combining Random Forest and Genetic Algorithm

Kwon

Lim

2023

IEEE Access

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This paper proposes a method that combines random forest technology (RF) and a genetic algorithm (GA) for optimal design of traction motors for electric vehicles (EVs). The target motor is a permanent magnet assisted synchronous reluctance motor (PMa-SynRM), for which the design goal is to increase the average torque and efficiency while reducing the total harmonic distortion (THD) of the lineto-line back electromotive force (LtoL BEMF) and torque ripple. The superiority of the RF technology was verified by comparison with other machine learning methods through two mathematical test functions. Prediction accuracy was improved through hyperparameter tuning using the GridSearchCV technique. The design variables, selected for the optimal design of the target application, were proved to be an influential variable through sensitivity analysis techniques. Finally, stress analysis, thermal analysis, and irreversible demagnetization analysis were conducted to prove the validity of the selected optimal design. The applicability of the proposed method was verified by increasing the average torque by 12.92% and reducing torque ripple, cogging torque, and LtoL BEMF THD by 36.01%, 50.0%, and 4.58% respectively.INDEX TERMS Design optimization, electric vehicles, motor design, permanent magnet assisted synchronous reluctance motor, random forest, traction motor. DONG-KUK LIM (Member, IEEE) received the B.S. degree in electrical engineering from Dong-

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Tornado Optimization With Pattern Search Method for Optimal Design of IPMSM

Cited by 8 publications

References 9 publications

Optimal Design of an IPMSM for HEVs Using Circular Area Movement Optimization With the Pattern Search Method

Optimal Design of an IPMSM for HEVs Using Circular Area Movement Optimization With the Pattern Search Method

Design and optimisation of energy‐efficient PM‐assisted synchronous reluctance machines for electric vehicles

A Study on the Optimal Design of PMa-SynRM for Electric Vehicles Combining Random Forest and Genetic Algorithm

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