Artificial Neural Network (ANN) Based Meta-Model Design Optimization of Direct-Drive Wheel Hub Motor for an Electric Scooter

Ida Mahartana; Harus Laksana Guntur

doi:10.4028/p-e0d37r

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vol. 912Artificial Neural Network (ANN) Based Meta-Model...

Artificial Neural Network (ANN) Based Meta-Model Design Optimization of Direct-Drive Wheel Hub Motor for an Electric Scooter

Abstract:

Direct-drive electric motor or In-Wheel Hub Motor (WHM) is gradually becoming popular as a new solution for electric propulsion. By meas of direct drive, the conventional transmission of belt or chain sprocket is not required to deliver the high torque requirement at the wheel. This paper studied metamodel machine leasning (MOP-ANN) implementation to predict the relationship model of geometries input of 42 slot/40 pole WHM with corresponding output performance. The multi-layer perceptron (MLP) of the neural network structure was trained to reveal the underlying pattern within the data sample. The sample data was obtained from a sensitivity analysis drive from FEA. The optimum geometry of the motor was directly solved by implementing a multiobjective optimization (MOO) method of genetic algorithm. Finnaly, the optimum geometry of the MOP-ANN based result is validated by FEA.

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Periodical:

Applied Mechanics and Materials (Volume 912)

Pages:

91-97

DOI:

https://doi.org/10.4028/p-e0d37r

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Online since:

February 2023

Authors:

Ida Mahartana*, Harus Laksana Guntur

Keywords:

Genetic Algorithm, MLP, MOP-ANN, Wheel Hub Motor

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References

[1] Y. L. Karnavas, I. D. Chasiotis, C. D. Korkas, and S. K. Amoutzidis, Modelling and multiobjective optimization analysis of a permanent magnet synchronous motor design,, Int. J. Numer. Model. Electron. Networks, Devices Fields, vol. 30, no. 6, p.1–14, 2017,.

DOI: 10.1002/jnm.2232

Google Scholar

[2] B. M. Song, K. C. Chang, and J. Y. Choi, Design of an outer-rotor-type permanent magnet motor for electric scooter propulsion systems,, 2010 Int. Power Electron. Conf. - ECCE Asia -, IPEC 2010, p.2736–2742, 2010,.

DOI: 10.1109/ipec.2010.5542349

Google Scholar

[3] W. Gruber, W. Bäck, and W. Amrhein, Design and implementation of a wheel hub motor for an electric scooter,, 2011 IEEE Veh. Power Propuls. Conf. VPPC 2011, p.1–6, 2011,.

DOI: 10.1109/vppc.2011.6043139

Google Scholar

[4] D. J. Van Schalkwyk and M. J. Kamper, Effect of hub motor mass on stability and comfort of electric vehicles,, 2006 IEEE Veh. Power Propuls. Conf. VPPC 2006, 2006,.

DOI: 10.1109/vppc.2006.364297

Google Scholar

[5] S. Chung, S. Moon, D. Kim, and J. Kim, Development of a 20Pole-24Slot SPMSM with Consequent Pole Rotor for In-Wheel Direct Drive,, vol. 0046, no. c, 2015,.

DOI: 10.1109/tie.2015.2472375

Google Scholar

[6] S. E. Skaar, O. Krovel, and R. Nilssen, Distribution, coil-span and winding factors for PM machines with concentrated windings,, XVII Int. Conf. Electr. Mach. ICEM 2006, p.346, 2006,.

Google Scholar

[7] T. Most and J. Will, Metamodel of Optimal Prognosis: An Automatic Approach for Variable Reduction and Optimal Metamodel Selection," Weimarer Optimierungs-und Stochastiktage, no. November, 2008, [Online]. Available: https://www.dynardo.de/fileadmin/ Material_Dynardo/WOST/Paper/wost5.0/Paper_Most.pdf%0A http://citeseerx.ist.psu.edu/viewdoc/download;jsessionid=56BFA6867DBA96D6C200BCACD14EA530,doi=10.1.1.411.1767&rep=rep1&type=pdf.

Google Scholar

[8] S. A. Sadrossadat and O. Rahmani, ANN-based method for parametric modelling and optimising efficiency, output power and material cost of BLDC motor,, IET Electr. Power Appl., vol. 14, no. 6, p.951–960, 2020,.

DOI: 10.1049/iet-epa.2019.0686

Google Scholar