Optimal drive cycle current supply of a wound field automotive electrical machine using surrogate models | Science and Technology for Energy Transition (STET)

Open Access

Review

Issue		Sci. Tech. Energ. Transition Volume 79, 2024 Power Components For Electric Vehicles


Article Number		2
Number of page(s)		9
DOI		https://doi.org/10.2516/stet/2023041
Published online		09 January 2024

Keane A., Forrester A., Sobester A. (2008) Engineering Design via Surrogate Modelling: A Practical Guide, John Wiley & Sons. [CrossRef] [Google Scholar]
Dupuis R. (2019) Surrogate models coupled with machine learning to approximate complex physical phenomena involving aerodynamic and aerothermal simulations, Doctoral Dissertation. [Google Scholar]
Asher M.J., Croke B.F.W., Jakeman A.J., Peeters L.J.M. (2015) A review of surrogate models and their application to groundwater modeling, Water Resour. Res. 51, 8, 5957–5973. https://doi.org/10.1002/2015WR016967. [CrossRef] [Google Scholar]
Tao J., Sun G. (2019) Application of deep learning based multi-fidelity surrogate model to robust aerodynamic design optimization, Aerosp. Sci. Technol. 92, 722–737. https://doi.org/10.1016/j.ast.2019.07.002. [CrossRef] [Google Scholar]
Duchaud J.L., Hlioui S., Louf F., Gabsi M. (2014) Electrical machine optimization using a kriging predictor, in 2014 17th Int Conf. Electr. Mach. Syst. ICEMS 2014, 3476–3481. https://doi.org/10.1109/ICEMS.2014.7014091. [Google Scholar]
Harris T.P., Nix A.C., Perhinschi M.G., Wayne W.S., Diethorn J.A., Mull A.R. (2021) Implementation of radial basis function artificial neural network into an adaptive equivalent consumption minimization strategy for optimized control of a hybrid electric vehicle, J. Transp. Technol. 11, 4, 471–503. https://doi.org/10.4236/jtts.2021.114031. [Google Scholar]
Sun X., Shi Z., Cai Y., Lei G., Guo Y., Zhu J. (2020) Driving-cycle-oriented design optimization of a permanent magnet hub motor drive system for a four-wheel-drive electric vehicle, IEEE Trans. Transp. Electrif. 6, 3, 1115–1125. https://doi.org/10.1109/TTE.2020.3009396. [CrossRef] [Google Scholar]
Salameh M., Brown I.P., Krishnamurthy M. (2019) Fundamental evaluation of data clustering approaches for driving cycle-based machine design optimization, IEEE Trans. Transp. Electrif. 5, 4, 1395–1405. https://doi.org/10.1109/TTE.2019.2950869. [CrossRef] [Google Scholar]
Fatemi A., Demerdash N.A.O., Nehl T.W., Ionel D.M. (2016) Large-scale design optimization of pm machines over a target operating cycle, IEEE Trans. Ind. Appl. 52, 5, 3772–3782. https://doi.org/10.1109/TIA.2016.2563383. [CrossRef] [Google Scholar]
Djami M., Hage-Hassan M., Marchand C., Krebs G., Dessante P., Belhaj L.A. (2022) Kriging metamodel for electric machines: A drive cycle approach, in 2022 Int. Conf. Electr. Mach. ICEM 2022, 251–256. https://doi.org/10.1109/ICEM51905.2022.9910946. [CrossRef] [Google Scholar]
Ciuffo B., Maratta A., Tutuianu M., Anagnostopoulos K., Fontaras G., Pavlovic J., Serra S., Tsiakmakis S., Zacharof N. (2015) Development of the worldwide harmonized test procedure for light-duty vehicles: Pathway for implementation in European Union legislation, Transp. Res. Rec. 2503, 1, 110–118. https://doi.org/10.3141/2503-12. [CrossRef] [Google Scholar]
De Santiago J., Bernhoff H., Ekergård B., Eriksson S., Ferhatovic S., Waters R., Leijon M. (2011) Electrical motor drivelines in commercial all-electric vehicles: A review, IEEE Trans. Veh. Technol. 61, 2, 475–484. https://doi.org/10.1109/TVT.2011.2177873. [Google Scholar]
Di Gioia A., Brown I.P., Nie Y., Knippel R., Ludois D.C., Dai J., Hagen S., Alteheld C. (2017) Design and demonstration of a wound field synchronous machine for electric vehicle traction with brushless capacitive field excitation, IEEE Trans. Ind. Appl. 54, 2, 1390–1403. https://doi.org/10.1109/TIA.2017.2784799. [Google Scholar]
Crozier R., Mueller M. (2016) A new MATLAB and octave interface to a popular magnetics finite element code, in Proc. – 2016 22nd Int. Conf. Electr. Mach. ICEM 2016, 1251–1256. https://doi.org/10.1109/ICELMACH.2016.7732685. [Google Scholar]
Park R.H. (1929) Two-reaction theory of synchronous machines: generalized method of analysis – part I, Trans. Am. Inst. Electr. Eng. 48, 3, 716–727. https://doi.org/10.1109/T-AIEE.1929.5055275. [CrossRef] [Google Scholar]
Herold T., Franck D., Lange E., Hameyer K. (2011) Extension of a d-q model of a permanent magnet excited synchronous machine by including saturation, cross-coupling and slotting effects, 2011 IEEE Int Electr. Mach. Drives Conf. IEMDC 2011, 1363–1367. https://doi.org/10.1109/IEMDC.2011.5994804. [CrossRef] [Google Scholar]
Mazloum R., Hlioui S., Laurent L., Belhadi M., Mermaz-Rollet G., Gabsi M. (2022) Wound field synchronous drive cycle current parameters optimization: A metamodel-based approach, in 2022 Int. Conf. Electr. Mach. ICEM 2022, 199–205. https://doi.org/10.1109/ICEM51905.2022.9910948. [CrossRef] [Google Scholar]
Bertotti G. (1987) General properties of power losses in soft ferromagnetic materials, IEEE Trans. Magn. 24, 1, 621–630. https://doi.org/10.1109/20.43994. [Google Scholar]
Jiang P., Zhou Q., Shao X. (2020) Surrogate-model-based design and optimization, Springer Tracts in Mechanical Engineering (STME). [CrossRef] [Google Scholar]
Rasmussen C.E., Williams C.K.I. (2006) Gaussian Processes for Machine Learning, Vol. 7, 5, MIT Press, Cambridge, MA. [MathSciNet] [Google Scholar]
Choi Y., Song D., Yoon S., Koo J. (2021) Comparison of factorial and latin hypercube sampling designs for meta-models of building heating and cooling loads, Energies 14, 2. https://doi.org/10.3390/en14020512. [Google Scholar]
Manteufel R.D. (2001) Distributed hypercube sampling algorithm, in Collect. Tech. Pap. – AIAA/ASME/ASCE/AHS/ASC Struct. Struct. Dyn. Mater. Conf., vol. 5, pp. 3617–3622. https://doi.org/10.2514/6.2001-1673. [Google Scholar]
Beachkofski B.K., Grandhi R.V. (2002) Improved distributed hypercube sampling, in Collect. Tech. Pap. – AIAA/ASME/ASCE/AHS/ASC Struct. Struct. Dyn. Mater. Conf. 1, April, pp. 580–586. https://doi.org/10.2514/6.2002-1274. [Google Scholar]
Laurent L. (2019) MultiDOE: sampling technics on MATLAB/OCTAVE (v3.3). Zenodo. https://doi.org/10.5281/zenodo.2677748. [Google Scholar]
Mathworks (2019), Statistics and Machine Learning Toolbox User’s Guide R2019b. [Google Scholar]
Mazloum R., Hlioui S., Laurent L., Belhadi M., Mermaz-Rollet G., Gabsi M. (2022) On the use of surrogate models for drive cycle automotive electrical machine design, in 2022 IEEE Int. Conf. Electr. Sci. Technol. Maghreb, Cist., Vol. 4, pp. 1–6. https://doi.org/10.1109/CISTEM55808.2022.10043894. [Google Scholar]
Cisse K.M., Hlioui S., Belhadi M., Mermaz-Rollet G., Gabsi M., Cheng Y. (2021) Design optimization of multi-layer permanent magnet synchronous machines for electric vehicle applications, Energies 14, 1–21. https://doi.org/10.3390/en14217116. [Google Scholar]
Fuhg J.N., Fau A., Nackenhorst U. (2021) State-of-the-art and comparative review of adaptive sampling methods for Kriging, Arch. Comput. Methods Eng. 28, 4, 2689–2747. https://doi.org/10.1007/s11831-020-09474-6. [CrossRef] [MathSciNet] [Google Scholar]
Xu H., Liu L., Zhang M. (2020) Adaptive surrogate model-based optimization framework applied to battery pack design, Mater. Des. 195, 108938. https://doi.org/10.1016/j.matdes.2020.108938. [CrossRef] [Google Scholar]
Fricker T.E., Oakley J.E., Urban N.M. (2013) Multivariate Gaussian process emulators with nonseparable covariance structures, Technometrics 55, 1, 47–56. https://doi.org/10.1080/00401706.2012.715835. [CrossRef] [MathSciNet] [Google Scholar]

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