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Tytuł artykułu

Multicriterion synthesis of an electric circuit for wireless power transfer systems

Wybrane pełne teksty z tego czasopisma
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Warianty tytułu
PL
Multikryterialna synteza obwodu do bezprzewodowego transferu energii elektrycznej
Języki publikacji
EN
Abstrakty
EN
The paper presents the optimal design of the electric circuit of a Wireless Power Transfer Systems used to recharge the battery of an electric car. A field-circuit model is developed for the purpose of analysis, while a Pareto-like approach – based on SA-MNSGA-III and µ-BiMO, two nature-inspired algorithms - is used for synthesis. An excellent correspondence between the results obtained with the two methods was found. Then, the optimization algorithms could be applied successfully even in more complicated cases, such as WPTSs design.
PL
W artykule zaprezentowano projekt I optymalizację obwodu do bezprzewodowego transferu energii przeznaczonego do ładowania baterii samochodu elektrycznego. Wykorzystano algorytm Pareto. Uzyskano bardzo dobrą zgodność modelu z wynikami eksperymentu.
Rocznik
Strony
188--192
Opis fizyczny
Bibliogr. 20 poz., rys., tab.
Twórcy
  • University of Padova, Department of Industrial Engineering, 35131 Padova, Italy
  • University of Pavia, Department of Industrial and Information Engineering, Pavia, 27100, Italy
  • University of Padova, Department of Industrial Engineering, 35131 Padova, Italy
  • University of Pavia, Department of Industrial and Information Engineering, Pavia, 27100, Italy
  • University of Insubria, via Dunant, 3, 21100 Varese, Italy
Bibliografia
  • [1] M. Bertoluzzo, N. Zabihi, G. Buja, Overview on battery chargers for plug-in electric vehicles, in: 2012 15th International Power Electronics and Motion Control Conference (EPE/PEMC), 2012: p. LS4d.1-1. https://doi.org/10.1109/EPEPEMC.2012.6397461.
  • [2] G. Buja, M. Bertoluzzo, K. N. Mude, Design and Experimentation of WPT Charger for Electric City Car, IEEE Transactions on Industrial Electronics. 62 (2015) 7436-7447. https://doi.org/10.1109/TIE.2015.2455524.
  • [3] P. Di Barba, M.E. Mognaschi, A. Savini, S. Wiak, Island biogeography as a paradigm for MEMS optimal design, International Journal of Applied Electromagnetics and Mechanics. 51 (2016) S97-S105. https://doi.org/10.3233/JAE- 2015.
  • [4] P. Di Barba, F. Dughiero, M. Forzan, E. Sieni, Handling sensitivity in multiobjective design optimization of MFH inductors, IEEE Transactions on Magnetics. (2017) 1-1. https://doi.org/10.1109/TMAG.2017.2658728.
  • [5] E. Sieni, P. Di Barba, M. Forzan, Migration NSGA: method to improve a non-elitist searching of Pareto front, with application in magnetics, Inverse Problems in Science and Engineering. 24 (2016) 543-566. https://doi.org/10.1080/17415977.2015.1047366.
  • [6] E. Sieni, P. Di Barba, F. Dughiero, M. Forzan, Self-adaptive migration NSGA and optimal design of inductors for magnetofluid hyperthermia, Engineering Computations. 35 (2018) 1727-1746. https://doi.org/10.1108/EC-05-2016-0186.
  • [7] R. K. Jha, G. Buja, M. Bertoluzzo, S. Giacomuzzi, K. N. Mude, Performance Comparison of the One-Element Resonant EV Wireless Battery Chargers, IEEE Transactions on Industry Applications. 54 (2018) 2471-2482. https://doi.org/10.1109/TIA.2018.2796058.
  • [8] K. Deb, A. Pratap, S. Agarwal, T. Meyarivan, A fast and elitist multiobjective genetic algorithm: NSGA-II, Evolutionary Computation, IEEE Transactions on DOI - 10.1109/4235.996017. 6 (2002) 182-197.
  • [9] D. Simon, Biogeography-Based Optimization, Evolutionary Computation, IEEE Transactions On. 12 (2008) 702-713. https://doi.org/10.1109/TEVC.2008.919004.
  • [10] M.E. Mognaschi, Micro biogeography-inspired multi-objective optimisation for industrial electromagnetic design, Electronics Letters. 53 (2017) 1458-1460. https://doi.org/10.1049/el.2017.3072.
  • [11] P. Di Barba, F. Dughiero, M.E. Mognaschi, A. Savini, S. Wiak, Biogeography-Inspired Multiobjective Optimization and MEMS Design, IEEE Transactions on Magnetics. 52 (2016) 1-4. https://doi.org/10.1109/TMAG.2015.2488982.
  • [12] P. Di Barba, L. Fassina, G. Magenes, M.E. Mognaschi, Automated optimal design of wells for electromagnetic cell stimulation, ELECTROTECHNICAL REVIEW. 1 (2019) 3-6. https://doi.org/10.15199/48.2019.05.01.
  • [13] P. Di Barba, F. Dughiero, M. Forzan, E. Sieni, Self-adaptive NGSA algorithm and optimal design of inductors for magnetofluid hyperthermia, COMPEL - The International Journal for Computation and Mathematics in Electrical and Electronic Engineering. 36 (2017) 535-545.
  • [14] P. Di Barba, F. Dughiero, M. Forzan, E. Sieni, Migrationcorrected NSGA-II for improving multiobjective design optimization in electromagnetics, International Journal of Applied Electromagnetics and Mechanics. 51 (2016) 161-172. https://doi.org/10.3233/JAE-150171.
  • [15] K. Deb, Multi-objective optimization using evolutionary algorithms, 1st ed, John Wiley & Sons, Chichester ; New York, 2001.
  • [16] P. Di Barba, M.E. Mognaschi, A. Krawczyk, The biogeographyinspired optimization for the design of coils for nerve stimulation, in: IEEE, 2017: pp. 542-545. https://doi.org/10.1109/EUROCON.2017.8011170.
  • [17] P. Di Barba, M.E. Mognaschi, P. Venini, S. Wiak, Biogeography-inspired multiobjective optimization for helping MEMS synthesis, Archives of Electrical Engineering. 66 (2017). https://doi.org/10.1515/aee-2017-0046.
  • [18] Di Barba P., F. Dughiero, M. Forzan, M.E. Mognaschi, Sieni E., New solutions to a multi-objective benchmark problem of induction heating: an application of computational biogeography and evolutionary algorithms, Archives of Electrical Engineering. 67 (2018) 139-149. https://doi.org/10.24425/118997.
  • [19] FLUX, (Altair): https://altairhyperworks.com/product/flux, (n.d.).
  • [20] M. Bertoluzzo, G. Buja, H. K. Dashora, Design of DWC System Track with Unequal DD Coil Set, IEEE Transactions on Transportation Electrification. 3 (2017) 380-391. https://doi.org/10.1109/TTE.2016.2646740.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-7e4aaae4-568d-422d-b9c5-7c0c5a228730
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