Kriging-Assisted Multi-Objective Optimization Framework for Electric Motors Using Predetermined Driving Strategy
In this paper, a multi-objective optimization framework for electric motors and its validation is presented. This framework is suitable for the optimization of design variables of electric motors based on a predetermined driving strategy using MATLAB R2019b and Ansys Maxwell 2019 R3 software. The fr...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-06-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/16/12/4713 |
| _version_ | 1827737577984098304 |
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| author | György Istenes Zoltán Pusztai Péter Kőrös Zoltán Horváth Ferenc Friedler |
| author_facet | György Istenes Zoltán Pusztai Péter Kőrös Zoltán Horváth Ferenc Friedler |
| author_sort | György Istenes |
| collection | DOAJ |
| description | In this paper, a multi-objective optimization framework for electric motors and its validation is presented. This framework is suitable for the optimization of design variables of electric motors based on a predetermined driving strategy using MATLAB R2019b and Ansys Maxwell 2019 R3 software. The framework is capable of managing a wide range of objective functions due to its modular structure. The optimization can also be easily parallelized and enhanced with surrogate models to reduce the runtime. The framework is validated by manufacturing and measuring the optimized electric motor. The method’s applicability for solving electric motor design problems is demonstrated via the validation process. A test application is also presented, in which the operating points of a predetermined driving strategy provide the input for the optimization. The kriging surrogate model is used in the framework to reduce the runtime. The results of the optimization and the framework’s benefits and drawbacks are discussed through the provided examples, in addition to displaying the properly applicable design processes. The optimization framework provides a ready-to-use tool for optimizing electric motors based on the driving strategy for single- or multi-objective purposes. The applicability of the framework is demonstrated by optimizing the electric motor of a world recorder energy-efficient race vehicle. In this application, the optimization framework achieved a 2% improvement in energy consumption and a 9% increase in speed at a rated DC voltage, allowing the motor to operate at desired working points even with low battery voltage. |
| first_indexed | 2024-03-11T02:31:43Z |
| format | Article |
| id | doaj.art-708eac4aa9ae49409c9392123ba47ab7 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-11T02:31:43Z |
| publishDate | 2023-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-708eac4aa9ae49409c9392123ba47ab72023-11-18T10:13:07ZengMDPI AGEnergies1996-10732023-06-011612471310.3390/en16124713Kriging-Assisted Multi-Objective Optimization Framework for Electric Motors Using Predetermined Driving StrategyGyörgy Istenes0Zoltán Pusztai1Péter Kőrös2Zoltán Horváth3Ferenc Friedler4Vehicle Industry Research Center, Széchenyi István University, Egyetem Tér 1, 9026 Győr, HungaryVehicle Industry Research Center, Széchenyi István University, Egyetem Tér 1, 9026 Győr, HungaryVehicle Industry Research Center, Széchenyi István University, Egyetem Tér 1, 9026 Győr, HungaryDepartment of Mathematics and Computational Sciences, Széchenyi István University, Egyetem Tér 1, 9026 Győr, HungaryVehicle Industry Research Center, Széchenyi István University, Egyetem Tér 1, 9026 Győr, HungaryIn this paper, a multi-objective optimization framework for electric motors and its validation is presented. This framework is suitable for the optimization of design variables of electric motors based on a predetermined driving strategy using MATLAB R2019b and Ansys Maxwell 2019 R3 software. The framework is capable of managing a wide range of objective functions due to its modular structure. The optimization can also be easily parallelized and enhanced with surrogate models to reduce the runtime. The framework is validated by manufacturing and measuring the optimized electric motor. The method’s applicability for solving electric motor design problems is demonstrated via the validation process. A test application is also presented, in which the operating points of a predetermined driving strategy provide the input for the optimization. The kriging surrogate model is used in the framework to reduce the runtime. The results of the optimization and the framework’s benefits and drawbacks are discussed through the provided examples, in addition to displaying the properly applicable design processes. The optimization framework provides a ready-to-use tool for optimizing electric motors based on the driving strategy for single- or multi-objective purposes. The applicability of the framework is demonstrated by optimizing the electric motor of a world recorder energy-efficient race vehicle. In this application, the optimization framework achieved a 2% improvement in energy consumption and a 9% increase in speed at a rated DC voltage, allowing the motor to operate at desired working points even with low battery voltage.https://www.mdpi.com/1996-1073/16/12/4713multi-objective optimizationkriging surrogate modelelectric motorsdriving strategyelectric drivesfinite element method |
| spellingShingle | György Istenes Zoltán Pusztai Péter Kőrös Zoltán Horváth Ferenc Friedler Kriging-Assisted Multi-Objective Optimization Framework for Electric Motors Using Predetermined Driving Strategy Energies multi-objective optimization kriging surrogate model electric motors driving strategy electric drives finite element method |
| title | Kriging-Assisted Multi-Objective Optimization Framework for Electric Motors Using Predetermined Driving Strategy |
| title_full | Kriging-Assisted Multi-Objective Optimization Framework for Electric Motors Using Predetermined Driving Strategy |
| title_fullStr | Kriging-Assisted Multi-Objective Optimization Framework for Electric Motors Using Predetermined Driving Strategy |
| title_full_unstemmed | Kriging-Assisted Multi-Objective Optimization Framework for Electric Motors Using Predetermined Driving Strategy |
| title_short | Kriging-Assisted Multi-Objective Optimization Framework for Electric Motors Using Predetermined Driving Strategy |
| title_sort | kriging assisted multi objective optimization framework for electric motors using predetermined driving strategy |
| topic | multi-objective optimization kriging surrogate model electric motors driving strategy electric drives finite element method |
| url | https://www.mdpi.com/1996-1073/16/12/4713 |
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