Systemic Multi-Objective Optimization of Induction Motor-Driven Electromechanical Systems
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Abstract
The optimization of electromechanical systems, such as those involving induction motors and gearboxes, is crucial for improving energy efficiency, system performance, and reliability in industrial applications. This paper presents an advanced methodology for optimizing the energy efficiency of electromechanical systems, integrating both mechanical and electrical subsystems to minimize the system's overall weight, energy losses, and transient response time. The optimization problem is approached holistically, considering the interdependence of various system parameters and applying multi-objective optimization techniques to address conflicting objectives. The analysis focuses on optimizing the gearbox speed ratio to minimize the relative weight of the motor-gearbox system while maintaining operational efficiency. A systemic approach, utilizing convex surrogate modeling and multi-stage gearboxes, is proposed to improve the scalability of the solution. The results demonstrate the existence of optimal gearbox speed ratios for various motor sizes and configurations, offering insights into the best design choices for minimizing system weight and optimizing performance. These findings apply to a range of real-world systems, including electric vehicles and industrial machinery, where minimizing weight and optimizing energy efficiency are critical for improving overall system performance and reducing operational costs.
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Paape, Nick, J. A. W. M. van Eekelen, and Michel A. Reniers. "Simulation-based optimization of a production system topology - a neural network-assisted genetic algorithm. "arXiv preprint arXiv:2402.01511 (2024). DOI: https://doi.org/10.48550/arXiv.2402.01511
Löppenberg, Marlon, and Andreas Schwung. "Self optimisation and automatic code generation by evolutionary algorithms in PLC based controlling processes." 2023. DOI: https://doi.org/10.1109/INDIN51400.2023.10218168
Muliak, Nazarii, et al. "Optimization of Electromechanical Systems by Intelligent Design Methods." 2022 IEEE XVIII International Conference on the Perspective Technologies and Methods in MEMS Design (MEMSTECH). IEEE, 2022. https://ieeexplore.ieee.org/document/10002914
Wiesheu, Michael, et al. "Combined parameter and shape optimization of electric machines with isogeometric analysis." Optimization and Engineering (2024): 1-28. DOI: https://doi.org/10.1007/s11081-024-09947-8
Dalton, Amaris, Karin Wolff, and Bernard Bekker. "Interdisciplinary research as a complicated system." International Journal of Qualitative Methods 21 (2022): DOI: https://doi.org/10.1177/16094069221100397.
Francoso Dal Piccol Sotto, Leo, et al. "Evolutionary Solution Adaption for Multi-Objective Metal Cutting Process Optimization." arXiv e-prints (2023): arXiv-2305. DOI:
https://doi.org/10.48550/arXiv.2305.19775.
Borsboom, Olaf, Mauro Salazar, and Theo Hofman. "Electric motor design optimization: A convex surrogate modeling approach." IFAC-PapersOnLine 55.24 (2022): 373-378. DOI: https://doi.org/10.1016/j.ifacol.2022.10.312
Li, Zhe, et al. "Multi-objective optimization of active suspension system in electric vehicle with In-Wheel-Motor against the negative electromechanical coupling effects." Mechanical Systems and Signal Processing 116 (2019): 545-565. https://www.sciencedirect.com/science/article/abs/pii/S0888327018304023
Zheng, Shicheng, et al. "Investigations on System Integration Method and Optimum Design Method of Electro-Mechanical Actuator System." Actuators. Vol. 12. No. 1. MDPI, 2023. https://www.mdpi.com/2076-0825/12/1/23
Luo, Jianqiang, Siqi Bu, and Jiebei Zhu. "Transition from electromechanical dynamics to quasi-electromechanical dynamics caused by participation of full converter-based wind power generation." Energies 13.23 (2020): 6270. https://www.mdpi.com/1996-1073/13/23/6270
Borsboom, Olaf, et al. "Control and Design Optimization of an Electric Vehicle Transmission Using Analytical Modeling Methods." arXiv preprint arXiv:2210.13287 (2022). DOI: https://doi.org/10.1016/j.ifacol.2023.10.1303
Avcu, Mehmet Enes, and Harun Gökçe. "Weight Optimization of the Gearbox Using Interior Point Method." International Journal of Precision Engineering and Manufacturing 24.1 (2023): 113-118. DOI: https://doi.org/10.1007/s12541-022-00723-1
Koli, H., & Chawla, Prof. M. P. S. (2022). Comparative Study of Electric Vehicle Battery Systems with Lithium-Ion and Solid State Batteries. In International Journal of Emerging Science and Engineering (Vol. 10, Issue 10, pp. 1–6). DOI: https://doi.org/10.35940/ijese.i2540.09101022
Ashish Rajeshwar Kulkarni, Sudhanshu Singh, Krishan Kant, Hemant Bharti, Experimental Electric Retrofitting of an Ice Vehicle with Simulation and Cost Analysis. (2020). In International Journal of Innovative Technology and Exploring Engineering (Vol. 9, Issue 9s, pp. 45–50). DOI: https://doi.org/10.35940/ijitee.i1008.0799s20
Dhrawadkar, S., Dani, U. H., Harmalkar, S., & Joshi, A. (2020). Design and Simulation of Electric Vehicle. In International Journal of Recent Technology and Engineering (IJRTE) (Vol. 9, Issue 4, pp. 131–133). DOI: https://doi.org/10.35940/ijrte.c4303.119420
Pandita, Dr. D., Bhatt, Dr. V., Kumar, Dr. V. V. R., & Gotise, Dr. P. (2023). Enablers of Electric Vehicles Adoption in India: A Review. In International Journal of Management and Humanities (Vol. 9, Issue 5, pp. 1–4). DOI: https://doi.org/10.35940/ijmh.e1550.019523
Kaushik, S. (2019). Modeling and Simulation of Electric Vehicle to Optimize Its Cost and Range. In International Journal of Engineering and Advanced Technology (Vol. 8, Issue 6, pp. 415–419). DOI: https://doi.org/10.35940/ijeat.e7819.088619