Modelling of dynamic modes in a DC motor for electric vehicle
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Modelling of dynamic modes in a DC motor for electric vehicle
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Main Article Content
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Abstract
The paper investigates mathematical models of a DC electric motor with independent excitation for electric vehicles and PWM as a speed control method. Differential equations of electromechanical state are written in normal Cauchy form. This representation simplifies the computational process, since integration is carried out by the explicit numerical method of Runge-Kutta of the fourth order, which is simpler than implicit and more accurate than single-step explicit methods. The symbolic programming language Force 2.0, which is a variant of the Fortran language, is used for modelling. Compared to mathematical packages of simulation modelling, it is convenient in terms of low time costs for compiling the program itself, since the program includes model equations together with initial conditions, a numerical method, and an integration procedure. The developed models take into account electromagnetic couplings of the motor's electrical circuits and make it possible to simulate dynamic operating modes. Such models can be used to analyse the operation of motors both autonomously and as an element of an electromechanical system, including valve converters. The operation and transient modes of a DC motor are simulated, the simulation results are given, and their analysis is presented. The results confirmed the correctness of the chosen approach to modelling and numerical methods, as well as compliance with the classical theory of electric machines.
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References
[1] Abouseda, A. I., Doruk, R., Emin, A., & Akdeniz, O. (2025). Modeling, dynamic characterization, and performance analysis of a 2.2 kW BLDC motor under fixed load torque levels and variable speed inputs: An experimental study. Actuators, 14(8), 400. https://doi.org/10.3390/act14080400 DOI: https://doi.org/10.3390/act14080400
[2] Arévalo, E., Herrera Hernández, R., Katselis, D., Reusser, C., & Carvajal, R. (2025). On modelling and state estimation of DC motors. Actuators, 14(4), 160. https://doi.org/10.3390/act14040160 DOI: https://doi.org/10.3390/act14040160
[3] Basu, P. K., & Dhasmana, H. (2023). Electromagnetic theory fundamentals. In: P. K. Basu, H. Dhasmana (red), Electromagnetic theory XII, 224. Springer. https://doi.org/10.1007/978-3-031-12318-4 DOI: https://doi.org/10.1007/978-3-031-12318-4
[4] Chamraz, S., & Balogh, R. (2025). Addressing practical limitations in DC motor speed control. In: Proceedings of Cybernetics & Informatics (K&I 2025), 1–7. IEEE. https://doi.org/10.1109/KI64036.2025.10916441 DOI: https://doi.org/10.1109/KI64036.2025.10916441
[5] Chen, Z., & Liu, J. (2024). Exploring the drive motor of electric vehicles: Structure, temperature rises, and operational control of permanent magnet motors. World Electric Vehicle Journal, 15(11), 483. https://doi.org/10.3390/wevj15110483 DOI: https://doi.org/10.3390/wevj15110483
[6] Dembitskyi, V., & Grabovets, V. (2023). Modeling of a power consumption by bus in the real operating conditions. Transportation Engineering, 14, 100216. https://doi.org/10.1016/j.treng.2023.100216 DOI: https://doi.org/10.1016/j.treng.2023.100216
[7] Elp, H. E., Altug, H., & İnan, R. (2024). Designing a brushed DC motor driver with a novel adaptive learning algorithm for the automotive industry. Electronics, 13(22), 4344. https://doi.org/10.3390/electronics13224344 DOI: https://doi.org/10.3390/electronics13224344
[8] Fazdi, M. F., & Hsueh, P.-W. (2023). Parameters identification of a permanent magnet DC motor: A review. Electronics, 12(12), 2559. https://doi.org/10.3390/electronics12122559 DOI: https://doi.org/10.3390/electronics12122559
[9] Gmyrek, Z. (2024). Optimal electric motor designs of light electric vehicles: A review. Energies, 17(14), 3462. https://doi.org/10.3390/en17143462 DOI: https://doi.org/10.3390/en17143462
[10] Hu, Z., Shen, S., Su, Y., & Li, Z. (2024). DC motor control: A global prescribed performance control strategy based on extended state observer. In: Proceedings of the 36th Chinese Control and Decision Conference (CCDC 2024), 3490–3495. IEEE. https://doi.org/10.1109/CCDC62350.2024.10587568 DOI: https://doi.org/10.1109/CCDC62350.2024.10587568
[11] Hua, L., Tang, J., & Zhu, G. (2025). A survey of vehicle system and energy models. Actuators, 14(1), 10. https://doi.org/10.3390/act14010010 DOI: https://doi.org/10.3390/act14010010
[12] Jinwen, Z., & Yumei, Q. (2023). Research on motor control of new energy electric vehicle. In: Proceedings of the IEEE 3rd International Conference on Electronic Technology, Communication and Information (ICETCI 2023), 637–640. IEEE. https://doi.org/10.1109/ICETCI57876.2023.10176454 DOI: https://doi.org/10.1109/ICETCI57876.2023.10176454
[13] Karahan, M. (2024). Modeling of a DC motor and position angle control using optimized PID controller. In: Proceedings of the 11th International Conference on Electrical and Electronics Engineering (ICEEE 2024), 107–110. IEEE. https://doi.org/10.1109/ICEEE62185.2024.10779219 DOI: https://doi.org/10.1109/ICEEE62185.2024.10779219
[14] Kinoti, E., Mosetlhe, T. C., & Yusuff, A. A. (2024). Multi-criteria analysis of electric vehicle motor technologies: A review. World Electric Vehicle Journal, 15(12), 541. https://doi.org/10.3390/wevj15120541 DOI: https://doi.org/10.3390/wevj15120541
[15] Kostiuchko, S., Polishchuk, M., Zabolotnyi, O., Tkachuk, A., & Twarog, B. (2021). The auxiliary parametric sensitivity method as a means of improving project management analysis and synthesis of executive elements. In: Emerging Technologies in Computing (iCETiC 2021) Vol. 395, 174–184. Springer. https://doi.org/10.1007/978-3-030-90016-8_12 DOI: https://doi.org/10.1007/978-3-030-90016-8_12
[16] Kuczmann, M. (2024). Review of DC motor modeling and linear control: Theory with laboratory tests. Electronics, 13(11), 2225. https://doi.org/10.3390/electronics13112225 DOI: https://doi.org/10.3390/electronics13112225
[17] Malyar, V., & Havd’yo, I. (2024). Mathematical model of transient processes of a DC motor with permanent magnet excitation. Journal of Computational and Electrical Engineering, 14(1), 12–18. https://doi.org/10.23939/jcpee2024.01.012 DOI: https://doi.org/10.23939/jcpee2024.01.012
[18] Mohanty, A., Manitha, P. V., & Lekshmi, S. (2022). Simulation of electric vehicles using permanent magnet DC motor. In: Proceedings of the 4th International Conference on Smart Systems and Inventive Technology (ICSSIT 2022), 1020–1025. IEEE. https://doi.org/10.1109/ICSSIT53264.2022.9716549 DOI: https://doi.org/10.1109/ICSSIT53264.2022.9716549
[19] Molina-Santana, E., Iturralde Carrera, L. A., Álvarez-Alvarado, J. M., Aviles, M., & Rodríguez-Resendiz, J. (2025). Modeling and control of a permanent magnet DC motor: A case study for a bidirectional conveyor belt’s application. Eng, 6(3), 42. https://doi.org/10.3390/eng6030042 DOI: https://doi.org/10.3390/eng6030042
[20] Pinto, V. H., Gonçalves, J., & Costa, P. (2020). Modeling and control of a DC motor coupled to a non-rigid joint. Applied System Innovation, 3(2), 24. https://doi.org/10.3390/asi3020024 DOI: https://doi.org/10.3390/asi3020024
[21] Škopek, K., Úradníček, J., Musil, M., Gašparovič, Ľ., & Havelka, F. (2025). Diagnosing and reducing noise and vibration in automotive DC motors with time synchronous averaging. Applied Sciences, 15(16), 8904. https://doi.org/10.3390/app15168904 DOI: https://doi.org/10.3390/app15168904
[22] Taleb, M. A., & Husi, G. (2025). Dynamic modeling of a three-phase BLDC motor using bond graph methodology. Actuators, 14(11), 523. https://doi.org/10.3390/act14110523 DOI: https://doi.org/10.3390/act14110523
[23] Wang, Z., Wang, W., Chen, W., Li, C., & Lin, Z. (2025). A high-precision torque control method for new energy vehicle motors based on virtual signal injection. Electronics, 14(7), 1443. https://doi.org/10.3390/electronics14071443 DOI: https://doi.org/10.3390/electronics14071443
[24] Wu, C.-H., Tsai, C.-L., & Yang, J.-M. (2025). Energy management design of dual-motor system for electric vehicles using whale optimization algorithm. Sensors, 25(14), 4317. https://doi.org/10.3390/s25144317 DOI: https://doi.org/10.3390/s25144317
[25] Zikri, A., Abdul Ghani, N. M., & Ahmad, S. (2025). Modeling, estimation and control of DC motors: An optimization approach using hardware-in-the-loop (HIL). In: Proceedings of the IEEE 8th International Conference on Electrical, Control and Computer Engineering (InECCE 2025), 239–244. IEEE. https://doi.org/10.1109/InECCE64959.2025.11150958 DOI: https://doi.org/10.1109/InECCE64959.2025.11150958
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