Torque enhancement of AFPMSM motor for electric vehicles using model predictive control combined with deep neural network
Email:
vothanhha.ktd@utc.edu.vn
Abstract
With the increasing demand for high-performance and energy-efficient electric vehicles, enhancing the torque control of traction motors has become a key research focus. Axial flux permanent magnet synchronous motors (AFPMSMs), known for their compact structure and high torque density, are widely used in modern electric drives. This paper presents a method to boost the torque of AFPMSMs in electric vehicles through a combination of predictive control (MPC) and a deep learning network (DNN) to optimize the cost function. MPC adjusts weight values to minimize the cost function (J) while adhering to voltage and current constraints. The DNN comprises five layers: an input layer with five neurons for parameters like required torque, motor speed, current, motor torque, and load torque; three hidden layers with 224 neurons using ReLU activation; and an output layer with three neurons and a Sigmoid activation. This architecture enables real-time weight adjustments for torque, current, and control voltage, enhancing observation accuracy, reducing torque ripples, and increasing system efficiency. The MPC-DNN controller’s adaptability ensures high precision even under noise or varying conditions. Implemented in Python, the proposed hybrid controller shows promising results, paving the way for advancements in intelligent control and modern electric drive systems.References
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[13]. Muhammad Rafaqat Ishaque, Muhammad Adil Khan, Muhammad Moin Afzal, Abdul Wadood, Seung-Ryle Oh, Muhammad Talha, Sang-Bong Rhee, Fuzzy Logic-Based Duty Cycle Controller for the Energy Management System of Hybrid Electric Vehicles with Hybrid Energy Storage System, Applied Sciences, 11(7) (2021) 3192. https://doi.org/10.3390/app11073192
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[16]. Yao-Hua Li, Ting-Xu Wu, Deng-Wang Zhai, Cheng-Hui Zhao, Yi-Fan Zhou, Yu-Gui Qin, Jin-Shi Su, Hui Qin, Hybrid Decision Based on DNN and DTC for Model Predictive Torque Control of PMSM, Symmetry, 14 (2022) 693. https://doi.org/10.3390/sym14040693
[17]. S. Vazquez, J. Rodriguez, M. Rivera, L. G. Franquelo, M. Norambuena, Model Predictive Control for Power Converters and Drives : Advances and Trends, IEEE Transactions on Industrial Electronics, 64 (2017) 935–947. https://doi.org/10.1109/TIE.2016.2625238
[18]. Shakil Mirza, Arif Hussain, New Approaches in Finite Control Set Model Predictive Control for Grid-Connected Photovoltaic Inverters: State of the Art, Solar, 4 (2024) 491-508. https://doi.org/10.3390/solar4030023
[19]. A. Markel, T. Brooker, T. Hendricks et al., ADVISOR : a systems analysis tool for advanced vehicle modelling, Journal of Power Sources, 110 (2020) 255–266. https://doi.org/10.1016/S0378-7753(02)00189
[2]. Merve Yıldırım, Mehmet Polat, H. Kurum, A survey comparing electric motor types and drives used for electric vehicles, 16th International Power Electronics and Motion Control Conference and Exposition, Antalya, Turkey, 21-24 Sept 2014. https://doi.org/10.1109/EPEPEMC.2014.6980715
[3]. Dimitrios Rimpas, Stavros D. Kaminaris, Dimitrios D. Piromalis, George Vokas, Konstantinos G. Arvanitis, Christos-Spyridon Karavas, Comparative Review of Motor Technologies for Electric Vehicles Powered by a Hybrid Energy Storage System Based on Multi-Criteria Analysis, Energies, 16 (2023) 2555. https://doi.org/10.3390/en16062555
[4]. S. Wahsh, A. Yassin, Thermal modelling of an AFPMSM : A review, Journal of Electrical Systems and Information Technology, 2 (2015) 18-26. https://doi.org/10.1016/j.jesit.2015.03.003
[5]. Y. Hori, Future vehicle driven by electricity and Control-research on four-wheel-motored 'UOT electric march II', IEEE Transactions on Industrial Electronics, 51 (2004) 954-962. https://doi.org/10.1109/TIE.2004.834944
[6]. X. D. T. Garcia, Comparison between FOC and DTC Strategies for Permanent Magnet Synchronous Motors, Advances in Electrical and Electronic Engineering, 5 (2011) 76-81. https://doi.org/10.11648/j.jeee.s.2015030201.30
[7]. Vo Thanh Ha, Torque Control of an In-Wheel Axial Flux Permanent Magnet Synchronous Motor using a Fuzzy Logic Controller for Electric Vehicles, Engineering, Technology & Applied Science Research, 13 (2023) 10357-10362. https://doi.org/10.48084/etasr.5689
[8]. Shuang Wang, Jianfei Zhao, Tingzhang Liu, Minqi Hua, Adaptive Robust Control System for Axial Flux Permanent Magnet Synchronous Motor of Electric Medium Bus Based on Torque Optimal Distribution Method, Energies, 12 (2019) 4681. https://doi.org/10.3390/en12244681
[9]. D. Croccolo, M. D. Agostinis, N. Vincenzi, Structural Analysis of an Articulated Urban Bus Chassis via Finite Element Method : A Methodology Applied to a Case Study, Journal of Mechanical Engineering, 57 (2011) 799-809. https://doi.org/10.1109/IRANIANCEE.2010.5506971
[10]. A. Daghigh, M. B. B. Sharifian, M. Farasat, A Modified Direct Torque Control of IPM Synchronous Machine Drive with Constant Switching Frequency and Low Ripple in Torque, 18th Iranian Conference on Electrical Engineering (ICEE), Isfahan, Iran, (2010). https://doi.org/10.1109/IRANIANCEE.2010.5506971
[11]. Pooja Bhatt, Hemant Mehar, Manish Sahajwani, Electrical Motors for Electric Vehicle – A Comparative Study, Proceedings of Recent Advances in Interdisciplinary Trends in Engineering & Applications (RAITEA), (2019). https://ssrn.com/abstract=3364887
[12]. D. Tan, C. Lu, The Influence of the Magnetic Force Generated by the In-Wheel Motor on the Vertical and Lateral Coupling Dynamics of Electric Vehicles, IEEE Transactions on Vehicular Technology, 65 (2016) 4655-4668. https://doi.org/10.1109/TVT.2015.2461635
[13]. Muhammad Rafaqat Ishaque, Muhammad Adil Khan, Muhammad Moin Afzal, Abdul Wadood, Seung-Ryle Oh, Muhammad Talha, Sang-Bong Rhee, Fuzzy Logic-Based Duty Cycle Controller for the Energy Management System of Hybrid Electric Vehicles with Hybrid Energy Storage System, Applied Sciences, 11(7) (2021) 3192. https://doi.org/10.3390/app11073192
[14]. Nagham S. Farhan, Fadil A. Hasan, Abd al-Rahim Dhiyab Humud, Field-oriented control of AFPMSM for an electrical vehicle using adaptive neuro-fuzzy inference system (ANFIS), Engineering and Technology Journal, 39 (2021) 1571-1582. https://doi.org/10.30684/etj.v39i10.1969
[15]. Mengxue Zou, Shuang Wang, Mengqi Liu, Kang Chen, Model Predictive Control of Permanent-Magnet Synchronous Motor with Disturbance Observer, IEEE International Symposium on Predictive Control of Electrical Drives and Power Electronics (PRECEDE), (2019). https://doi.org/10.1109/PRECEDE.2019.8753245
[16]. Yao-Hua Li, Ting-Xu Wu, Deng-Wang Zhai, Cheng-Hui Zhao, Yi-Fan Zhou, Yu-Gui Qin, Jin-Shi Su, Hui Qin, Hybrid Decision Based on DNN and DTC for Model Predictive Torque Control of PMSM, Symmetry, 14 (2022) 693. https://doi.org/10.3390/sym14040693
[17]. S. Vazquez, J. Rodriguez, M. Rivera, L. G. Franquelo, M. Norambuena, Model Predictive Control for Power Converters and Drives : Advances and Trends, IEEE Transactions on Industrial Electronics, 64 (2017) 935–947. https://doi.org/10.1109/TIE.2016.2625238
[18]. Shakil Mirza, Arif Hussain, New Approaches in Finite Control Set Model Predictive Control for Grid-Connected Photovoltaic Inverters: State of the Art, Solar, 4 (2024) 491-508. https://doi.org/10.3390/solar4030023
[19]. A. Markel, T. Brooker, T. Hendricks et al., ADVISOR : a systems analysis tool for advanced vehicle modelling, Journal of Power Sources, 110 (2020) 255–266. https://doi.org/10.1016/S0378-7753(02)00189
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Received
25/03/2025
Revised
09/04/2025
Accepted
10/04/2025
Published
15/04/2025
Type
Research Article
How to Cite
Nguyễn Văn, H., & Võ Thanh, H. (1744650000). Torque enhancement of AFPMSM motor for electric vehicles using model predictive control combined with deep neural network . Transport and Communications Science Journal, 76(3), 228-242. https://doi.org/10.47869/tcsj.76.3.3
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