Article: Performance enhancement of DC motor drive for electric vehicle application by using deep neural network Journal: International Journal of Innovative Computing and Applications (IJICA) 2025 Vol.15 No.3 pp.145 - 155 Abstract: In order to address the drawbacks of conventional proportional-integral (PI) controllers, including their inability to effectively handle nonlinearity, parameter fluctuations, and external disturbances, this research proposes a deep learning (DL)-based controller for DC motor drives in electric vehicles (EVs). The development of a custom neural network (CNN) controller is contrasted with neural net fitting (NNF) and PI controllers. The CNN controller is implemented and evaluated under various failure conditions and speed fluctuations after the DC motor drive system has been modelled. The CNN controller reduces overshoot by 25%, settling time by 30%, and speed tracking accuracy by 20%, according to the results. It keeps the system stable during high-resistance faults with a torque variation of only 15 Nm, whereas the PI controller becomes unstable when the torque variation reaches 25 Nm. This research comes to the conclusion that DL-based CNN controllers notably improve EV motor driving performance and dependability. Inderscience Publishers - linking academia, business and industry through research

Title: Performance enhancement of DC motor drive for electric vehicle application by using deep neural network

Authors: Anurag Singh; Shekhar Yadav; Nitesh Tiwari; Sandesh Patel

Addresses: Department of Electrical Engineering, Madan Mohan Malaviya University of Technology, Gorakhpur, UP, India ' Department of Electrical Engineering, Madan Mohan Malaviya University of Technology, Gorakhpur, UP, India ' Department of Electrical Engineering, Madan Mohan Malaviya University of Technology, Gorakhpur, UP, India ' Department of Electrical Engineering, Madan Mohan Malaviya University of Technology, Gorakhpur, UP, India

Abstract: In order to address the drawbacks of conventional proportional-integral (PI) controllers, including their inability to effectively handle nonlinearity, parameter fluctuations, and external disturbances, this research proposes a deep learning (DL)-based controller for DC motor drives in electric vehicles (EVs). The development of a custom neural network (CNN) controller is contrasted with neural net fitting (NNF) and PI controllers. The CNN controller is implemented and evaluated under various failure conditions and speed fluctuations after the DC motor drive system has been modelled. The CNN controller reduces overshoot by 25%, settling time by 30%, and speed tracking accuracy by 20%, according to the results. It keeps the system stable during high-resistance faults with a torque variation of only 15 Nm, whereas the PI controller becomes unstable when the torque variation reaches 25 Nm. This research comes to the conclusion that DL-based CNN controllers notably improve EV motor driving performance and dependability.

Keywords: electric vehicles; deep neural networks; custom neural networks; DC motor drives.

DOI: 10.1504/IJICA.2025.148630

International Journal of Innovative Computing and Applications, 2025 Vol.15 No.3, pp.145 - 155

Received: 25 Jan 2025
Accepted: 05 May 2025
Published online: 16 Sep 2025 *

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Title: Performance enhancement of DC motor drive for electric vehicle application by using deep neural network

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