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@article{185316,
        author = {Miss. Sujanwar Mohini Sanjay and Prof. A.B. Ghule and Prof. Dr. S.V. Yerigeri},
        title = {Smart Speed Control of Switched Reluctance Motor in EVs Using Machine Learning Algorithms},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {5},
        pages = {856-860},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=185316},
        abstract = {Electric Vehicles (EVs) demand efficient, reliable, and cost-effective motor drive systems to enhance performance and energy utilization. Switched Reluctance Motors (SRMs) have gained increasing attention due to their simple structure, robustness, high torque-to-weight ratio, and suitability for wide speed operation. However, nonlinear magnetic characteristics, torque ripple, and parameter variations in SRMs pose significant challenges in achieving precise speed control using conventional methods such as PID or linear controllers. To address these issues, this paper proposes a smart speed control strategy for SRMs in EV applications using Machine Learning (ML) algorithms. The proposed approach leverages data-driven models, including Artificial Neural Networks (ANN) and Reinforcement Learning (RL), to predict motor dynamics and optimize control parameters adaptively. Simulation and experimental analysis demonstrate improved dynamic response, reduced torque ripple, and enhanced energy efficiency compared to traditional controllers. The results validate the effectiveness of ML-based controllers in advancing the performance and reliability of EV drives powered by SRMs.},
        keywords = {Switched Reluctance Motor (SRM); Electric Vehicle (EV); Machine Learning (ML); Artificial Neural Network (ANN); Reinforcement Learning (RL); Smart Speed Control; Torque Ripple Reduction},
        month = {October},
        }