SUDEEP GADUPUTI

Research-oriented Electrical Engineer with over 5 years of experience in electric vehicle motor drive systems, advanced control algorithms, and machine learning applications for electrical drives. Specialized in Model Predictive Control (MPC), AI/ML-based torque estimation, and parameter identification techniques for Interior Permanent Magnet Synchronous Motors (IPMSM) used in electric vehicle propulsion systems. Experienced in developing deep learning models for torque estimation and motor parameter prediction using Python, TensorFlow, MATLAB, and Simulink. Strong background in embedded systems programming using AVR, TI and Silicon Labs microcontrollers and real-time control implementation. Author of multiple Scopus-indexed research publications in electric vehicle motor control and predictive algorithms. Seeking R&D or industry roles in electric mobility, motor control systems, or intelligent control algorithms.

• Embedded Programming Essentials, Skill-Lync, 2024
• Fundamentals of Embedded Systems, Skill-Lync, 2024
• AVR Bare Metal Programming, Skill-Lync, 2024
• Software Verification & Validation, Skill-Lync, 2024

• AI-Based Torque Estimation for IPMSM Drives, Developed machine learning models for accurate torque prediction in electric vehicle motor drives., Used deep neural networks to estimate torque from motor current and flux parameters., Evaluated performance against conventional estimation techniques.
• Model Predictive Control for PMSM Drive, Designed and implemented MPC algorithms for torque and flux control of IPMSM drives., Compared performance with conventional control strategies.
• Embedded Systems Development, Developed embedded applications using Arduino and AVR microcontrollers., Implemented interrupt-based real-time control programs., Worked with timers, counters, and serial communication protocols.

• Telugu, English, Hindi, Tamil

• Gaduputi, S., & Sekhar, J. (2024). “Enhanced Torque Estimation Based on a Cognitive Training Model for Robust PMSM in EV Applications”. ITEGAM-JETIA, 10(50), 168-174. https://doi.org/10.5935/jetia.v10i50.1271.(scopus)
• G.Sudeep, J.N.Chandra Sekhar “Adaptive Model Predictive Control For IPMSM Based on Machine Learning Models”,J.Electrical System 20-6s (2024): 3081-3086. DOI: https://doi.org/10.52783/jes.8919(scopus)
• G.Sudeep, J.N.Chandra Sekhar “A Robust and Efficient Model Predictive Control for IPMSM Drive in Electric Vehicle Applications”, IJEER, Vol 13, Issue No.4, pp.954-959, (2025), 10.37391/ijeer.130435.(scopus)
• G.Sudeep, J.N.Chandra Sekhar, “Integrating Deep Learning with Model Predictive Control for Enhanced Torque estimation in permanent magnet synchronous Motor”, 5th International Conference on Engineering Science and Technology and Management (ICESTM-2024), organized by Narayana Engineering College (Autonomous), Nellore,Andhra Pradesh, India on 24.05.2024 to 25.05.2024
• G.Sudeep, J.N.Chandra Sekhar,“Enhancing the Performance of MPC for IPMSM Drive using Machine Learning Algorithms”, ANRF-SERB Sponsored International Conference on sustainable Electrical Engineering and Intelligent System (ICSEEIS) held on 10-03-2025 and 11-03-2025 organized by the Dept., of Electrical and Electronics Engineering. Sri Venkateswara college of Engineering, Tirupati, India.
• G.Sudeep, J.N.Chandra Sekhar, “Model Predictive Control-Based Torque Estimation of IPMSM Using Online Flux and Parameter Identification”, Electrifying the Future Innovation, Challenges and Sustainable Mobility (EFICS-2025),14th-15thJuly Dept of EEE, S V University, Tirupati, India.

Research Project Associate, Sri Venkateswara University, Tirupati, India, 11/01/20, Present, Advanced control and machine learning techniques for electric vehicle motor drives., Developed machine learning-based torque estimation models for IPMSM drives used in EV applications., Integrated Deep Learning models with Model Predictive Control (MPC) to enhance torque estimation accuracy., Implemented online motor parameter estimation using Recursive Least Squares (RLS)., Applied Extended Kalman Filter (EKF) techniques for inductance estimation in PMSM drives., Built deep neural network models using TensorFlow for high-accuracy torque prediction., Trained models using large datasets  for torque estimation models.

• Electric Vehicle Motor Drives
• Interior Permanent Magnet Synchronous Motor (IPMSM) Control
• Model Predictive Control (MPC)
• AI/ML for Electrical Drives
• Motor Parameter Estimation Techniques
• Embedded Control Systems for Motor Drives
• Intelligent Control Algorithms for Electric Mobility