Dr. J N Chandra Sekhar
Sri Venkateswara University, Satyabhama University, Jawaharlal Nehru Technological University Anantapur
0000-0003-2767-2467
About
Dr.J.N.Chandra Sekhar obtained his Bachelor’s degree in Electrical and Electronics Engineering in 2004 from JNT University, Hyderabad. Then obtained his Master’s degree in 2006 in Power Electronics and Industrial Drives from Satyabama University, Chennai. Further he was awarded with Ph. D in 2019 from Sri Venkateswara University, Tirupati. He joined as Assistant Professor (2009) in the Department of Electrical and Electronics Engineering, Sri Venkateswara University College of Engineering, Tirupati. His interest is to work on Power Electronic Converters in Electric Vehicles, developing Deep Learning models for Autonomous Driving systems and design of Super capacitors.
He is currently working as an Associate Professor in the Department of Electrical and Electronics Engineering Sri Venkateswara University College of Engineering, Tirupati.
Employment
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Sri Venkateswara University Associate Professor2009 - Present
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Sri Venkateswara University Associate Professor2009 - Present
Education
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Sri Venkateswara University Ph.D2010 - 2019
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Satyabhama University M.E.(Power Electronics and Industrial Drives)2004 - 2006
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Jawaharlal Nehru Technological University Anantapur B.Tech2000 - 2004
Projects & Funding
Projects & funding information is unavailable.
Publications (24)
- Boosting trifunctional electrocatalytic activity of Zn2.64Co0.15V2O11H6 with graphitic carbon nitride for efficient hydrogen evolution and zinc-air batteries Save
- Enhanced physical and electrochemical properties of selenium-doped zinc cobalt oxide for hybrid supercapacitors Save
- Ensemble Learning-Based Battery Health Estimation: A Comparative Study of RUL, SOC, and SOH Prediction Using Machine Learning Models Save
- An electro-optic sequence detector to detect 1010 bits with a lithium niobate-based Mach-Zehnder interferometer Save
- Supervised learning for fast inverse motor control mapping: a comparative study on SRM and BLDC motors Save
- Advances in Nano Metal Oxides and Nanotechnology for Lithium-Ion Batteries in EV: A Comprehensive Review Save
- An Overview of Remaining Useful Life Prediction of Battery Using Deep Learning and Ensemble Learning Algorithms on Data‐Dependent Models Save
- A Robust and Efficient Model Predictive Control for IPMSM Drive in Electric Vehicle Applications Save
- PREDICTING REMAINING USEFUL LIFE OF LITHIUM-ION BATTERIES FOR ELECTRIC VEHICLES USING MACHINE LEARNING REGRESSION MODELS Save
- RUL Prediction of Electric Battery with Error Indices Using Machine Learning Techniques Save