SAKSHI SHARMA
Manipal Academy of Higher Education, Indian Institute of Technology Delhi, J.C. Bose University of Science & Technology, YMCA
0000-0002-4504-2883
About
Dr. Sakshi Sharma is a researcher in electrical engineering with expertise in intelligent energy systems, real-time simulation, and data-driven modeling for electric mobility and distributed energy resources. She recently completed her Ph.D. from the Indian Institute of Technology Delhi (IIT Delhi), where she was affiliated with the Centre for Automotive Research and Tribology (CART). Her doctoral work focused on AI-integrated battery diagnostics and state estimation, validated through Power Hardware-in-the-Loop (PHIL) platforms like Opal-RT and Speedgoat.
She holds a Master’s degree in Power Systems from Y.M.C.A. University of Science & Technology, where she was recognized with the Silver Medal for academic excellence, and a Bachelor’s degree in Electrical and Electronics Engineering from Dr. A.P.J. Abdul Kalam Technical University.
Her research contributions have been published in leading IEEE journals and international conferences. She was awarded the Best Presentation Award at the IEEE IECON 2024 in Chicago for her work on battery health modeling. Dr. Sharma has collaborated with industry partners and academic labs on scalable solutions for battery management, grid integration, and EV infrastructure planning.
Employment
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Manipal Academy of Higher Education2018 - 2019
Education
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Indian Institute of Technology Delhi2020 - Present
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J.C. Bose University of Science & Technology, YMCA Masters of Technology2015 - 2017
Projects & Funding
Projects & funding information is unavailable.
Publications (8)
- A Machine Learning Approach to Capacity Estimation of Lithium-Ion Batteries using Electrochemical Impedance Spectroscopy Save
- Lithium-Ion Battery State-of-Charge and State-of-Energy Simultaneous Estimation via Sparse- Quasi Recurrent Neural Networks(S-QRNN) Save
- Predicting State-of-Charge Using Gradient-Boosted SVR Ensemble Technique for Lithium Ion Battery Used in EVs Save
- Aging Responsive State of Charge Prediction of Lithium-Ion Battery Using Attention Mechanism Based Convolutional Neural Networks Save
- Combined SoC and SoE Estimation of Lithium-ion Battery using Multi-layer Feedforward Neural Network Save
- Review on technological advancement of lithium-ion battery states estimation methods for electric vehicle applications Save
- Neural Network based State of Charge Prediction of Lithium-ion Battery Save
- Hybrid T-I-D and Fuzzy Logic Based SVC Controller for Transient Stability Enhancement Save