Sairaj Arandhakar
Sathyabama Institute of Science and Technology, National Institute of Technology Andhra Pradesh
0000-0003-1765-592X
About
Sairaj Arandhakar is a Research Scholar in the Department of Electrical Engineering at the National Institute of Technology Andhra Pradesh, Tadepalligudem, where he is currently pursuing his Ph.D. His research interests broadly encompass Battery Management Systems (BMS) for electric vehicles, power electronic converters, renewable energy systems, and advanced control and optimization techniques. He holds a Bachelor of Technology degree in Electrical and Electronics Engineering from Vaagdevi College of Engineering, JNTU Hyderabad, and a Master of Technology degree in Electrical Engineering from CVR College of Engineering, JNTU Hyderabad, where he secured strong academic performance. He has significant academic and industrial exposure, having worked as a Technical Associate at Multibyte Technologies from 2013 to 2020. He also managed a NewGen IEDC-funded project on an “Automatic Solar Panel Cleaner,” focusing on innovative and automated maintenance solutions for photovoltaic systems. His scholarly contributions include several high-quality publications in reputed SCIE- and ESCI-indexed journals, particularly in the areas of intelligent active cell balancing, deep learning-based control, and energy storage systems for electric vehicles. He has received the Best Paper Award at an IEEE-sponsored international conference and has actively contributed to teaching and laboratory management at NIT Andhra Pradesh. Proficient in MATLAB/Simulink and RT-LAB, he is passionate about research-driven innovation aimed at improving energy efficiency, sustainability, and next-generation electric vehicle technologies.
Employment
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Sathyabama Institute of Science and Technology Assistant Professor2026 - Present
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National Institute of Technology Andhra Pradesh Research Scholar2022 - 2025
Education
Education history is unavailable.
Projects & Funding
Projects & funding information is unavailable.
Publications (16)
- Improved remaining useful life prediction of lithium-ion batteries using novel hybrid algorithm for enhanced accuracy Save
- An advanced optimal active cell equalization algorithm for electric vehicle battery system using deep learning evaluation: a comparative survey Save
- An ameliorated single-phase five-level multi-switch fault-tolerant inverter with reduced number of switches Save
- Deep learning-driven robust model predictive control based active cell equalisation for electric vehicle battery management system Save
- An exploratory Study on Intelligent Active Cell Balancing of Electric Vehicle Battery Management and Performance Using Machine Learning Algorithms Save
- A comprehensive analysis and future prospects on battery energy storage systems for electric vehicle applications Save
- State of charge estimation of lithium ion battery for electric vehicle using cutting edge machine learning algorithms: A review Save
- Photovoltaic string fault optimization using multi-layer neural network technique Save
- Optimizing Solar Panel Maximum Power Point Tracking and Parasitic Parameter Extraction in Partial Shading with Enhanced Slime Mold Optimization Save
- Soft-switching dual Active Bridge Converter-based Bidirectional On-Board Charger for Electric Vehicles under Vehicle-to-Grid and Grid-to-Vehicle Control Optimization Save