Assistant Professor
(Coordinator, Department of Electrical Engineering)


Research Area \ Research Statement \ Research Group Info

Research Area

  • Electric Vehicles (EVs) and Smart Mobility
  • Data-Driven Optimization and Stochastic Optimization
  • Reinforcement Learning and Artificial Intelligence in Power Systems
  • Smart Grids and Demand Response
  • Virtual Power Plants (VPPs)
  • Digital Twin Technology for EVs and Power Systems
  • Second-Life Applications of EV Batteries
  • Intelligent Transportation Systems 

 

Research Statement

My research focuses on developing intelligent, data-driven, and optimization-based solutions for next-generation energy and transportation systems, with a primary emphasis on electric vehicles and smart grids. I work at the intersection of optimization, machine learning, and control to address large-scale, complex, and uncertain systems.

A significant part of my work involves routing and scheduling of EV charging infrastructure, including mobile charging stations, to enhance grid reliability and reduce congestion in restructured power markets. I also explore reinforcement learning and AI techniques for adaptive decision-making in dynamic environments.

Recently, my research has expanded toward the development of digital twin frameworks for electric vehicles, enabling real-time monitoring, predictive maintenance, and fault diagnostics. Additionally, I am actively working on sustainable energy solutions using second-life EV batteries, particularly for off-grid and rural electrification through virtual power plants.

My long-term goal is to contribute to the development of resilient, efficient, and sustainable energy ecosystems by integrating advanced optimization techniques with emerging AI technologies.

 

Research Group / Lab Information

Smart Energy Systems and Electric Mobility Lab

The research group focuses on cutting-edge problems in electric mobility, smart grids, and AI-driven energy systems. The group aims to bridge theoretical advancements with practical implementations to address real-world challenges in modern power and transportation networks.

Key Focus Areas:

  • Optimization of large-scale EV charging networks
  • AI and reinforcement learning for smart grid applications
  • Digital twin development for EVs and power systems
  • Virtual power plants and distributed energy resource management
  • Second-life EV battery utilization for sustainable energy solutions

Facilities and Activities:

  • Simulation and modeling of power and transportation systems
  • Development of AI-based control and optimization algorithms
  • Collaboration with industry and government agencies
  • Student training in advanced tools such as Python, MATLAB, and optimization solvers

Opportunities:
The group welcomes motivated B.Tech, M.Tech, and PhD students interested in working on interdisciplinary research problems involving energy systems, optimization, and artificial intelligence.