A Three-Day National Workshop on AI-Integrated, Quantum-Aware Embedded Nitride Semiconductor Systems for Sustainable, High- Efficiency Autonomous Wireless EV Charging Systems 2026

SCOPE OF THE WORKSHOP
This workshop focuses on sustainable, high-efficiency autonomous wireless EV charging systems using AI- integrated, and quantum-aware embedded nitride semiconductor technologies. It covers wide bandgap devices (GaN/SiC), wireless power transfer, and embedded control for smart charging. Emphasis is on AI-driven optimization, predictive maintenance, and energy management, along with quantum-inspired modelling for performance enhancement. The workshop also explores smart grid integration, V2G systems, and renewable energy utilization, promoting scalable, intelligent, and eco-friendly EV charging infrastructure aligned with net-zero and sustainable mobility goals.

OBJECTIVES OF THE WORKSHOP Explore AI- and quantum-enabled embedded nitride semiconductor technologies for wireless EV charging.
Understand high-efficiency wireless power transfer and embedded system design.
Promote sustainable, energy-efficient EV charging with smart grid and V2G integration. Encourage research and industry collaboration in next-generation electric mobility

SCIENTIFIC COVERAGE AND TRAINING MODULES
AI- and Quantum-Enabled Embedded Nitride
Semiconductor Technologies
Autonomous Wireless EV Charging Systems and
High-Efficiency Wireless Power Transfer (WPT)
Embedded System Design, Real-Time Control,
and Predictive Maintenance
Sustainable EV Charging with Smart Grid, V2G,
and Renewable Energy Integration
Digital Twins and Quantum-Aware Algorithms
Future Trends in Electric Mobility, Energy- Efficient Power Electronics, and Smart Charging Infrastructure

TRAINING MODULES:
Introduction to Nitride Semiconductors and
Embedded Systems
AI Algorithms for Autonomous EV Charging
Quantum-Aware Modeling and Optimization
Techniques
Wireless Power Transfer Principles and High- Efficiency Converter Design
Embedded System Design for Real-Time Control and Monitoring
Smart Grid, V2G Integration, and Renewable
Energy Applications
Simulation and Digital Twin-Based Performance
Evaluation
Case Studies and Hands-On Design Exercises

RESOURCE PERSONS
Experts in AI-Integrated Embedded Systems
Specialists in Quantum-Aware Semiconductor
Technologies
Researchers in Nitride Semiconductor Devices
(GaN/SiC)
Professionals in Wireless EV Charging and
Power Electronics
Academicians in Embedded System Design and
Real-Time Control
Experts in Smart Grid, V2G, and Renewable
Energy Integration

TRAINING MODULES:
Introduction to Nitride Semiconductors and
Embedded Systems
AI Algorithms for Autonomous EV Charging
Quantum-Aware Modeling and Optimization
Techniques
Wireless Power Transfer Principles and High- Efficiency Converter Design
Embedded System Design for Real-Time Control and Monitoring
Smart Grid, V2G Integration, and Renewable
Energy Applications
Simulation and Digital Twin-Based Performance
Evaluation
Case Studies and Hands-On Design Exercises

TRAINING MODULES:
Introduction to Nitride Semiconductors and
Embedded Systems
AI Algorithms for Autonomous EV Charging
Quantum-Aware Modeling and Optimization
Techniques
Wireless Power Transfer Principles and High- Efficiency Converter Design
Embedded System Design for Real-Time Control and Monitoring
Smart Grid, V2G Integration, and Renewable
Energy Applications
Simulation and Digital Twin-Based Performance
Evaluation
Case Studies and Hands-On Design Exercises

Registration opens : 20/09/2026
Registration closes : 25/09/2026
Selection list by E-mail : 05/10/2026

Research Scholar/Student : Rs. 300
Academicians/Teacher : Rs. 400
Industry Personnel : Rs. 500
Faculty, Students, Research Scholars from
UG & PG Colleges/Institutes
Participants will be selected on a first- come basis.
Limited to 75 participants.

1) From Ukkadam → Hindusthan Institute of Technology

Bus:

Go to Ukkadam Bus Stand.
Take Pollachi / Kinathukadavu route bus.
Get down at Othakkalmandapam / Hindusthan College stop.
Walk or take auto to campus.
Approx: 13–15 km, 35–45 minutes (depending on traffic).

Car/Bike:
Ukkadam → Sungam → Podanur → Eachanari → Othakkalmandapam → Hindusthan

Distance: ~13 km
Time: ~30–40 minutes.

Train:

Nearest major station: Coimbatore Junction.
From Junction take bus/taxi via Podanur–Pollachi Road.
Railway station to campus is around 13–15 km.
2) From Gandhipuram → Hindusthan Institute of Technology

Bus:

Gandhipuram → Ukkadam (city bus)
Ukkadam → Pollachi route bus
Get down at Othakkalmandapam.

OR

Take buses towards Eachanari / Pollachi Road and get down near campus.

Car:
Gandhipuram → Town Hall → Ukkadam → Podanur → Eachanari → Othakkalmandapam

Approx: 18–20 km
Time: 45–60 minutes.
3) From Pollachi → Hindusthan Institute of Technology

Bus:

Take Pollachi–Coimbatore bus.
Get down at Othakkalmandapam / Hindusthan stop.
The college also has a Pollachi transport route (college bus route no. 30).

Car:
Pollachi → Kinathukadavu → Othakkalmandapam → Hindusthan

Approx: 25–30 km
Time: 40–50 minutes.