Gen AI & Data Engineering Workshop [Part-2]: EV Charging Optimization Through V2G Technology Integration

Electric vehicles (EVs) have changed how we think about mobility, but they have also created new challenges for our power grids. Every time a large number of EVs plug in during peak hours, the grid feels the weight, which leads to an energy imbalance and wasted potential.

During our GenAI and Data Engineering Workshop, Dev Panchal, Associate Software Engineer at Azilen, decided to take this challenge personally. Working solo, he set out to build a model that treats every EV as more than just a power consumer – as a contributor to the grid itself.

His project, EV Charging Optimization using the Vehicle-to-Grid (V2G) concept, rethinks how energy flows between vehicles and the grid, creating a balance where both sides gain: grid stability and smarter energy use for every EV owner.

Here’s a glimpse of Dev presenting his project on V2G-based Intelligent Energy Management during the GenAI and Data Engineering Workshop.

The Thought Process Behind the EV Charging Optimization Using V2G Technology Integration

The idea started with a simple observation. When multiple EVs charge simultaneously, grid operators face three core challenges:

→ Uncontrolled demand spikes that cause instability and overload.

→ Simultaneous charging that leads to local congestion and power loss.

→ Lack of intelligence in the existing charging infrastructure to balance the load dynamically.

Dev aimed to address these pain points by creating an intelligent scheduling framework, one that could decide “when” an EV should charge and “when” it could supply power back to the grid.

The objective was twofold:

1️⃣ Reduce grid stress during high-demand periods.

2️⃣ Enable economic benefits for EV owners through smart scheduling.

His thought process centered on one principle:

Every EV has the potential to stabilize the grid and reward its owner at the same time.

Understanding the V2G Technology

The V2G concept flips the traditional idea of EV charging.

Instead of EVs being only consumers, they become bidirectional energy entities capable of returning power to the grid when needed.

Each EV acts as a mobile energy storage unit that contributes to grid stability, particularly during peak demand hours or renewable supply drops.

Framing the Problem

Dev mapped out the core challenge using real-world variables:

→ Grid demand varies significantly throughout the day.

→ EVs have unique State of Charge (SoC) and usage constraints.

→ Energy prices fluctuate by time and region.

Without an intelligent coordination layer, these variables create inefficiencies in how energy is consumed and distributed.

The goal was to build a smart orchestration layer that leverages machine learning and optimization algorithms to generate dynamic charge–discharge schedules.

This required a structured approach, starting from data acquisition to intelligent decision-making.

System Architecture of Data-Driven EV Charging Optimization Model

The solution framework was divided into four functional layers:

Future Scope and Enhancements in EV Charging Optimization Through V2G Technology Integration

Dev outlined clear directions for scaling the solution:

1️⃣ Dynamic Pricing Integration: Incorporate real-time energy market data to enhance profitability and responsiveness.

2️⃣ Renewable Integration: Connect solar and wind energy sources directly into the model to reduce grid dependency further.

3️⃣ Adaptive Learning Models: Introduce reinforcement learning to continuously improve scheduling efficiency based on feedback loops.

4️⃣ Scalability for Smart Cities: Extend the model to operate across distributed charging networks in smart city ecosystems.