This project focuses on the development of a novel green charging station testbed to accelerate sustainable electric vehicle (EV) adoption. It integrates renewable energy sources (solar, wind) with battery energy storage to reduce grid dependence. A digital twin model will be built to simulate and optimize system performance. The project’s emphasis is on the development of optimized smart charging techniques that align with renewable energy availability, ensuring a reliable and eco-friendly EV charging infrastructure. 

Key Research Aims/Objectives: 

• Construct a physical green charging station testbed powered primarily by renewable energy sources for real world data collection and charging scheme algorithms verification. 

• Create a digital twin of the network EV smart charging for virtual experimentation and optimization of operating scenarios. 

Design and implement EV smart charging techniques that maximize renewable energy use, minimize grid dependence, and reduce cost and CO2 emissions. 

Schematic diagram of a hybrid PV-wind system with BESS to support EV charging station 

Research design and methods 

The project is divided into the following 4 work packages (WPs), spanning over three years:  

• Work Package 1: EV Smart Charging Design & Construction 

• Work Package 2: Building open-source computer-based model for the EC charging system 

• Work Package 3: Modeling Optimized Energy Use for Renewable-Driven EV Smart Charging  

• Work Package 4: Validating EV Charging Behavior Optimization with Uncertain Data 

1. WP1: EV Smart charging Design & Construction

• Objectives: Secure a suitable location, design the EV smart charging station, and construct the physical testbed. 

Steps: 

• Conduct a site survey (solar/wind resources, grid proximity, EV user patterns). 

• Design the renewable energy system for EV charging stations (solar modules, small wind-turbine system), Battery energy storage (BESS), and EV Charging (AC, DC charging) 

• Obtain permits and finalize engineering plans. 

• Construct the EV smart charging stations and integrate data collection and controlling systems. 

2. WP2: Building open-source computer-based model for the EC charging system

• Objective: Create a digital twin of the EV smart charging station and develop strategies for scaling the model to represent a distributed network of green charging stations across a city or country. 

Steps: 

• Data collection and analysis: Collect relevant data on EV usage patterns, charging infrastructure, electricity demand, and grid constraints. 

• Select a simulation tool for modeling renewable systems, energy storage, and EV charging: Utilize or develop a simulation platform capable of handling complex hybrid renewable energy systems and real-time power flow optimization. 

• Develop models for solar and wind generation based on site-specific data. 

• Model the energy storage system (capacity, charging/discharging dynamics). 

• Create EV charging station models with various power levels and user demand patterns. 

3. WP3: Modeling Optimized Energy Use for Renewable-Driven EV Smart Charging

• Objectives: Design smart charging techniques that optimize energy use, prioritizing renewables while ensuring EV charging needs are met. 

Steps: 

• Develop models that incorporate uncertainty (energy generation, EV demand, grid availability). 

• Design smart charging algorithms (e.g., time-of-use pricing, grid optimization, V2G). 

• Simulate and evaluate algorithm performance within the digital twin (metrics: grid dependence, renewable energy use, user satisfaction) 

4. WP4: Validating EV Charging Behavior Optimization with Uncertain Data

• Objective: Analyze data from the EV smart charging station, compare it to the digital twin, assess performance, and draw conclusions. 

Steps: 

• Collect real-time data on energy generation, battery energy storage use, and EV charging patterns. 

• Validate the digital twin’s accuracy by comparing it with physical testbed data. 

• Evaluate the economic feasibility of the green charging model with traditional stations. 

• Analyze the impact of EV smart charging on CO2 emissions and grid efficiency. 

• Generate a comprehensive project report for dissemination.