Development of Artificial intelligence system for integration of fast charging station under smart grid environment
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Project Implementation Method (less than 2500 characters)
| Project Title |
Development of Artificial intelligence system for integration of fast charging station under smart grid environment
| Project Area of Specialization |
Electrical/Electronic Engineering | | Project Summary |
- In this project, we will use Artificial Intelligence techniques for the integration of Electric Vehicle Stations (EVs) into the smart grid.
- With the passage of time, the wider adoption of Electric Vehicles (EVs) is seen as a catalyst for the reduction of CO2 emissions and more intelligent transportation systems.
- The Smart Grid, an electricity supply network that uses digital communications technology to detect and react to local changes in usage, is the foundation that will enable the adoption of EVs in the marketplace.
- In this project, we will use CAN (Control Area Network) for communication and control between the different systems and components.
| | Project Objectives |
- Development of an Intelligent communication system between Smart Grid and Fast Charging Stations
- Virtual Smart Grid Development
- Intelligent Control System for Power Generation and Utilization
- Controlled Charging Mechanism based on Load Demand
- Intelligent Integration of charging mechanism with the Smart Grid
- Power Quality Monitoring (V, I, Power factor)
- Use of CAN (Controller Area Network)
ยก | | Project Implementation Method |
- A charger with a duty cycle control function is developed.
- A load variation graph (from the previous record) is set up by traffic load at different charging stations
- The data is sent from a server to the main computer (which is acting as a smart grid) using Wifi Module.
- Then the data are transmitted automatically to different charging stations using Controller area Network communication.
- The charging stations with changing their speed of battery storage relative to the predictable upcoming load relative to the graph.
- The overall impact of load variation on the grid could be countered easily by this method and energy utilization will be smart.
| | Benefits of the Project |
Problem: The large and variable load currents of ECSs can bring negative impacts to both EV-related power converters and power distribution systems if the energy flow is not regulated properly. Impacts: - System Instability.
- Instant variation in loads can cause a collapse of the system.
- Power Supply and Demand Issues.
- Tension in the system.
- Improper Load Determination causes inefficiency in the system.
Benefits: - We can predetermine load variations, prepare the system and prevent from losing system stability.
- The severe impact of large-scale Electric Charging stations on the grid could be eliminated.
- Energy could be generated and utilized smartly.
- Power Supply and demand issues could be countered.
- Tension in the power system could be reduced.
| | Technical Details of Final Deliverable |
- A charger with a duty cycle control function using the transistor switching is developed.
- By using the Image processing technique (By machine learning technique of AI) to measure vehicles at charging stations a load variation graph (from the previous record) is set up by traffic load at different charging stations
- The main control system is developed using AI to control charging at the charging stations. The data is sent from a server to the main computer (which is acting as a smart grid) using Wifi Module.
- Then the data are transmitted automatically to different charging stations using Controller area Network communication.
- The charging stations with changing their speed of battery storage relative to the predictable upcoming load relative to the graph.
- The overall impact of load variation on the grid could be countered easily by this method and energy utilization will be smart.
| | Final Deliverable of the Project |
HW/SW integrated system | | Core Industry |
Energy | | Other Industries |
Transportation | | Core Technology |
Artificial Intelligence(AI) | | Other Technologies |
Clean Tech | | Sustainable Development Goals |
Affordable and Clean Energy, Industry, Innovation and Infrastructure | Required Resources
| Item Name | Type | No. of Units | Per Unit Cost (in Rs) | Total (in Rs) |
| Arduino Uno | Equipment | 2 | 1500 | 3000 |
| Arduino Atmega2560 16AU Microcontroller | Equipment | 1 | 3500 | 3500 |
| MCP 2515 CAN Bus | Equipment | 3 | 800 | 2400 |
| 16*2 LCD Display | Equipment | 1 | 500 | 500 |
| I2C LCD module | Equipment | 1 | 180 | 180 |
| IRF-9530 MOSPHET | Equipment | 2 | 100 | 200 |
| 2n2222 NPN transistor | Equipment | 10 | 8 | 80 |
| 24V DC Bulb | Equipment | 1 | 50 | 50 |
| Bread Board | Equipment | 1 | 250 | 250 |
| Vero Board | Equipment | 2 | 150 | 300 |
| Connecting Wires | Equipment | 2 | 200 | 400 |
| Nodemcu ESP-8266 Wifi Module | Equipment | 2 | 600 | 1200 |
| 24V DC Battery | Equipment | 1 | 2000 | 2000 |
| 5V Charger | Equipment | 1 | 500 | 500 |
| | | Total in (Rs) | 14560 |