EV Data Collection and Visualization System (EVDCVS)

This project is a data collection and visualization system for an Electric Vehicle (EV) using a Raspberry Pi with an RFM9x LoRa radio. The system collects data, stores it in CSV files, and visualizes it through a web interface.

Table of Contents

• Overview
• Components
  • Python Script (main.py)
  • Node.js Server (server.js)
  • Frontend Components (front_end directory)
• Data Flow
• Installation
• Usage
• License

Overview

The system collects real-time data from various sensors on the EV using a Raspberry Pi connected to an RFM9x LoRa radio. The data is processed and saved into CSV files, which are then served by a Node.js server and visualized in a web-based user interface.

Components

Python Script (main.py)

• Purpose: Collects data from the LoRa radio and stores it in a CSV file.
• Functionality:
  • Configures the LoRa radio using the adafruit_rfm9x library.
  • Checks for the next available file name and prepares to write data.
  • Enters an infinite loop where it:
    • Receives data packets from the LoRa radio.
    • Parses and processes incoming data.
    • Logs data to a CSV file with a timestamp and counter.
  • Provides error handling for data processing.

Node.js Server (server.js)

• Purpose: Serves the CSV data and frontend files over HTTP.
• Functionality:
  • Uses the Express framework to serve static files from the front_end directory.
  • Provides an endpoint (/list-files) to list all CSV files available in the front_end directory.
  • Fetches and returns the most recent CSV data upon request.

Frontend Components (front_end directory)

• index.html: Main HTML page for the web interface.
• dropdown.js: Handles user interaction for selecting different data views.
• main.js: Fetches the latest CSV data and updates the UI dynamically.
• objects.js: Defines classes for various data visualization components (e.g., odometers, graphs).
• style.css: CSS styles for layout and design.
• Sample Data (001.data.csv): Example CSV file containing collected data.

Data Flow

1. Data Collection: The Raspberry Pi collects data from the EV via the LoRa radio.
2. Data Storage: The data is processed by main.py and stored in CSV files.
3. Data Serving: The Node.js server (server.js) serves the CSV files and static frontend.
4. Data Visualization: The web interface fetches and displays the latest data dynamically for real-time monitoring.

Installation

1. Install necessary Python libraries:

pip install adafruit-circuitpython-rfm9x
Set Up Node.js Server:

2. Install node.js:

curl -o- https://raw.githubusercontent.com/nvm-sh/nvm/v0.40.0/install.sh | bash
nvm install 20

3. Install dependencies:

npm install express

Usage

1. Make sure that server.js is running:

node server.js

2. Run the Python Script:

python3 main.py

3. Run the Node.js server: Access the Web Interface:

Open a web browser and navigate to http://electric.local (or the IP address of the Raspberry Pi).

License

This project is licensed under the MIT License.

Also, this whole README.md file was writen by ChatGPT. 😀