⚡ Charging-Simulator

A dynamic robot scheduling simulator for electric vehicle charging in smart campus or industrial parks.
This project supports multiple dispatching strategies, visual simulation, and performance analytics.

🧰 Tech Stack

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🚀 Project Overview

This system simulates autonomous charging robots that dynamically navigate and serve electric vehicles
based on real-time task generation, battery constraints, and optimization strategies.

• Frontend: React + Vite (JavaScript / TypeScript)
• Backend: Flask (Python)
• Visualization: SVG + visx (Bar, Threshold, Box, Violin)
• Simulation: Discrete-time tick-based system

🧠 Core Features

• Dynamic task generation and dispatching
• Supports multiple strategies: Hungarian, PPO, SPSO
• Real-time map rendering with robots, tasks, and chargers
• 2D grid environment with scalable map sizes (small, medium, large)
• Performance metrics: energy used, wait time, completion rate, etc.
• Responsive UI with detailed visual comparison

📂 Project Structure

charging-simulator/
│
├── Backend/ # Flask Backend
│ ├── app.py # API routes (init_map, next_step, get_state, metrics_summary)
│ ├── environment.py # Simulation environment
│ ├── hungarian.py # Hungarian algorithm
│ ├── ppo_custom.py # PPO agent wrapper
│ ├── spso_algorithm.py # SPSO metaheuristic scheduler
│ ├── train_ppp.py # PPO training interface
│ └── ppo——model/ 
│
├── Frontend/ # React + Vite Frontend
│ ├── src/
│ │ ├── components/ # Boxplot, Barchart, Violin, MapView, etc.
│ │ ├── pages/ # Overview.jsx, Metrics.jsx
│ │ ├── styles/ # Custom CSS files
│ │ ├── App.jsx # Application entry
│ │ └── main.jsx # ReactDOM root
│ └── node_modules/ 
│
├── .venv/
│
├── docs/
│ ├── FinalReport
│ ├── PRE
│ └── showvideo
│
└── README.md

📡 API Endpoints

Method	Endpoint	Description
GET	/api/init_map	Initialize map and state
GET	/api/next_step	Advance one tick
GET	/api/get_state	Get current state snapshot
GET	/api/metrics_summary	Retrieve aggregated performance results

📊 Metrics Dashboard (/metrics)

• 🔋 Total Energy Consumption (BarChart)
• 📉 Average Wait Time (LineChart)
• 📏 Wait Time Variance (Boxplot + ViolinPlot)
• ✅ Completion & Timeout Rate (Stacked Bar)

🗺 Scenario Parameters

Each experiment is defined by a 4-tuple:

• strategy: hungarian / ppo / spso
• scale: small / medium / large
• distribution: uniform / cluster / hybrid
• arrival: poisson / normal

The simulator combines these to generate scenarios like ppo_medium_uniform_poisson.

Run Instructions

1. Start Backend

cd backend
pip install -r requirements.txt
python app.py

2. Start Frontend

cd charging-frontend
npm install
npm run dev

Then open http://localhost:5173.

📷 Preview

📄 License

MIT License © 2025