⚡ EV Trip Optimizer 🚗🔋

An AI-powered Electric Vehicle (EV) Trip Optimization System that helps drivers maximize efficiency and range.
The system simulates real-world EV driving conditions, optimizes trip strategy, and visualizes energy usage through interactive charts.

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✨ Features

✅ Intuitive Web UI for trip planning
✅ FastAPI backend for serving optimization logic & ML predictions
✅ Input form with real-world trip parameters:

• Distance to travel
• Current Battery %
• Outside & Battery Temperature
• Traffic condition (Light / Medium / Heavy)
• Driving condition (Highway / Intracity / Uphill / Downhill)
• Mode preference (Eco / Normal / Sport / Custom)
• Payload (passengers & luggage)

✅ Output includes:

• ⚙️ Recommended driving settings (Speed, AC %, Regen braking, Acceleration limit)
• 🔋 Predicted range & estimated battery left after trip
• 📊 Optimization summary cards
• 📈 Interactive Visualizations:
  • Battery % vs Distance (Line Chart)
  • Speed vs Consumption (Scatter/Line Chart)
  • Mode Comparison (Bar Chart)
  • Energy Breakdown (Pie Chart)
  • Projected Range (Gauge Chart)

✅ Future-ready:

• Integrate ML model (Regression + Optimization algorithms)
• Support live telemetry and map-based simulation

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🛠️ Tech Stack

• Frontend: HTML, JavaScript, Plotly.js (charts), Axios (API calls)
• Backend: FastAPI (Python), Uvicorn
• Machine Learning (Planned): Scikit-learn / TensorFlow / PyTorch
• Database (Planned): PostgreSQL / SQLite (for trip history & training data)
• Deployment: Docker, Cloud (AWS/GCP/Render)

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🔄 System Workflow

1. User enters trip details on the frontend form
2. Data sent to FastAPI backend via REST API
3. Backend applies:
   • Rule-based optimization (initial version)
   • ML-powered prediction (future upgrade)
4. Results returned as:
   • JSON with recommended settings
   • Numerical predictions (range, battery left)
5. Frontend renders:
   • Optimization summary cards
   • Dynamic interactive graphs using Plotly.js

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🏗️ Low Level Design (LLD)

📌 Components

• Frontend Module

  • FormHandler → Collects user input & validates data
  • APICaller → Sends request to backend using Axios
  • ChartManager → Renders Plotly.js charts
  • UIManager → Displays results in structured format

• Backend Module (FastAPI)

  • InputModel (Pydantic) → Schema validation
  • OptimizationEngine → Core logic for speed, AC, regen braking
  • MLModelHandler (Future) → Loads trained ML model for predictions
  • ResponseBuilder → Formats output JSON

• Data Layer

  • Planned: Store historical trips for ML training (SQLite/PostgreSQL)

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📌 Sequence Flow

User → Frontend Form → APICaller → FastAPI Endpoint (/optimize)
→ OptimizationEngine → MLModelHandler (future)
→ ResponseBuilder → JSON Response → ChartManager → UI

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📌 Class-Level LLD (Python Backend)

class TripInput(BaseModel):
    distance_km: float
    battery_percent: float
    outside_temp: float
    battery_temp: float
    traffic: str
    driving: str
    mode: str
    payload: float

class OptimizationEngine:
    def __init__(self, data: TripInput):
        self.data = data

    def compute_settings(self) -> dict:
        # Logic for speed, AC %, regen braking
        ...

    def predict_range(self) -> float:
        # Formula/ML prediction
        ...

    def estimate_battery_left(self) -> float:
        ...

class ResponseBuilder:
    def __init__(self, settings, range_val, battery_left):
        self.settings = settings
        self.range_val = range_val
        self.battery_left = battery_left

    def to_dict(self):
        return {
            "recommended_settings": self.settings,
            "predicted_range": self.range_val,
            "battery_left": self.battery_left
        }

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📊 Example Output

{
  "recommended_settings": {
    "speed": 55,
    "ac": 25,
    "regen": "Medium",
    "acceleration_limit": "Low"
  },
  "predicted_range": 178.5,
  "battery_left": 45.0
}

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🚀 Setup Instructions

2️⃣ Setup Backend (FastAPI)

cd backend
pip install fastapi uvicorn pydantic
uvicorn main:app --reload --port 8000

Backend runs at 👉 http://127.0.0.1:8000

3️⃣ Run Frontend

Open frontend/index.html in your browser. It connects to the backend API automatically.

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🔮 Future Enhancements

• ✅ ML model integration for range prediction & optimal strategy
• ✅ Trip history database for personalized recommendations
• ✅ Google Maps API integration for real routes
• ✅ Real-time driving telemetry simulation
• ✅ Mobile app (React Native / Flutter)

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