🔋 Smart EV Route Planner & Cost Optimizer

🚀 Try the Live Application Here

📌 Project Overview

The Smart EV Route Planner is an engineering-grade web application designed to eliminate Electric Vehicle (EV) range anxiety. Unlike standard navigation apps that only calculate distance, this simulator uses a deterministic physics engine to calculate actual battery consumption. It actively factors in 3D topography (hills and slopes), real-time weather (headwinds and rain friction), and vehicle-specific aerodynamics to predict exactly when and where an EV will need to recharge.

✨ Core Engineering Features

• Real-Time Physics Engine: Calculates traction power requirements by summing forces of gravity (mg sin θ), rolling resistance (mg Cr cos θ), and aerodynamic drag (0.5 ρ Cd A v²) across micro-segments of the route.
• Smart Charging Interceptor: Algorithmically predicts when the battery will breach safe thresholds and dynamically drops predictive fast-charging waypoints on the map.
• Economic Grid Optimization: Features a Time-of-Day (ToD) tariff matrix that calculates the exact monetary savings of shifting the journey to off-peak electrical grid hours.
• Curated EV Database: Pre-configured with the exact weight, battery capacity, and drag coefficients of 20 popular Indian market EVs (e.g., Tata Nexon EV, BYD Seal, MG ZS EV), plus a fully unlocked Custom EV Manual Override for future-proofing.
• Geospatial Visualization: Renders interactive maps with Folium and dynamic dual-axis Plotly telemetry charts tracking elevation vs. State of Charge (SoC).

🛠️ Technology Stack

• Frontend/Framework: Streamlit
• Data Processing & Math: Pandas, NumPy, Math
• Geospatial & Visualization: Folium, Streamlit-Folium, Plotly
• External APIs: * OpenRouteService API (Routing & 3D Elevation profiles)
  • OpenWeatherMap API (Live wind speed, temperature, and precipitation)

💻 Local Installation & Setup

If you wish to run this project locally on your machine, follow these steps:

1. Clone the repository:

   git clone [https://github.com/vishnu-g-dev/Smart-EV-Route-Planner.git](https://github.com/vishnu-g-dev/Smart-EV-Route-Planner.git)
   cd Smart-EV-Route-Planner

2. Install required dependencies:

   pip install -r requirements.txt

3. Set up API Keys: Create a .env file in the root directory and add your free API keys:

   ORS_API_KEY=your_openrouteservice_key_here
   OWM_API_KEY=your_openweathermap_key_here

4. Launch the application:

   streamlit run app.py

🏗️ System Architecture & Modular Design

To ensure clean code principles, testability, and separation of concerns, the application is structured into a modular architecture:

• app.py: The main execution script handling the Streamlit User Interface, session state memory, and frontend visualization.
• physics_engine.py: The isolated deterministic physics model. It calculates gravitational forces, rolling resistance, aerodynamic drag, and handles the safety-buffer logic for charging intercepts.
• api_client.py: The dedicated network layer managing external data streaming, request handling, and telemetry fetching from OpenRouteService and OpenWeatherMap.
• vehicle_db.py: A centralized data catalog storing the specific battery capacities, drag coefficients, and weights of 20 popular Indian market EVs.
• utils.py: A repository for standalone mathematical algorithms, such as the Haversine formula for geospatial distance calculation.

⚙️ How It Works

1. User Input: The user selects starting and destination cities, configures their specific EV model (or inputs custom aerodynamics/weight), and sets their current battery State of Charge (SoC).
2. API Telemetry Fetch: The application queries OpenRouteService for the 3D route geometry (longitude, latitude, and elevation points) and OpenWeatherMap for midpoint ambient weather conditions.
3. Physics Simulation: A deterministic loop calculates the specific energy required for every individual segment of the journey, dynamically factoring in incline (hill climbs), aerodynamic drag (wind speed), and rolling resistance (rain status).
4. Dynamic Output: The app renders an interactive eco-route map, drops smart charging waypoints when the battery dips below safety thresholds, and plots an interactive battery depletion chart alongside a Time-of-Day cost analysis.

🧠 Future Scope

• Integration of live traffic congestion data to factor in idle battery drain (HVAC usage).
• Dynamic fetching of live charging station availability near the predicted depletion coordinates.
• Enhanced regenerative braking algorithms based on specific deceleration profiles.

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Designed and Developed by Vishnu G | Electrical and Electronics Engineering, NSS College of Engineering