EV Range Estimator

A machine learning project that predicts the remaining range of an electric vehicle based on sensor inputs.

Features

• Regression model (Random Forest)
• Interactive Streamlit app with advanced visualizations
• Direct input and slider options for parameters
• Gauges and efficiency metrics
• 3D visualization of relationships between variables
• Input: Battery voltage, current, temperature, SOC, speed, load
• Output: Estimated range in km

Installation

1. Create and activate a virtual environment:

python -m venv venv
# On Windows:
venv\Scripts\activate
# On Unix/MacOS:
source venv/bin/activate

2. Install dependencies:

pip install -r requirements.txt

3. For enhanced visualizations, install Plotly:

pip install plotly

Usage

1. Generate sample data (if not already present):

python src/simulate_ev_range_data.py

2. Train the model:

python model/train_model.py

3. Run the interactive app:

streamlit run app/app.py

Interactive Features

• Choose between slider controls or direct numeric input
• Real-time prediction of EV range
• Visual battery and temperature gauges
• Parameter efficiency visualization
• Advanced 3D relationship plots and correlation heatmaps
• Reference values and optimal ranges

Screenshots

(Screenshots of the app would be displayed here)

python model/train_model.py