EV Range Prediction Model 🚗🔋

A machine learning project that predicts the driving range of electric vehicles (EVs) based on various input features such as battery capacity, motor power, vehicle weight, and efficiency parameters.

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Table of Contents

• Project Overview
• Dataset
• Features
• Model
• Usage
• Evaluation Metrics
• Future Work
• License

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Project Overview

This project aims to provide a predictive model for estimating the range of electric vehicles.
It can help users, manufacturers, and enthusiasts estimate the driving distance of an EV under different conditions.

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Dataset

• Title: Electric Vehicle Specs Dataset (2025)
• Author: Urvish Ahir
• Link: https://www.kaggle.com/datasets/urvishahir/electric-vehicle-specifications-dataset-2025
• Number of entries: 1000
• Columns include [Battery Capacity, Motor Power, Drivetrain, Efficiency, etc.]

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Features

The model uses the following features to predict EV range:

• Top Speed (Kmh)
• Battery Capacity (kWh)
• Number of cells
• Torque (nM)
• Acceleration (0-100, s)
• Fast charging Power (kW/dc)
• Towing capacity (kg)
• Motor Power (kW)
• Cargo Volume (L)
• Drivetrain (FWD/RWD/AWD)
• Segment
• Body type
• Vehicle Weight (kg)
• Efficiency (Wh/km)

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Model

• Model type: Random Forest
• Programming language: Python
• Libraries: pandas, numpy, scikit-learn, [Optional: matplotlib, seaborn]

Usage

1. Clone the repository:

git clone https://github.com/yourusername/ev-range-predictor.git

2. Install required packages:

pip install -r requirements.txt

3. Run the prediction script:

python predict_range.py

4. Input vehicle parameters and get the predicted range.

Evaluation Metrics

The model is evaluated using standard regression metrics:

• Mean Absolute Error (MAE): 13.458
• Mean Squared Error (MSE): 362.729
• Root Mean Squared Error (RMSE): 19.045
• R² Score: 0.965

Example Code Snippet

from sklearn.ensemble import RandomForestRegressor

model = RandomForestRegressor(n_estimators=100, random_state=42)
model.fit(X_train, y_train)
y_pred = model.predict(X_test)

Future Work

• Include more features such as driving conditions, temperature, and load.
• Improve model accuracy using advanced algorithms or neural networks.
• Deploy as a web app or mobile app for interactive predictions.

License

This project is licensed under the GNU General Public v3.0 License.