New Energy Vehicles Diagnosis

Dataset Overview

The New Energy Vehicle (NEV) Drivetrain Fault Diagnosis Dataset is a comprehensive resource for advancing fault detection systems in electric vehicles. This dataset focuses on identifying and classifying faults within the drivetrain system, a critical component of New Energy Vehicles. It provides a robust foundation for training and testing deep learning models designed to improve the safety, efficiency, and reliability of electric vehicles.

Key Features

• Diverse Sensor Data: Includes readings from multiple sensors such as voltage, current, motor speed, temperature, vibration, ambient temperature, and humidity.
• Balanced Dataset: Contains class labels for normal operation (label 0) and various fault types (labels 1-3), ensuring equitable distribution for model training.
• Wide Fault Coverage: Represents faults in key drivetrain components, including the motor, inverter, and battery.

Applications

This dataset is ideal for:

• Fault Detection Systems: Train AI models for real-time fault identification and classification in NEVs.
• Electric Vehicle Safety: Enhance safety measures by detecting potential drivetrain issues early.
• Predictive Maintenance: Support the development of advanced predictive maintenance solutions to reduce downtime and extend vehicle lifespan.