Automotive Data Analysis with Python

Overview

This project analyzes simulated automotive sensor data using Python.

It reads vehicle data from a CSV file, validates the dataset, calculates engineering statistics, detects warning conditions, creates visualizations, and generates an automated technical report.

The project is relevant for automotive engineering, mechatronics, data analysis, testing, validation, and vehicle performance evaluation.

Main Features

• Reads automotive sensor data from a CSV file
• Validates required columns and numeric values
• Sorts the dataset by time
• Detects duplicate or incorrect time values
• Calculates acceleration from vehicle speed and time
• Analyzes vehicle speed, engine RPM, battery voltage, and motor temperature
• Detects high motor temperature warnings
• Detects low battery voltage warnings
• Detects high-speed events
• Detects hard acceleration and hard braking events
• Generates separate signal plots
• Generates a dashboard-style overview plot
• Creates an automated engineering report

Technologies Used

• Python
• pandas
• matplotlib
• CSV data analysis
• Automotive sensor data
• Data validation
• Warning detection
• Engineering visualization
• Automated reporting

Repository Structure

automotive-data-analysis-python/
│
├── automotive_analysis.py
├── vehicle_data.csv
├── README.md
├── requirements.txt
├── .gitignore
├── screenshots/
│   ├── automotive_dashboard.png
│   ├── speed_plot.png
│   ├── rpm_plot.png
│   ├── temperature_plot.png
│   ├── battery_plot.png
│   └── throttle_brake_plot.png
└── outputs/
    ├── automotive_report.txt
    ├── speed_plot.png
    ├── rpm_plot.png
    ├── temperature_plot.png
    ├── battery_plot.png
    ├── throttle_brake_plot.png
    └── automotive_dashboard.png

Input Data

The project uses a CSV file named:

vehicle_data.csv

The dataset contains simulated automotive sensor values.

Column	Description
time_s	Time in seconds
vehicle_speed_kmh	Vehicle speed in km/h
engine_rpm	Engine speed in revolutions per minute
throttle_percent	Throttle position in percent
brake_pressure_bar	Brake pressure in bar
battery_voltage_v	Battery voltage in volts
motor_temperature_c	Motor temperature in degrees Celsius

Analysis Logic

Data Validation

Before the analysis starts, the script checks that:

• the CSV file exists
• all required columns are available
• all required values are numeric
• time values are sorted correctly
• time values do not repeat or go backwards

This makes the project closer to a real engineering data workflow.

Acceleration Calculation

Acceleration is calculated from speed and time data.

The script converts speed from km/h to m/s and then calculates the acceleration between time steps.

Warning Detection

The script detects warning conditions using simple engineering limits.

Condition	Limit
High motor temperature	motor_temperature_c > 90
Low battery voltage	battery_voltage_v < 12.0
High speed event	vehicle_speed_kmh > 100
Hard acceleration	acceleration_mps2 > 2.5
Hard braking	acceleration_mps2 < -3.0

Generated Output

After running the script, the following files are generated in the outputs/ folder:

• outputs/automotive_report.txt
• outputs/speed_plot.png
• outputs/rpm_plot.png
• outputs/temperature_plot.png
• outputs/battery_plot.png
• outputs/throttle_brake_plot.png
• outputs/automotive_dashboard.png

Example Output

Automotive Dashboard

Vehicle Speed

Engine RPM

Motor Temperature

Battery Voltage

Throttle and Brake Signals

How to Run

Install the required libraries:

pip install -r requirements.txt

Run the analysis script:

python automotive_analysis.py

Expected terminal output:

Automotive analysis completed successfully.
Report saved as: outputs/automotive_report.txt
Plots saved in: outputs

Skills Demonstrated

• Python programming
• CSV data handling
• Data validation
• Automotive sensor data analysis
• Engineering calculations
• Acceleration calculation
• Warning detection
• Data visualization with matplotlib
• Automated technical report generation

What I Learned

• How to analyze automotive sensor data with Python
• How to work with CSV datasets using pandas
• How to validate input data before analysis
• How to calculate acceleration from speed and time data
• How to visualize vehicle signals using matplotlib
• How to detect warning conditions from sensor values
• How Python can support automotive and mechatronics workflows

Possible Applications

• Automotive data analysis
• Vehicle performance evaluation
• Sensor data monitoring
• Engineering test reports
• Mechatronics projects
• Embedded systems data analysis
• Internship portfolio project for automotive companies

Future Improvements

• Add real vehicle data
• Add CAN bus data analysis
• Add OBD-II data support
• Add battery performance analysis
• Add electric vehicle data analysis
• Add dashboard visualization with Streamlit
• Add PDF report generation
• Add comparison between different driving cycles
• Add machine learning for anomaly detection

Project Status

This project was created as a Python engineering portfolio project focused on automotive data analysis, warning detection, visualization, and automated reporting.