OBD-II Data Analysis with Python

Automotive data is available from OBD-II codes that are standard for engine monitoring of light duty vehicles (passenger cars) starting in year 1996. Newer vehicles may include additional sensors from other electronic systems such as the brake system, tire pressure, climate control, and transmission. Data is processed with on-board computers to monitor the health of the vehicle systems and provide warning diagnostics when there is an indication of malfunction.

Data is collected from a 2014 Ford Focus (manual transmission) and a 2021 Chrysler Pacifica minivan with city and freeway driving:
Automotive Warmup (30 min), 62 columns, Ford
Provo to St. George, Utah (~8 hrs), 40 columns, Ford
Provo to Salt Lake City, Utah (~2 hrs), 22 columns, Ford
Des Moines to Pella, Iowa (~1 hr), 46 columns, 2021 Chrysler

Data sets 1 and 2 are collected with the ELM327 module with raw sensor data and calculated values that are collected with Car Scanner (App) from the OBD-II codes through a Bluetooth connection. Data set 3 is collected with an OBDLink MX+ OBD-II module through a Bluetooth connection.

Objective: Investigate driver and automobile performance. Use an OBD-II reader and collect data from a vehicle. Analyze the following:
1. Determine Light-off Time of the Catalytic Converter
2. Visualize the Speed and Elevation on a Map
3. Find maximum fuel economy versus speed

Case Study 1: Catalyst Light-Off
The catalyst light-off temperature is a minimum temperature to initiate the catalytic reaction. Before light-off temperature is reached, the catalytic converter is less effective at reducing the pollutants. A typical light-off temperature is between 400 to 600 degrees F. The normal operating temperature is between 750 to 1,600 degrees F. The operating temperature increases with more pollutants in the exhaust. Calculate the time to catalyst light-off (500 degF) in seconds. Create an ARX model of the catalytic temperature and coolant temperature based on the vehicle speed and engine status.

Case Study 2: GIS Map Visualization
Geographic Information Systems (GIS) are used to create, manage, analyze, and map data. GPS is used to identify the location of the vehicle. Car data is merged with location data in a single data file. This exercise demonstrates mapping capabilities in Python with three options to create a map and display speed and elevation on the map.

Case Study 3: Fuel Efficiency Regression
Fuel efficiency is affected by many factors such as speed, elevation changes, and aggressive acceleration. Calculate the fuel efficiency in miles per gallon (MPG) or Liters per 100 km (L/100 km). Use regression to develop a model of how measured factors relate to the fuel efficiency.

Data and Python Code: https://apmonitor.com/pds/index.php/Main/AutomotiveMonitoring
Machine Learning for Engineers: https://apmonitor.com/pds

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