On-Board-Diagnostics (OBD) Data Integration into Traffic Microsimulation for Vehicle-Specific Fuel Use and Emissions Modeling and In-Vehicle App Testing

UTC Project Information

In current work, our team is developing a new methodological approach for incorporating models of vehicle fuel use and emission rates based on Vehicle Specific Power (VSP) into CORSIM. VSP is a function of vehicle speed, acceleration, and grade, each of which can be observed from outside of the vehicle. However, fuel use and emissions depend on what is happening inside the engine. Models of fuel use and emission rates based on engine data, such as engine revolutions per minute (RPM) and manifold absolute pressure (MAP), are more predictive than those based only on VSP.

All 1996 and newer model-year vehicles have an On-Board Diagnostic (OBD) port that broadcasts real-time data for parameters including RPM, MAP, and many others. There is increasing interest in developing applications that use OBD data. The U.S. Department of Energy recently sponsored an “Apps for Vehicles” challenge to use OBD data for driver feedback on fuel economy. General Motors is allowing third party developers to develop “apps” that use OBD data that can run on in-dash information systems. NCSU is collaborating with an IT company in Portugal on “i2d” (intelligence to drive), a small device that connects to the OBD port in the vehicle and transmits data from the OBD and in-built sensors to a central server via cellular phone. Drivers who subscribe to this service obtain detailed evaluation of how their driving impacts fuel use, comfort, and safety.  In-vehicle data collection for proof-of-concept and feasibility assessment can be costly. There is growing demand for a simulation platform that enables developers to test and evaluate products, and for an improved method for quantifying fuel use and emissions to support transportation planning.

This research has three objectives: (1) develop and implement a method for predicting second-by-second (1 Hz) values of selected OBD parameters to simulate the real-time OBD data that can be obtained from an actual vehicle; (2) develop predictive models for vehicle energy use and emissions based on use of OBD parameters as the explanatory variables; and (3) implement the new predictive models for OBD Parameter IDs (PIDs), and the new OBD parameter-based fuel use and emission models, into CORSIM NG for the purpose of simulating OBD parameter values, fuel use, and emission rates for individual vehicles as they operate on the road network. The outcome of this work will be a new capability to simulate OBD data for use in developing new in-vehicle software applications and to improve the accuracy of fuel use and emissions estimates needed for transportation planning.

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