Center for Urban Transportation Research | University of South Florida

Implementation of On-Board-Diagnostics (OBD) Data into a Custom Traffic Microsimulation Tool

Seckin Ozkul, Ph.D., E.I., Research Faculty, CUTR at University of South Florida

Scott Washburn, Ph.D., P.E.,Associate Professor, UFTI, University of Florida

March 27, 2015 l 2015 Southeastern Region UTC Conference

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Outline

• Background on OBD

• Project Overview and Objective

• Methodology and Data Collection

• Integration of OBD Data Estimation into Traffic Microsimulation

• Conclusions

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Background on OBD

• All 1996 and newer model year vehicles have an On-Board-Diagnostics-II (OBD-II) port that broadcasts real-time data for engine parameters including revolutions per minute (RPM), manifold absolute pressure (MAP), and many others.

Figure 1. Photo of an OBD-II cable attached to the OBD-II port of a vehicle – Photo by Seckin Ozkul

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Project Overview and Objective

• Fuel use and emissions depend on what is happening inside the engine, therefore models of fuel use and emission rates based on engine data, such as engine RPM and MAP, are more predictive than those based ONLY on Vehicle Specific Power (VSP), (Frey et al., 2010).

• The goal of this project was to develop and implement a method for predicting second-by-second (1 Hz) values of selected OBD parameters to simulate real-time OBD data that can be obtained from an actual vehicle.

• Therefore, the main objective is to increase the fidelity of microsimulation in modeling air quality impacts.

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Methodology and Data Collection

Numerous types of OBD data are available:

• vehicle’s fuel level• engine coolant temp.• short/long term

fuel trim• fuel pressure• manifold absolute pressure• engine RPM• vehicle speed• run time since engine start

• intake air temperature• engine air flow rate• throttle position• fuel rail pressure• barometric pressure• distance traveled• ambient air temperature• engine torque data• oxygen sensor info

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Methodology and Data Collection(cont’d)

• For the purposes of this study, not all available OBD data are required to be estimated/coded into the custom traffic microsimulation program.

• Calculate and output the following parameters, which factor into the emissions and fuel rate estimation models:

• engine speed (revs/min, also referred to as RPM)

• engine torque (lb-ft)• engine power (hp)• calculated engine load (%)

• vehicle speed (mi/h)• selected transmission gear• overall gear ratio• manifold absolute pressure

(kPa)

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Methodology and Data Collection(cont’d)

• Frey et al. (2010) reported that the vehicle emissions and the fuel rate estimation models that are a function of the product of RPM and MAP have an R2

value of 0.99 compared to the “older” binned VSP ONLY approach model R2 value of 0.87.

• Thus, an accurate OBD data (especially RPM and MAP) estimation capability in a traffic microsimulatorwould allow for future research to develop more accurate vehicle emissions and fuel rate estimations.

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Methodology and Data Collection(cont’d)

• OBD data collection was performed on a six-mile freeway corridor with a posted speed limit of 70 mi/h.

Figure 2. Aerial Photo of Freeway I-75 Segment in Gainesville, FL

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Methodology and Data Collection(cont’d)

• Another field data collection was also performed at an arterial corridor with a posted speed limit of 45 mi/h to observe data for lower speeds as well.

Figure 3. Aerial Photo of Arterial (Newberry Road) Segment in Gainesville, FL

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Methodology and Data Collection(cont’d)

• 3,355 seconds of valid OBD data was obtained.

• Three different types of driving behavior used:• Non-aggressive• Moderate• Aggressive

• Observe and account for these driver type differences that affect the internal engine variables of interest (MAP and RPM).

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Integration of OBD Data Estimation into Traffic Microsimulation

• Implementation of the OBD field data measurement vehicle into traffic simulation for field condition replication:

Figure 4. Torque/Power – Engine Speed Curves for 2003 Honda Civic LX (E-Trailer, 2014)

2003 Honda Civic LX

Vehicle Height (ft.) 4.59

Vehicle Width (ft.) 5.56

Vehicle Length (ft.) 14.56Vehicle Weight (lb.) 2,474

Maximum Torque (lb-ft.) 105

Maximum Power (hp) 115

Wheel Radius (ft.) 1.03

Differential Gear Ratio 4.07

Transmission Gear Ratios

Gear 1 2.722

Gear 2 1.516

Gear 3 0.975

Gear 4 0.674

Table 1. 2003 Honda Civic LX Vehicle Characteristics Data

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Integration of OBD Data Estimation into Traffic Microsimulation (cont’d)

• Using the OBD data collected, regression analysis was performed to estimate MAP, RPM and engine load and these formulas were coded into the custom simulation tool.

Figure 5. MAP*RPM versus Calculated Engine Load Field Data

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Integration of OBD Data Estimation into Traffic Microsimulation (cont’d)

• Fairly higher R2 values in the 0.70s were obtained for the RPM, MAP and engine load models.

• Once the models were obtained, they were integrated into the custom simulation tool and runs were performed to verify simulation tool calculations via comparison to manual calculations.

• At this point, further comparisons were performed for validation purposes.

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Integration of OBD Data Estimation into Traffic Microsimulation (cont’d)

Figure 7. Custom Simulation Tool Animation Window

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Integration of OBD Data Estimation into Traffic Microsimulation (cont’d)

Figure 7. Comparison of Field and SwashSim MAP*RPM versus Calculated

Engine Load Data

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Integration of OBD Data Estimation into Traffic Microsimulation (cont’d)

Figure 8. Comparison of Validation Data and SwashSim Engine Speed versus Vehicle Speed

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Integration of OBD Data Estimation into Traffic Microsimulation (cont’d)

Figure 9. Comparison of Validation Data and SwashSim Engine Speed, Selected Transmission Gear and Vehicle Speed

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Conclusions

• The results generated by the custom simulation tool highlycorrelate with the field data and thus can be used as a toolfor future research on OBD data based vehicle emissions andfuel consumption rate models.

• The results of this study provides the foundation, whichfuture research can build upon in developing more accuratevehicle emissions and fuel consumption rate models formicrosimulation.

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Seckin Ozkul, Ph.D., E.I.Research Faculty

ITS, Traffic Operations and Safetysozkul@cutr.usf.edu

Phone: 813-974-0445

Center for Urban Transportation Research (CUTR)University of South Florida

4202 E. Fowler Avenue, CUT 100Tampa, FL 33620