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Predicting Vehicle Fuel Consumption & Emissions

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PEMS in the United States
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Predicting Vehicle Fuel Consumption & Emissions

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Simple telematics devices known as OBD “dongles” are being used for a wide range of applications, including driver insurance programs, boundary and speed alerts for young drivers, and powertrain diagnostics. SGS has explored the potential for another application, using OBD dongle data to predict fuel consumption and tailpipe exhaust emissions. In this study, SGS accurately measured instantaneous fuel consumption and emissions in the laboratory and on the road using PEMS technology. We then employed an advanced analytical technique known as “machine learning” to discover the relationship between engine sensor data and exhaust emissions. The machine learning approach showed promise to predict fuel consumption and emissions more accurately, and could be used to augment government Remote OBD and emissions inventory modeling programs.

Simple telematics devices known as OBD “dongles” are being used for a wide range of applications, including driver insurance programs, boundary and speed alerts for young drivers, and powertrain diagnostics. SGS has explored the potential for another application, using OBD dongle data to predict fuel consumption and tailpipe exhaust emissions. In this study, SGS accurately measured instantaneous fuel consumption and emissions in the laboratory and on the road using PEMS technology. We then employed an advanced analytical technique known as “machine learning” to discover the relationship between engine sensor data and exhaust emissions. The machine learning approach showed promise to predict fuel consumption and emissions more accurately, and could be used to augment government Remote OBD and emissions inventory modeling programs.

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Predicting Vehicle Fuel Consumption & Emissions

  1. 1. 1 On the Efficacy of Predicting Light Duty Vehicle Fuel Consumption and Exhaust Emissions using SAE J1979 CAN Data I/M SOLUTIONS 2017 SGS Transportation Keith Vertin and Brent Schuchmann Email: keith.vertin@sgs.com May 23, 2017
  2. 2. 2 Light Duty Vehicle Fuel Consumption & Emissions Prediction  State I/M programs have adopted OBD inspections and have reduced tailpipe emissions testing  EPA recognizes Remote OBD as a continuous monitoring approach  Low cost dongles provide a viable means to transmit Diagnostic Trouble Codes and I/M readiness  Telematics/dongles can also log and transmit time-series vehicle data (SAE J1979 Mode 01 data)  Can fuel consumption and exhaust emissions be accurately estimated using Mode 01 data?
  3. 3. 3 Benefits for Predictive Fuel Consumption and Emissions  Traffic network impacts on energy and the environment  OBD provides granular data that enables seasonal and diurnal emissions trends analysis  Real-time feedback for Connected Vehicle driver assistance features  Individual vehicle data may have a role for future improvement of emissions modeling and validation activities – “Micro scale” data processed on big data platforms – Potential data source for MOVES, to support emissions inventories and State Implementation Plans
  4. 4. 4 Data Sources for Emissions Rate Models MOVES 2014a Data Sources for Emissions Rates (historical)  I/M Lanes  In Use Verification Program (IUVP)  Mobile Source Observation Database  Government sponsored studies Potential Future Data Sources for Activity and Emissions (real world micro scale)  Roadside Sensing  Remote OBD derived information  PEMS MOVES emissions rates grouped by Vehicle Specific Power operating modes
  5. 5. 5 Method Approach Issues Gasoline vehicles with MAF Fuel consumption only: Mass Air Flow / AFR * Equivalence Ratio MAF sensor not first principles measurement, transfer function is OEM specific Gasoline vehicles without MAF Fuel consumption only: Ideal Gas Law using stoichiometric combustion and volumetric efficiency Volumetric efficiency assumption Regression Multivariate linear regression Nonlinear: a * (Eng Speed * MAP)^b Form of equation may not fit data for diverse vehicle model possibilities - different forms for different vehicles Vehicle simulation models Powertain system models including major components and control features Component design and performance information must be known and specified Machine Learning Pattern recognition models using engine sensor data Larger computational requirements Micro Scale Estimation of Fuel Consumption and Emissions This study explores another possibility for modeling - *MAF = Mass Air Flow, AFR = Air Fuel Ratio, MAP = Manifold Absolute Pressure
  6. 6. 6 Previous Emissions Modeling Studies Using Time Series OBD Data  Several studies have been published, but there is sparse information about machine learning models using lab-grade PEMS data  Emissions prediction is challenging for low emission vehicles, as shown for this exponential equation solution = a * (Eng_Speed * MAP)b Source: “Comparison of Vehicle-Specific Fuel Use and Emissions Models Based on Externally and Internally Observable Activity Data”, Hu, Frey, Washburn, 2015. Note R-squared values are for model fits, and not for blind prediction using new data.
  7. 7. 7 Our Study: Vehicle Testing On-Dyno and On-Road  MY 2013 Jeep Wrangler, 3.6L V6, PFI, EPA Tier 2 Bin 4, no MAF  Chassis dyno emissions testing at SGS in Aurora, CO using standard emissions cycles (FTP, HWFET, US06, SRC) and one real-world cycle  AVL MOVES 483 Portable Emissions Measurement System (emissions certification grade PEMS)
  8. 8. 8 Time Series Data (1 Hz) Split into Micro Trips for Analysis On Dynamometer  122 micro trips  3.1 hours of operation On Road  93 micro trips  3.8 hours of operation
  9. 9. 9 Dynamometer and On-Road Vehicle Operation  On-road testing in Denver metro area included modes of operation similar to the dyno tests – Cold Start – City – Highway – Rapid accelerations  On-road testing also included mountain drives not simulated in the dyno lab  The on-road testing had greater variation in fuel consumption as expected
  10. 10. 10 Machine Learning Approach  [TRAIN] Train model using dyno laboratory time-series data only  [TEST] Predict on-road vehicle operation Make predictions using blinded OBD test data Independent OBD-II Parameters* Engine Speed Intake Manifold Absolute Pressure Ambient Air Temperature Intake Air Temperature Long Term Fuel Trim Equivalence Ratio Spark Timing Exhaust Gas Temp (Catalyst Inlet) Barometric Pressure Coolant Outlet Temperature *Correlated predictors such as load, torque and pedal position removed Dependent Parameters Fuel Consumption CO NOx THC Diagonal Line and R2 = 1.0 indicate perfect fit Fuel Consumption Model Fit to Training Data Time Series R2 = 0.931
  11. 11. 11 Fuel Consumption Predictions – For Each Time Series Data Point  Results shown are blind predictions (not the model fits to the training data)  On-Road fuel consumption could be predicted using dyno data alone  Predictions improved by including some randomly selected on-road microtrips Machine Learning with 30% On-Road Data Time Series R2 = 0.834 Machine Learning with Dyno Data Only Time Series R2 = 0.785 Under Predict Over Predict
  12. 12. 12 Fuel Consumption Predictions – Micro Trips Machine Learning with 30% On-Road Data Micro Trip R2 = 0.972 Time Series R2 = 0.834 Machine Learning with Dyno Data Only OBD Data Ideal Gas Law Micro Trip R2 = 0.945 Time Series R2 = 0.774 Micro Trip R2 = 0.962 Time Series R2 = 0.785  The estimated fuel consumption from the OBD dongle for this vehicle correlated well with measurements, but underestimated at mid to high loads
  13. 13. 13 Exhaust Emissions Prediction for Micro Trips Carbon Monoxide  Carbon monoxide had fewer non-detects compared to other species  Feature importance: equiv. ratio, long term fuel trim, MAP, engine speed  The predicted average CO emissions rate distribution by VSP mode had a similar trend compared to measured values Micro Trip R2 = 0.986 Time Series R2 = 0.841 Largest discrepancy at Mode 14 attributed to very little data at this highest power condition
  14. 14. 14 Exhaust Emissions Prediction for Micro Trips NOx  21% of the NOx emissions data collected for the micro trips were below the detection limit (treated as zero for training)  The model did not accurately predict NOx emissions, suggesting there was not sufficient explanatory data. Considerations: additional CAN parameters, physics, catalyst sensitivity to fuel sulfur, model training, algorithms. Micro Trip R2 = 0.201 Time Series R2 = 0.061 Model discrepancy at higher loads
  15. 15. 15 Exhaust Emissions Prediction for Micro Trips Total Hydrocarbons  53% of the hydrocarbon emissions data collected for the micro trips were below the detection limit (treated as zero for training)  The predicted average THC emissions rate distribution by VSP mode had a similar trend compared to measured values Micro Trip R2 = 0.941 Time Series R2 = 0.561
  16. 16. 16 Summary and Conclusions  Machine Learning (pattern recognition) was employed to predict fuel consumption and emissions using only OBD parameters  Based on testing one vehicle and approximately 7 hours of data: – On-road vehicle fuel consumption was predicted using only the dyno laboratory data source for model training – Good predictions of CO and THC emissions were possible but required the use of some on-road data for model training – NOx emissions were not accurately predicted, suggesting a lack of explanatory information – The predictions showed potential to faithfully represent the real-world emissions rate distribution by Vehicle Specific Power mode  More on-road training data would further improve prediction accuracy

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