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PhD Dissertation Defense
- 1. Study of Transit Bus Duty Cycle and its Influence on Fuel Economy and Emissions of Diesel Electric-Hybrids Jairo A Sandoval León PhD Dissertation Defense Center for Alternative Fuels, Engines and Emissions Department of Mechanical and Aerospace Engineering West Virginia University Tuesday, March 29, 2011
- 2. Outline Motivation and Problem Statement Research Approach FE and Emissions Prediction Results Conclusions Demo of IBIS Tools 2
- 3. 1.1. Motivation Environmental and health effects of transportation Vehicle duty cycle Fuel economy & Emissions Hybrid buses will not perform the same on all service routes 3
- 4. 1.2. Problem Statement Integrated Bus Information System (IBIS) http://ibis.wvu.edu/ - U.S. DOT / FTA Transit Bus Emissions Database Transit Fleet Emissions Model Emissions / FE -> f (Cycle Metrics/Statistics) Which cycle metrics correspond to a particular type of operation? 4
- 5. 1.2. Problem Statement What set of metrics should be used as explanatory variables for emissions / FE prediction? What structure should be used for the predictive model? How to obtain an adequate set of data to train the predictive models? Which routes that take the most advantage of hybrid-electric buses? How much fuel / emissions can hybrid buses save under specific driving conditions? How to include uncertainty figures in the predictions? 5
- 6. 1.3. Contribution Analysis of in-use transit bus routes Database of bus Duty Cycles (~3000 miles) w/ grade A robust FE / Emissions prediction methodology Two Tools for IBIS: Fuel and CO2 savings of hybrid buses Post-processing of GPS route logs PSAT model for Series-Hybrid transit bus 6
- 7. 1.4. Previous Work 7 MOBILE 6 / MOVES Continuous Emissions / Fuel Consumption Data IBIS Linear Cycle Interpolation Cycle FE / Emissions Prediction Tools
- 8. 1.4. Previous Work 8 Vehicle Specific Power Continuous Emissions / Fuel Consumption Data On-Board Emissions Measurement Chassis-Dynamometer Testing Vehicle Simulation Environment Vehicle Operating Modes Cycle FE / Emissions Prediction Tool
- 9. 2. Research Approach 9 Extensive Duty Cycle Database The Ideal Scenario WVUTransLab (*) Continuous Emissions / Fuel Consumption Data Cycle FE / Emissions Prediction Tool (*) A.C. Nix, J.A. Sandoval, W.S. Wayne, N.N. Clark & D.L.McKain, Fuel economy and emissions analysis of conventional diesel, diesel-electric hybrid, biodiesel and natural gas powered transit buses, 3rd International Conference on Energy and Sustainability , Wessex Institute of Technology, Alicante, Spain, April 11-13, 2011
- 10. 2. Research Approach How can we predict FE / Emissions under a wide range or driving conditions? 10 Continuous Emissions / Fuel Consumption Data Develop Vehicle Dynamic Model (Simulation) Develop Extensive Duty Cycle Database Cycle FE / Emissions Prediction Tool
- 11. 2. Research Approach 2.1. Transit Bus Routes 2.2. Analysis of Cycle Metrics 2.3. Test Data for Vehicle Model 2.4. Engine Model 2.5. Vehicle Dynamic Model 11
- 12. 2.1. Transit Bus Routes WMATA, Spring 2009 2900 mi / 260 hr Ūmin = 6.7 mph – Inner City Ūmax = 25.9 mph - Commuter 12
- 14. 2.2. Analysis of Cycle Metrics 14 Correlations between pairs of metrics R2 Strong: >80% Mild : 50% - 80% Low : < 50% Explore predictions with Ū, Idle, and Acceleration
- 15. Routes by Service Type K-means Analysis 15
- 16. 2.3. Test Data (*) MY 2006 40’ Orion diesel-electric hybrid bus Series architecture powered with the BAE Systems HybriDrive® propulsion system 6 different drive cycles: Beeline, Manhattan, New York Bus, OCTA, UDDS, and WMATA 16 (*) Transit Resource Center and West Virginia University Center for Alternative Fuels Engines and Emissions. “Analysis of Tailpipe Emissions from Westchester County Transit Buses.” Submitted to Liberty Lines Transit and Westchester County Department of Transportation. May 31, 2007
- 17. 2.4. Engine Model Target: MY 2007-2009 Cummins ISB 260H (1.2-1.5 g/bhp∙hr NOx) Test data: MY 2006 ISB 260H (2.5 g/bhp∙hr NOx) Correction factor for NOx emissions: 0.85 (-15%) 2D Maps + ANNs 17
- 18. 18 and more Lug Curve Region Covered By Test Data NOx Emissions (MY 2006) Fuel Consumption
- 19. 2.4. Engine Model 19 ANN Architecture Speed and Torque: tt - 0.1 sec t - 1 sec t - 5 sec % Error for NOx
- 20. 2.5. Vehicle Dynamic Model 20 Vehicle Architecture in PSAT Char / Disch 160 / 230 hp 600V, NiMH Hybrid Controller 8 hp (6 kW) (*) (*) (*) 320 hp Peak (238 kW) AC Induction 2,700 ft-lb (3,660 Nm) 260 hp Cont (194 kW) Perm. Magn. 1:1 260 hp Peak (194 kW) 5.9 L Diesel 600 ft-lb Peak (815 Nm) 8 hp (6 kW) Ratio 4.9 80 ft2Af 0.79 Cd φ38.5” 8.6∙10-3 Cr (*) Bell, C. “An Investigation of Road Load Effects on Fuel Economy and NOx Emissions of Hybrid and Conventional Transit Buses.” Thesis (MS). West Virginia University, 2011
- 21. 2.5. Vehicle Dynamic Model Load-Following Control + Improved Motor / Generator / Battery: approximated based on BAE specs 21
- 22. Outline Motivation and Problem Statement Research Approach FE and Emissions Prediction Results Conclusions Demo of IBIS Tools 22
- 23. 3. FE and Emissions Prediction Driving + Idle Contributions: Idle FC/NOx rates: F.C.Driving = f ( Ūno idle, ãno grade) Fuel savings: gallons per hour 23
- 24. 24 Flow Chart
- 25. Outline Motivation and Problem Statement Research Approach FE and Emissions Prediction Results Conclusions Demo of IBIS Tools 25
- 26. 4. Results Simulation Vs. Test: 26 Fuel Economy NOx Emissions
- 27. 4. Results Regressions for Driving Contributions: 27 Fuel Consumption NOx Emissions
- 28. 28 Fuel Economy NOx Emissions CO2 Emissions
- 29. Hybrid Advantage and Fuel Savings 29 Hybrid Advantage Fuel Savings
- 30. Service Categories 30 Conventional vs. Hybrid Fuel Savings
- 31. Outline Motivation and Problem Statement Research Approach FE and Emissions Prediction Results Conclusions Demo of IBIS Tools 31
- 32. 5. Conclusions Predictor metrics: Average speed, Idle fraction, and Characteristic acceleration The highest fuel saving rates (gallons per hour) were observed at middle to high speed service The highest fuel savings correspond to routes with the highest values of characteristic acceleration Not all routes in a particular service category get the same benefit from hybridization The data did not reveal a benefit in NOx emissions from hybridization 32
- 33. Future Work Study the effect of road grade Built into IBIS road grade calculation Explore laboratory bias uncertainty and detection limits 33
- 34. 6. Demo of IBIS Tools GPS Data Cleaning Tool Hybrid Savings Calculation Tool Questions ? 34
- 36. Fleet Route Speed Distributions 36 WMATA DART ∴ Operation differs between transit agencies … Importance of present work
- 37. Vehicle Level Validation 37 Fuel Economy NOx Emissions
- 38. Limited User Inputs Use approximations for Idle and Acceleration: 38 Idle ãno grade
- 39. Conventional Vehicle 39 FE vs. Ū Predicted vs. Target
Editor's Notes
- My StoryIt was five years ago when my wife and I moved to West Virginia. Here we had the most wonderful welcome by Dr Wayne who guided us through all the process of adjusting to a new country and culture. For that and all of his support through my time at WVU I want to express my gratitude.I thank all the people who have given us a hand: Dr Clark, my professors, MAE and CAFEE staff, the list is endless. I am also thankful for all the friendships we’ve made and which we’ll never forget.Thank you for listening and now, let’s get to what we’re here for.
- PM: Respiratory, Haze, acid in water reservesNOx: Respiratory and heart, ozone and smog, acid rain, GHGHC: oxone, GHGCO2: GHG
- Operation: IBIS target users in the transit bus industry. Except for average route speed, They have limited information about the cycle metrics for their operation.
- Metrics: There was some preliminary work done by the IBIS development group. But I wanted to analyze this with the dataset I collected.Structure: Again, preliminary work by IBIS group. Make further developments, so to speak.Dataset: The original dataset was from standard transit bus cycles. As we wanted the model to predict over a wide range of operation, we needed to expand the cycle dataset.Routes: We were interested in allowing transit agencies to evaluate their routes in order to place their hybrids where they could be best exploited. We also were interested in finding if there was a specific driving condition which was best suited for hybrids: e.g. inner-city vs. urban.Fuel: Of course one of the criteria to decide which route may be best is fuel savings which translates to dollars.Uncertainty: Of course the predictions cannot be absolute, they have uncertainty. Previous models included only absolute values of their predictions. I wanted to include confidence intervals.
- MOBILE 6/MOVES: EPADid not do what I needed
- What about the implications of the hybrid system?Regenerative brakingBattery State of Charge
- Perform chassis dynamometer tests over an extensive Duty Cycle database ExpensiveExtensive Cycle database was not available
- This is how the project flowedDuty cyclesDynamic modelSimulationsPrediction Tool
- GPS Speed / Position + Internal AccelerometersECU: J1939 interfaceBarometric pressure ~ Elevation
- This procedure is standard practice, nothing fancy here.Correlated. The predictive power of the second parameter may not be significant.Uncorrelated. The predictive power of both parameters should be significant.Ū_no_idle is known from Ū and Idle
- Capacity: 39 seated + 20 standingPM, CO, and HC were very low due to the DOC/DPF aftertreatmentCurb / GVWR / Test weights: 33,440 lb / 42,540 lb / 38,540 lb 15,170 kg / 19,295 kg / 17,480 kgULSD#1 fuelDiesel oxidation catalyst (DOC) and particulate trap (DPF)
- % Torque was translated to Nm with the engine performance curve supplied by Cummins.The data were not corrected for dispersion / diffusion.Time alignment and CFR Part 86 Subpart N reductionECU: Speed, % Torque, and Fuel RateFuel consumption and NOx (CO2: carbon balance)
- Cell counts= 10 rpm x 5 Nm2D Map Grid: 50 rpm x 10 Nm for Fuel, R^2 = 0.98210 rpm x 5 Nm for NOx, R^2 = 0.902Max Efficiency: 43%
- rarbas = radial basistansig = hyperbolic tangent sigmoidThe figure at the bottom shows an example of the improvement with ANN
- Batteries in test vehicle were lead-acid. In the model: NiMH
- * SOC recharge if below threshold* Better monitoring of SOC* Limit performance if SOC is low* Mechanical accessory loads at braking/stops
- Plot
- the higher the ã, the more room there is for the hybrid system to recover regenerative braking energy and to balance engine operation.NOx: This fact can be explained by the lower loads experienced by the engine on the hybrid bus, as lower loads are associated with higher brake-specific NOx.
- Nothing below 5 mph or above 30 mph
- Overall, the model predictions have good agreement with test data.Results are in the ballpark