Real drive cycles analysis by ordered power methodology applied to fuel consumption, CO₂, NO_x and PM emissions estimation

Highlights

• New method named CAbOP is presented based on ordering data according to power.

• Three emission models are used and their emission results compared.

• Emissions data are analyzed in real driving cycles under CAbOP criteria.

• Methodology to collect data and reconstruct lost data in real urban driving cycles.

Abstract

In this work three fuel consumption and exhaust emission models, ADVISOR, VT-MICRO and the European Environmental Agency Emission factors, have been used to obtain fuel consumption (FC) and exhaust emissions. These models have been used at micro-scale, using the two signal treatment methods presented. The manuscript presents: 1) a methodology to collect data in real urban driving cycles, 2) an estimation of FC and tailpipe emissions using some available models in literature, and 3) a novel analysis of the results based on delivered wheel power. The results include Fuel Consumption (FC), CO₂, NO_x and PM₁₀ emissions, which are derived from the three simulators. In the first part of the paper we present a new procedure for incomplete drive cycle data treatment, which is necessary for real drive cycle acquisition in high density cities. Then the models are used to obtain second by second FC and exhaust emissions. Finally, a new methodology named Cycle Analysis by Ordered Power (CAbOP) is presented and used to compare the results. This method consists in the re-ordering of time dependant data, considering the wheel mechanical power domain instead of the standard time domain. This new strategy allows the 5 situations in drive cycles to be clearly visualized: hard breaking zone, slowdowns, idle or stop zone, sustained speed zone and acceleration zone. The complete methodology is applied in two real drive cycles surveyed in Barcelona (Spain) and the results are compared with a standardized WLTC urban cycle.

Graphical abstract

Keywords

Cycle Analysis by Ordered Power (CAbOP) / Micro and macro models / Real drive cycle / NO_x/PM₁₀/CO₂ emissions / Wheel mechanical power domain / Worldwide Harmonized Light-Duty Vehicles Test Cycle (WLTC)

References

[1] Ahn K, Rakha H, Trani A, Van Aerde M (2002). Estimating vehicle fuel consumption and emissions based on instantaneous speed and acceleration levels. Journal of Transportation Engineering, 128(2): 182–190

[2] Barth M, An F, Younglove T, Scora G, Levin C, Ross M, Wenzel T. (2000). The development of a comprehensive modal emissions model. NCHRP Web-Only Document, 122: 11–25

[3] Ben-Chaim M, Shmerling E, Kuperman A (2013). Analytic modeling of vehicle fuel consumption. Energies, 6(1): 117–127

[4] Bender W, Gruhl D, Morimoto N, Lu A (1996). Techniques for data hiding. IBM Systems Journal, 35(3–4): 313–336

[5] Bromba M U, Ziegler H (1981). Application hints for Savitzky-Golay digital smoothing filters. Analytical Chemistry, 53(11): 1583–1586

[6] California Air Resources Board (2017). EMFAC2017 User’s Guide V1.0.1. Sacramento: Mobile Source Analysis Branch. Air Quality Planning & Science Division

[7] Chen J, Jönsson P, Tamura M, Gu Z, Matsushita B, Eklundh L (2004). A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky–Golay filter. Remote Sensing of Environment, 91(3–4): 332–344

[8] Donateo T, Mattia G (2017). Building a cycle for real driving emissions. Energy Procedia, 126(9): 891–898

[9] Duarte G O, Gonçalo A G, Tiago L F A (2014). A Methodology to Estimate Real-world Vehicle Fuel Use and Emissions based on Certification Cycle Data. Procedia- Social and Behavioral Sciences (Elsevier), 111 (2): 702–710

[10] Feng A, Matthew B, Joseph N, Marc R (1997). Development of a comprehensive modal emissions model: Operating under hot-stabilized conditions. Journal of the Transportation Research Board, (1587): 52–62

[11] Hardin J W, Hilbe J M (2007). Generalized linear models and extensions. College Station: Stata Press

[12] Haugh L (1976). Checking the independence of two covariance-stationary time series: A univariate cross-correlation approach. Journal of the American Statistical Association, 71(354): 378–385

[13] Hentschel T, Fettweis G (2000). Sample rate conversion for software radio. IEEE Communications Magazine, 38(8): 142–150

[14] Horvatic D, Stanley H, Podobnik B (2011). Detrended cross-correlation analysis for non-stationary time series with periodic trends. EPL Europhysics Letters, 94(1): 18007

[15] Kim K H, Kabir E, Kabir S (2015). A review on the human health impact of airborne particulate matter. Environment International, 74: 136–143

[16] Luo J, Ying K, Bai J (2005). Savitzky-Golay smoothing and differentation filter for even number data. Signal Processing, 85(7): 1429–1434

[17] Malik A, Tauler R (2015). Exploring the interaction between O₃ and NO_x pollution patterns in the atmosphere of Barcelona, Spain using the MCR-ALS method. Science of the Total Environment, 517: 151–161

[18] Markel T, Brooker A, Hendricks T, Johnson V, Kelly K, Kramer B, O’Keefe M, Sprik S, Wipke K (2002). ADVISOR: a systems analysis tool for advanced vehicle modeling. Journal of Power Sources, 110(2): 255–266

[19] Ntziachistros L, Samaras Z (2017). EMEP/EEA emission inventory guidebook 2017: Exhaust emissions from road transport, passenger cars, light commercial trucks, heavy duty vehicles including buses and motor cycles. Copenhague. EMEP/European Environment Agency

[20] Ntziachristos L. (2009). COPERT: A European Road Transport Emission Inventory Model. Information Technologies in Environmental Engineering. Berlin Heidelberg: Springer, 491–504

[21] Perez L, Medina-Ramón M, Künzli N, Alastuey A, Pey J, Pérez N, Garcia R, Tobias A, Querol X, Sunyer J (2009). Size fractionate particulate matter, vehicle traffic, and case-specific daily mortality in Barcelona, Spain. Environmental Science & Technology, 43(13): 4707–4714

[22] Rakha H, Ahn K, Moran K, Saerens B, Van den Bulck E. (2011). Virginia Tech Comprehensive Power-Based Fuel Consumption Model: Model development and testing. Transportation Research Part D: Transport and Environment (Pergamon), 16 (7): 492–503

[23] Rakha H, Ahn K, Trani A (2003). Comparison of MOBILE5a, MOBILE6, VT-MICRO, and CMEM models for estimating hot-stabilized light-duty gasoline vehicle emissions. Canadian Journal of Civil Engineering, 30(6): 1010–1021

[24] Rakha H, Ahn K, Trani A (2004). Development of VT-Micro model for estimating hot stabilized light duty vehicle and truck emissions. Transportation Research Part D, Transport and Environment, 9(1): 49–74

[25] Savitzky A, Golay M (1964). Smoothing and differentiation of data by simplified least squares procedures. Analytical Chemistry, 36(8): 1627–1639

[26] Scora G, Barth M (2006). Comprehensive Modal Emissions Model (CMEM), version 3.01 User’s Guide. University of California. Riverside: Center for Environmental Research and Technology

[27] Sigmon K, Davis T (2004). MATLAB Primer. Taylor & Francis: Chapman and Hall/CRC

[28] Silva C M, Farias T L, Frey H C, Rouphail N M (2006). Evaluation of numerical models for simulation of real-world hot-stabilized fuel consumption and emissions of gasoline light-duty vehicles. Transportation Research Part D: Transport and Environment, 11(5): 377–385

[29] Tutuianu M, Bonnel P, Ciuffo B, Haniu T, Ichikawa N, Marotta A, Pavlovic J, Steven H (2015). Development of the World-wide harmonized Light duty Test Cycle (WLTC) and a possible pathway for its introduction in the European legislation. Transportation Research Part D, Transport and Environment, 40: 61–75

[30] United States Enviromental Protection Agency (2015). MOVES2014a User Guide. Website of nepis.epa.gov/Exe/ZyPDF.cgi?Dockey=P100NNCY.pdf (accessed September 3, 2018)

[31] United States Environmental Protection Agency (2002). User’s Guide to MOBILE6.1 and MOBILE6.2: Mobile source emission factor model. Website of nepis.epa.gov/Exe/ZyPDF.cgi/P1001DSD.PDF?Dockey=P1001DSD.pdf (accessed September 3, 2018)

[32] Wipke K B, Cuddy M R, Burch S D (1999). ADVISOR 2.1: A user-friendly advanced powertrain simulation using a combined backward/forward approach. IEEE Transactions on Vehicular Technology, 48(6): 1751–1761

Index

Terms ADvanced Vehicle SimulatOR (ADVISOR), Cycle Analysis by Ordered Power (CAbOP), Computer Program to calculate Emissions from Road Transport (COPERT), - CORe INventory AIR emissions (CORINAIR), European Environment Agency (EEA), European Monitoring and Evaluation Program (EMEP), Emission Factors (EFs), Fast Fourier Transform (FTT), Finite Impulse Response (FIR), Fuel consumption (FC), Geographic Information System (GIS), Global Positioning System (GPS), Internal Combustion Engine (ICE), National Renewable Energy Laboratory (NREL), Micro and macro models, Real drive cycle, NO_x/PM₁₀/CO₂ emissions, New European Drive Cycle (NEDC), Portable Emission Measurement System (PEMS), United States Environmental Protection Agency (US EPA), Wheel mechanical power domain, Worldwide Harmonized Light-Duty Vehicles Test Cycle (WLTC), Worldwide Harmonized Light-Duty Vehicles Test Procedure (WLTP).

Acknowledgements

The authors appreciate the support given by Juli Garcia Ramon and the contributions of Rubén Camúñez Llanos on creating the drive cycle tracking app.

RIGHTS & PERMISSIONS

2020 Higher Education Press