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Engine maps of fuel use and emissions from transient driving cycles

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  • Bishop, Justin D.K.
  • Stettler, Marc E.J.
  • Molden, N.
  • Boies, Adam M.

Abstract

Air pollution problems persist in many cities throughout the world, despite drastic reductions in regulated emissions of criteria pollutants from vehicles when tested on standardised driving cycles. New vehicle emissions regulations in the European Union and United States require the use of OBD and portable emissions measurement systems (PEMS) to confirm vehicles meet specified limits during on-road operation. The resultant in-use testing will yield a large amount of OBD and PEMS data across a range of vehicles. If used properly, the availability of OBD and PEMS data could enable greater insight into the nature of real-world emissions and allow detailed modelling of vehicle energy use and emissions. This paper presents a methodology to use this data to create engine maps of fuel use and emissions of nitrous oxides (NOx), carbon dioxide (CO2) and carbon monoxide (CO). Effective gear ratios, gearbox shift envelopes, candidate engine maps and a set of vehicle configurations are simulated over driving cycles using the ADVISOR powertrain simulation tool. This method is demonstrated on three vehicles – one truck and two passenger cars – tested on a vehicle dynamometer and one driven with a PEMS. The optimum vehicle configuration and associated maps were able to reproduce the shape and magnitude of observed fuel use and emissions on a per second basis. In general, total simulated fuel use and emissions were within 5% of observed values across the three test cases. The fitness of this method for other purposes was demonstrated by creating cold start maps and isolating the performance of tailpipe emissions reduction technologies. The potential of this work extends beyond the creation of vehicle engine maps to allow investigations into: emissions hot spots; real-world emissions factors; and accurate air quality modelling using simulated per second emissions from vehicles operating in over any driving cycle.

Suggested Citation

  • Bishop, Justin D.K. & Stettler, Marc E.J. & Molden, N. & Boies, Adam M., 2016. "Engine maps of fuel use and emissions from transient driving cycles," Applied Energy, Elsevier, vol. 183(C), pages 202-217.
  • Handle: RePEc:eee:appene:v:183:y:2016:i:c:p:202-217
    DOI: 10.1016/j.apenergy.2016.08.175
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    References listed on IDEAS

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    1. Roy, Sumit & Banerjee, Rahul & Bose, Probir Kumar, 2014. "Performance and exhaust emissions prediction of a CRDI assisted single cylinder diesel engine coupled with EGR using artificial neural network," Applied Energy, Elsevier, vol. 119(C), pages 330-340.
    2. Çelik, Veli & Arcaklioglu, Erol, 2005. "Performance maps of a diesel engine," Applied Energy, Elsevier, vol. 81(3), pages 247-259, July.
    3. Mohamed Ismail, Harun & Ng, Hoon Kiat & Queck, Cheen Wei & Gan, Suyin, 2012. "Artificial neural networks modelling of engine-out responses for a light-duty diesel engine fuelled with biodiesel blends," Applied Energy, Elsevier, vol. 92(C), pages 769-777.
    4. Giakoumis, E.G. & Alafouzos, A.I., 2010. "Study of diesel engine performance and emissions during a Transient Cycle applying an engine mapping-based methodology," Applied Energy, Elsevier, vol. 87(4), pages 1358-1365, April.
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    6. Frondel, Manuel & Marggraf, Clemens & Sommer, Stephan & Vance, Colin, 2021. "Reducing vehicle cold start emissions through carbon pricing: Evidence from Germany," Ruhr Economic Papers 896, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
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    9. Evangelos G. Giakoumis & George Triantafillou, 2018. "Analysis of the Effect of Vehicle, Driving and Road Parameters on the Transient Performance and Emissions of a Turbocharged Truck," Energies, MDPI, vol. 11(2), pages 1-21, January.
    10. Miranda, Matheus H.R. & Silva, Fabrício L. & Lourenço, Maria A.M. & Eckert, Jony J. & Silva, Ludmila C.A., 2022. "Vehicle drivetrain and fuzzy controller optimization using a planar dynamics simulation based on a real-world driving cycle," Energy, Elsevier, vol. 257(C).
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    13. Zhang, Hao & Liu, Shang & Lei, Nuo & Fan, Qinhao & Wang, Zhi, 2022. "Leveraging the benefits of ethanol-fueled advanced combustion and supervisory control optimization in hybrid biofuel-electric vehicles," Applied Energy, Elsevier, vol. 326(C).
    14. Miretti, Federico & Misul, Daniela & Gennaro, Giulio & Ferrari, Antonio, 2022. "Hybridizing waterborne transport: Modeling and simulation of low-emissions hybrid waterbuses for the city of Venice," Energy, Elsevier, vol. 244(PB).
    15. Bishop, Justin D.K. & Molden, N. & Boies, Adam M, 2019. "Using portable emissions measurement systems (PEMS) to derive more accurate estimates of fuel use and nitrogen oxides emissions from modern Euro 6 passenger cars under real-world driving conditions," Applied Energy, Elsevier, vol. 242(C), pages 942-973.
    16. Hooftman, Nils & Messagie, Maarten & Van Mierlo, Joeri & Coosemans, Thierry, 2018. "A review of the European passenger car regulations – Real driving emissions vs local air quality," Renewable and Sustainable Energy Reviews, Elsevier, vol. 86(C), pages 1-21.
    17. Yun Chen & Chengwei Liang & Dengcheng Liu & Qingren Niu & Xinke Miao & Guangyu Dong & Liguang Li & Shanbin Liao & Xiaoci Ni & Xiaobo Huang, 2022. "Embedding-Graph-Neural-Network for Transient NOx Emissions Prediction," Energies, MDPI, vol. 16(1), pages 1-20, December.
    18. Mera, Zamir & Varella, Roberto & Baptista, Patrícia & Duarte, Gonçalo & Rosero, Fredy, 2022. "Including engine data for energy and pollutants assessment into the vehicle specific power methodology," Applied Energy, Elsevier, vol. 311(C).
    19. Rosero, Fredy & Fonseca, Natalia & López, José-María & Casanova, Jesús, 2020. "Real-world fuel efficiency and emissions from an urban diesel bus engine under transient operating conditions," Applied Energy, Elsevier, vol. 261(C).
    20. Haoming Gu & Shenghua Liu & Yanju Wei & Xibin Liu & Xiaodong Zhu & Zheyang Li, 2022. "Effects of Polyoxymethylene Dimethyl Ethers Addition in Diesel on Real Driving Emission and Fuel Consumption Characteristics of a CHINA VI Heavy-Duty Vehicle," Energies, MDPI, vol. 15(7), pages 1-20, March.
    21. Salvo, Orlando de & Vaz de Almeida, Flávio G., 2019. "Influence of technologies on energy efficiency results of official Brazilian tests of vehicle energy consumption," Applied Energy, Elsevier, vol. 241(C), pages 98-112.

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