Numerical Assessment of Different Engine Model Levels in the View of Complex Hybrid Application

Authors

  • Giuseppe Di Luca Unina, Dipartimento Ingegneria Industriale via Claudio, 80125 Napoli, Italy
  • Massimiliano Muccillo Unina, Dipartimento Ingegneria Industriale via Claudio, 80125 Napoli, Italy
  • Giovanni Giardiello Unina, Dipartimento Ingegneria Industriale via Claudio, 80125 Napoli, Italy
  • Alfredo Gimelli Unina, Dipartimento Ingegneria Industriale via Claudio, 80125 Napoli, Italy
  • Gabriele Di Blasio Istituto Motori – CNR, via Marconi 4 80125 Napoli, Italy

DOI:

https://doi.org/10.12974/2311-8741.2020.08.6

Keywords:

Engine model accuracy, Heavy duty diesel, Engine numerical simulation, Real time factor.

Abstract

Despite the degree of railway electrification in many EU countries is higher than 50%, the diesel-driven railway vehicles continue to play an important role. As known, internal combustion engines, especially diesel engines, have also long been recognized as a significant source of pollutant emissions contributing to poor air quality, negative human health impacts and climate change. The future emissions regulatory control programs and the fuel-saving requirements for the new diesel engines for railways applications push worldwide OEMs, suppliers and scientific communities to investigate more advanced and alternative propulsion systems in which the diesel engines could still play an important role. Thus, the design of new power trains becomes more challenging considering the even more strict emission and efficiency targets. In this context, numerical simulation represents an essential tool in the entire development and optimization process of power trains. This study focuses on the numerical assessment of three different models of the same engine, characterized by different model accuracy, in order to evaluate the trade-off between model accuracy and computational time. The evaluation is carried out by performing the new emission standard Non-Road Transient Cycle (NRTC) applying the EU Non-Road Mobile Machinery (NRMM) directive to rail diesel vehicles. This work considers a 560 kW Heavy-Duty (HD) diesel engine. Regarding the models, the second and the third model are derived from the first one through an appropriate numerical procedure. The first, more accurate 1D model, is adequate when a deeper system analysis is required (i.e. wave dynamics, turbo-matching, etc.), while, for the evaluation of the global performance, the simplest model approach is more appropriate for complex systems, such as a hybrid powertrain. Indeed, the simplest model, despite its lower accuracy, shows good predictive results in terms of cumulative fuel consumption and cumulative NOx emissions over a transient homologation cycle. Moreover, for the lowest model accuracy, the real time factor is significantly lower compared to the more detailed one of about 250 times. 

References

A. p. Schiene, «Deutschland bei Bahn-Elektrifizierung nur Mittelmaß»

[Online]. Available: https://www.allianz-proschiene. de/presse/pressemitteilungen/2012-019- elektromobilitaet-deutschland-bei-bahn-elektrifizierungmittelmass/.

Report: "Energy savings with hybrid locomotives on TEN-T corridors", 2015.

N. Sonnichsen, «Statista»

[Online]. Available: https://www.statista.com/statistics/451522/share-of-the-railnetwork- which-is-electrified-in-europe/.

[Consultato il giorno 30 Luglio 2019].

European Commission, Mobility and Trasport: Electrified railway lines»

[Online]. Available: https://ec.europa.eu/transport/factsfundings/ scoreboard/compare/energy-unioninnovation/ share-electrified-railway_en.

[Consultato il giorno 30 Luglio 2019].

D. Holger, H. Dirk e U. Jörg, «Reducing DMU fuel consumption by means of hybrid energy storage» European Transport Research Review, 2011.

J. Norris, L. Ntziachristos, Z. Samaras e KH. Zieroch, «Air pollutant emission inventory guidebook: Railways» European Environment Agency (EEA), 2016.

T. Dalmann e A. Menon, «Technology Pathways for Diesel used in Non-Road Vehicles» Internation Council on Clean Transportation, 2016.

EU: Non Road Engines»

[Online]. Available: https://www.dieselnet.com/standards/eu/nonroad.php.

Z. Gao, JC. Conklin, CS. Daw e VK. Chakravarthy, «A proposed methodology for estimating transient engine - out temperature and emissions from steady - state maps» International Journal of Engine Research 2010; 11(2): 137- 151. https://doi.org/10.1243/14680874JER05609

RJ. Brooks e AM. Tobias, «Choosing the best model: Level of detail, complexity, and model perfomance,» Mathematical and Computer Modelling 1996; 24(4): 1-14. https://doi.org/10.1016/0895-7177(96)00103-3

F. Millo, L. Rolando e M. Andreata, «Numerical Simulation for Vehicle Powertrain Development,» Numerical Analysis - Theory and Application 2014; pp. 519-540.

D. Cieslar, A. Darlington, K. Glover e N. Collings, «Model based control for closed loop testing of 1-D engine simulation models» in Workshop on Engine and Powertrain Control, Simulation and Modeling, Rueil - Malmaison 2012. https://doi.org/10.3182/20121023-3-FR-4025.00005

A. Gimelli, M. Muccillo e O. Pennacchia, «Study of a new mechanical variable valve actuation system: Part IIEstimation of the actual fuel consumption improvement through one-dimensional fluid dynamic analysis and valve train friction estimation» International Journal of Engine Research 2015; 16(6): 762-772. https://doi.org/10.1177/1468087415604095

JC. Wurzenberger, R. Heinzle, MV. Deregnaucourt e T. Katrasnik, «A comprehensive study on different system level engine simulation models» 2013. https://doi.org/10.4271/2013-01-1116

AG. Konstandopoulos, M. Kostoglou, C. Beatrice, G. Di Blasio, A. Imren e I. Denbratt, «Impact of Combination of EGR, SCR, and DPF Technologies for the Low – Emission Rail Diesel Engines» Emission Control Science and Technology 2015; 1: 213-225. https://doi.org/10.1007/s40825-015-0020-0

Gamma Technologies, Vehicle Driveline and HEV Application Manual, 2016.

Gamma Technologies, Flow Theory Manual 2016.

H. Wu e MF. Li. A Hardware in-the-loop (HIL) Bench Test of a GT - Power Fast Running Model for Rapid Control Prototyping (RCP) Verification» SAE Technical Paper 2016; 2016-01-0549. https://doi.org/10.4271/2016-01-0549

Downloads

Published

2020-06-06

How to Cite

Luca, G. D., Muccillo, M., Giardiello, G., Gimelli, A., & Blasio, G. D. (2020). Numerical Assessment of Different Engine Model Levels in the View of Complex Hybrid Application. Journal of Environmental Science and Engineering Technology, 8, 52–62. https://doi.org/10.12974/2311-8741.2020.08.6

Issue

Section

Articles