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Tytuł artykułu

Conditions for optimizing powertrain performance in a vehicle with an internal combustion engine

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The paper presents optimization of the drive system in terms of adapting it to the characteristics of another engine. Powertrain parameters in a vehicle with an internal combustion engine were selected based on the following criteria: fuel consumption, engine dynamics, and emission standards for harmful substances. A light-duty passenger vehicle with gross vehicle weight rating (GVWR) of 3.5 tons was modified by replacing a spark-ignition engine with a diesel engine. The gear ratio in the powertrain had to be modified accordingly to optimize the engine’s performance, enhance engine dynamics, minimize fuel consumption and toxic emissions. The optimization of selected parameters of the vehicle driveline was performed based on the requirements of the standard NEDC and WLTC cycles.
Rocznik
Tom
Strony
291--307
Opis fizyczny
Bibliogr. 34 poz., rys., tab., wykr.
Twórcy
  • Katedra Budowy, Eksploatacji Pojazdów i Maszyn, Uniwersytet Warmińsko-Mazurski, ul. Michała Oczapowskiego 11, p. D2, 10-719 Olsztyn
Bibliografia
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  • Bertram C., Herzog H.-G. 2013. Optimization Method for Drive Train Topology Design and Control of Electric Vehicles. EVS27 International Battery, Hybrid and Fuel Cell Electric Vehicle Symposium, Barcelona.
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  • Dabadie J.C., Le Berr F., Salzgeber K., Prenninger P. 2011. Evaluation of TEG Potential in Hybrid Electric Vehicle by Simulation. Vehicle Thermal Management Systems, (10): 15-19.
  • Dabadie J.C., Sciarretta A., Font G., Le Berr F. 2017. Automatic Generation of Online Optimal Energy Management Strategies for Hybrid Powertrain Simulation. SAE Technical Paper 2017-24-0173.
  • Fraser N., Blaxill H., Lumsden G., Bassett M. 2009. Challenges for increased efficiency through gasoline engine downsizing. SAE Int. J. Engines, 2(1): 991-1008.
  • Fries M., Kruttschnitt M., Lienkamp M. 2018. Operational Strategy of Hybrid Heavy-Duty Trucks by Utilizing a Genetic Algorithm to Optimize the Fuel Economy Multiobjective Criteria. IEEE Transactions on Industry Applications, 54(4): 3668–3675.
  • Ghorbanian J., Ahmadi M., Soltani R. 2011. Design predictive tool and optimization of journal bearing using neural network model and multi-objective genetic algorithm. Scientia Iranica, Transactions B: Mechanical Engineering, 18: 1095–1105.
  • Giakoumis E.G., Zachiotis A.T. 2017. Investigation of a Diesel-Engine Vehicle Performance and Emissions during the WLTC Driving Cycle - Comparison with the NEDC. Energies 10: 240.
  • Gillespie T.D. 1992. Fundamentals of vehicle dynamics. Society of Automotive Engineers, Warrendale, PA.
  • Grytsyuk O., Vrublevskyi O. 2018. Investigations of diesel engine in the road test. Diagnostyka, 19(2): 89–94.
  • Heywood J.B. 1988. Internal Combustion Engine Fundamentals.
  • McGraw Hill, New York. Husain I., Islam M.S. 1999. Design, Modeling and Simulation of an Electric Vehicle System. SAE Paper 1999-01-1149.
  • Kropiwnicki J. 2011. Ocena efektywności energetycznej pojazdów samochodowych z silnikami spalinowymi. Monografie, 110.
  • Le Berr F., Abdelli A., Postariu D.-M., Benlamine R. 2012. Design and Optimization of Future Hybrid and Electric Propulsion Systems An Advanced Tool Integrated in a Complete Workflow to Study Electric Devices Oil & Gas Science and Technology. Rev. IFP Energies Nouvelles, 67(4): 547-562.
  • Li Y., Zhu B., Zhang N., Peng H., Chen Y. 2020. Parameters optimization of two-speed powertrain of electric vehicle based on genetic algorithm. Advances in Mechanical Engineering, 12(1).
  • Mirjalili S. 2019. Genetic algorithm. Evolutionary algorithms and neural networks. Springer, Cham.
  • Mitchke M. 1977. Dynamika samochodu. Wydawnictwa Komunikacji i Łączności, Warszawa.
  • Oglieve C., Mohammadpour M., Rahnejat H. 2017. Optimisation of the vehicle transmission and the gear-shifting strategy for the minimum fuel consumption and the minimum nitrogen oxide emissions. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, 231(7): 883-899.
  • Optimize system performance from early design stages. Simcenter Amesim. Siemens. https://www.plm.automation.siemens.com/global/en/products/simcenter/simcenter-amesim.html.
  • Orzełowski S. 1969. Budowa podwozi i nadwozi samochodowych. WSiP, Warszawa.
  • Peng M., Lin J., Liu X. 2018. Optimizing design of powertrain transmission ratio of heavy duty truck. IFAC-PapersOnLine, 51(31): 892-897.
  • Ross P.J. 1998. Taguchi Techniques for Quality Engineering. McGraw-Hill, New York.
  • Rubinstein R.Y., Kroese D.P. 2008. Simulation and the Monte Carlo Method. Second Edition, J. Wiley & Sons Inc., Hoboken.
  • Schittkowski K. 1986. NLPQL: A Fortran subroutine for solving constrained nonlinear programming problems. Annals of Operations Research, 5(2): 485–500.
  • Schittkowski K. 2011. A robust implementation of a sequential quadratic programming algorithm with successive error restoration. Optimization Letters, 5(2): 283-296.
  • Sciaretta A., Dabadie J., Albrecht A. 2008. Control-Oriented Modeling of Power Split Devices in Combined Hybrid-Electric Vehicles. SAE Paper 2008-01-1313.
  • Shen W., Yu H., Hu Y., Xi J. 2016. Optimization of shift schedule for hybrid electric vehicle with automated manual transmission. Energies, 9(3): 220.
  • Skugor B., Deur J. 2014. Dynamic programming-based optimization of electric vehicle fleet charging. 2014 IEEE International Electric Vehicle Conference (IEVC), Florence.
  • Sobol I.M., Statnikov R.B. 2006. Choice of optimal parameters in a framework with many criteria. Drofa, Moskwa.
  • Urbina Coronado P.D., Orta Castañón P., Ahuett-Garza H. 2018. Optimization of gear ratio and power distribution for a multimotor powertrain of an electric vehicle. Engineering Optimization, 50(2): 293–309.
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Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-c421ed59-e2d8-4700-b5b1-a1e24e199231
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