Journal of Mechanical Science and Technology

, Volume 33, Issue 4, pp 1523–1533 | Cite as

A method for solving causality conflicts in vehicle powertrain modeling

  • Namwook KimEmail author
  • Woong Lee
  • Haeseong Jeoung
  • Do Hyun Park
  • Deokjin Kim


Model-based systems engineering is becoming important and is being more widely used as the computing power is improved, because it can be extensively utilized for evaluating performances of vehicles with less effort and time than real-world tests. In this study, a port-based model technique is reviewed, especially for investigating the model connection between the transmission output and the final-drive input in vehicle powertrain models. Commercialized tools that model vehicle powertrains provide well-organized processes for users to connect conventional component models, but a confliction problem of connecting ports frequently arise when users develop their own models and integrate them into a vehicle system. In this study, several ideas to address the conflict issue have been reviewed, and an idea is implemented to solve the port conflict of the vehicle powertrain, and discussions about the method are provided. The idea is very useful especially when the powertrain model uses effort and flow signals to connect sub-component models. Simulation results show that the method produces accurate results and is fast enough, which requires additional calculation time less than 1 %, compared to an alternative method, and it is proved that it guarantees numerical stability in computations. The idea has been widely used in powertrain models, but the feasibility and the numerical stability have not been carefully investigated in the previous studies, so this study would be helpful for proving a basis to model users who develop new sub-component models.


Causality conflict Constrained dynamics MATLAB Simulink Model based analysis Powertrain modeling 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. [1]
    J. Schoeftner and W. Ebner, Simulation model of an electrohydraulic-actuated double-clutch transmission vehicle: Modelling and system design, Vehicle System Dynamics, 55 (12) (2017) 1865–1883.CrossRefGoogle Scholar
  2. [2]
    K. Oh, S. Yun, K. Ko, P. Kim, J. Seo and K. Yi, An investigation of energy efficiency of a wheel loader with automated manual transmission, Journal of Mechanical Science and Technology, 30 (7) (2016) 2933–2940.CrossRefGoogle Scholar
  3. [3]
    P. C. Breedveld, Port-based modeling of mechatronic systems, Mathematics and Computers in Simulation, 66 (2–3) (2004) 99–128.MathSciNetCrossRefzbMATHGoogle Scholar
  4. [4]
    J. J. Moskwa, S. A. Munns and Z. J. Rubin, The development of vehicular powertrain system modeling methodologies: Philosphy and implementation, SAE International (1997).Google Scholar
  5. [5]
    S. J. Kim, S. G. Kim, K. S. Oh and S. K. Lee, Excitation force analysis of a powertrain based on CAE technology, International Journal of Automotive Technology, 9 (6) (2008) 703–711.CrossRefGoogle Scholar
  6. [6]
    A. Rousseau, J. Kwon, P. Sharer, S. Pagerit and M. Duoba, Integrating data, performing quality assurance, and validating the vehicle model for the 2004 prius using PSAT, SAE International (2006).Google Scholar
  7. [7]
    F. Sangtarash, V. Esfahanian, H. Nehzati, S. Haddadi, M. A. Bavanpour and B. Haghpanah, Effect of different regenerative braking strategies on braking performance and fuel economy in a hybrid electric bus employing CRUISE vehicle simulation, SAE International Journal of Fuels and Lubricants, 1 (1) (2008) 828–837.CrossRefGoogle Scholar
  8. [8]
    N. Kim, H. Lohse-Busch and A. Rousseau, Development of a model of the dual clutch transmission in autonomie and validation with dynamometer test data, International Journal of Automotive Technology, 15 (2) (2014) 263–271.CrossRefGoogle Scholar
  9. [9]
    J. Fan, J. Zhang and T. Shen, Map-based power-split strategy design with predictive performance optimization for parallel hybrid electric vehicles, Energies, 8 (9) (2015) 9946–9968.CrossRefGoogle Scholar
  10. [10]
    O. Hayat, M. Lebrun and E. Domingues, Powertrain driveability evaluation: Analysis and simplification of dynamic models, SAE International (2003).Google Scholar
  11. [11]
    M. Datar, I. Stanciulescu and D. Negrut, A co-simulation framework for full vehicle analysis, SAE International (2011).Google Scholar
  12. [12]
    J. A. Cook, J. Sun, J. H. Buckland, I. V. Kolmanovsky, H. Peng and J. W. Grizzle, Automotive powertrain control — A survey, Asian Journal of Control, 8 (3) (2006) 237–260.MathSciNetCrossRefGoogle Scholar
  13. [13]
    H. Lee and H. Choi, Comparison of fuel efficiency and economical speed for internal combustion engine vehicle and battery electric vehicle using backward-looking simulation, Journal of Mechanical Science and Technology, 31 (9) (2017) 4499–4509.CrossRefGoogle Scholar
  14. [14]
    A. Cerofolini, Optimal supervisory control of hybrid vehicles, Alma Mater Studiorum — Università di Bolognaalma (2014).Google Scholar
  15. [15]
    T. Ersal, H. K. Fathy and J. L. Stein, Structural simplification of modular bond-graph models based on junction inactivity, Simulation Modelling Practice and Theory, 17 (1) (2009) 175–196.CrossRefGoogle Scholar
  16. [16]
    R. Ngwompo, R. Ngwompo, S. Scavarda and D. Thomasset, Physical model-based inversion in control systems design using bond graph representation Part 1: Theory, Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering, 215 (2) (2001) 95–104.zbMATHGoogle Scholar
  17. [17]
    J. Fan, J. Zhang and T. Shen, Map-based power-split strategy design with predictive performance optimization for parallel hybrid electric vehicles, Energies, 8 (9) (2015) 9946.CrossRefGoogle Scholar
  18. [18]
    K. Koprubasi, Modeling and Control of a Hybrid-electric Vehicle for Drivability and Fuel Economy Improvements, The Ohio State University (2008).Google Scholar
  19. [19]
    P. J. Gawthrop and G. P. Bevan, Bond-graph modeling, IEEE Control Systems, 27 (2) (2007) 24–45.MathSciNetCrossRefzbMATHGoogle Scholar
  20. [20]
    D. W. Gao, C. Mi and A. Emadi, Modeling and simulation of electric and hybrid vehicles, Proceedings of the IEEE, 95 (4) (2007) 729–745.CrossRefGoogle Scholar
  21. [21]
    B. Suh, Y. H. Chang, S. B. Han and Y. J. Chung, Simulation of a powertrain system for the diesel hybrid electric bus, International Journal of Automotive Technology, 13 (5) (2012) 701–711.CrossRefGoogle Scholar
  22. [22]
    R. V. Gopal and A. P. Rousseau, System analysis using multiple expert tools, SAE International (2011).Google Scholar
  23. [23]
    N. Kim and A. Rousseau, Thermal impact on the control and the efficiency of the 2010 Toyota Prius hybrid electric vehicle, Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, 230 (1) (2016) 82–92.Google Scholar
  24. [24]
    J. Liu, H. Peng and Z. Filipi, Modeling and analysis of the Toyota hybrid system, Proceedings of the 2005 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (2005) 134–139.Google Scholar
  25. [25]
    A. A. Shabana and B. A. Hussein, A two-loop sparse matrix numerical integration procedure for the solution of differential/algebraic equations: Application to multibody systems, Journal of Sound and Vibration, 327 (3) (2009) 557–563.CrossRefGoogle Scholar

Copyright information

© KSME & Springer 2019

Authors and Affiliations

  • Namwook Kim
    • 1
    Email author
  • Woong Lee
    • 1
  • Haeseong Jeoung
    • 1
  • Do Hyun Park
    • 1
  • Deokjin Kim
    • 2
  1. 1.Department of Mechanical EngineeringHanyang UniversityAnsanKorea
  2. 2.Green Car Power System R&D DivisionKorea Automotive Technology InstituteCheonan-siKorea

Personalised recommendations