Reducing the Design Complexity of Automated Vehicle Electrical and Electronic Systems Using a Cyber-physical System Concept

  • Younghun Song
  • Jeehun Park
  • Kyung-Chang LeeEmail author
  • Suk Lee
Regular Papers Robot and Applications


Green transportation dictated by low carbon policies means that vehicle power sources are changing from fossil fuels to electricity. In electric vehicles, the numbers of electronic devices and the complexity of control software are high; design complexity has thus increased. Efforts to reduce the complexity of automated vehicle electrical and electric systems (E/E systems) at the design stage are actively underway. To reduce system design complexity, we introduce a design methodology employing cyber-physical systems (CPS). We designed an automated forklift system to explore the effectiveness of the proposed methodology. This paper shows that the CPS design methodology enables effective development of automated E/E control systems.


Automated vehicle cyber-physical systems electrical and electronic systems functional modularization network design system design methodology 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. [1]
    S. Han, H. Aki, S. Han, B. Kwon, and J. B. Park, “Optimal charging strategy for a residential PEV battery considering bidirectional trade and frequency regulation,” International Journal of Control, Automation, and Systems, vol. 14, no. 2, pp. 587–597, 2016.CrossRefGoogle Scholar
  2. [2]
    Q. Meng, Z. Y. Sun, and Y. Li, “Finite–time controller design for four–wheel–steering of electric vehicle driven by four in–wheel motors,” International Journal of Control, Automation, and Systems, vol. 16, no. 4, pp. 1814–1823, 2018.CrossRefGoogle Scholar
  3. [3]
    D. Reinhardt, D. Kaule, and M. Kucera, “Achieving a scalable E/E–architecture using AUTOSAR and virtualization,” SAE International Journal of Passenger Cars–Electronic and Electrical Systems, vol. 6, no. 2, pp. 489–497, 2013.CrossRefGoogle Scholar
  4. [4]
    J. Axelsson, “Cost models for electronic architecture trade studies,” Proc. of 6th IEEE International Conference on Engineering of Complex Computer Systems, pp. 229–239, 2000.Google Scholar
  5. [5]
    O. Givehchi, K. Landsdorf, P. Simoens, and A. W. Colombo, “Interoperability for industrial cyber–physical systems: an approach for legacy systems,” IEEE Transactions on Industrial Informatics, vol. 13, no. 6, pp. 3370–3378, 2017.CrossRefGoogle Scholar
  6. [6]
    Z. Song, Y. Q. Chen, C. R. Sastry, and N. C. Tas, Optimal Observation for Cyber–physical Systems: A Fisherinformation–matrix Based Approach, Springer, London, 2009.CrossRefzbMATHGoogle Scholar
  7. [7]
    L. Zhang, J. Yan, X. Yang, X. Luo, F. Tan, and X. Li, “Consensus tracking for teleoperating cyber–physical system,” International Journal of Control, Automation, and Systems, vol. 16, no. 3, pp. 1303–1311, 2018.CrossRefGoogle Scholar
  8. [8]
    Y. Liu, Y. Peng, B. Wang, S. Yao, and Z. Liu, “Review on cyber–physical systems,” IEEE/CAA Journal of Automatica Sinica, vol. 4, no. 1, pp. 27–40, 2017.CrossRefGoogle Scholar
  9. [9]
    Y. Zhang, F. Xie, Y. Dong, G. Yang, and X. Zhou, “High fidelity virtualization of cyber–physical systems,” International Journal of Modeling, Simulation and Scientific Computing, vol. 4. no. 2, 2013.Google Scholar
  10. [10]
    A. Wolfram, M. Makarov, T. Kramer, W. Tamisch, and R. M?nzenberger, “Design of robust system architectures for automotive ECUs,” Proc. of 12th International Conference on Quality Engineering in Software Technology, pp. 1–14, 2009.Google Scholar
  11. [11]
    H. Kuder, HIS Source Code Metrics Ver. 1.3.1., 2008.Google Scholar
  12. [12]
    Y. Tipsuwan and M. Y. Chow, “Control methodologies in networked control systems,” Control Engineering Practice, vol. 11, pp. 1099–1111, 2003.CrossRefGoogle Scholar
  13. [13]
    S. H. Hong, “Scheduling algorithm of data sampling times in the integrated communication and control systems,” IEEE Transactions on Control Systems Technology, vol. 3, no. 2, pp. 225–230, 1995.CrossRefGoogle Scholar
  14. [14]
    S. H. Hong and W. H. Kim, “Bandwidth allocation scheme in CAN protocol,” IEE Proceedings–Control Theory and Applications, vol. 147, no. 1, pp. 37–44, 2000.CrossRefGoogle Scholar
  15. [15]
    J. H. Park, S. Lee, and K. C. Lee, “Study on design of embedded control network system using cyber physical system concept,” IEMEK Journal of Embedded Systems and Applications, vol. 7, no. 5, pp. 227–239, 2012.CrossRefGoogle Scholar
  16. [16]
    CLARK Material Handling Asia, CRX 10–25 User Manual, 2006.Google Scholar
  17. [17]
    K. Tindell, H. Hansson, and A. Wellings, “Analyzing realtime communications: controller area network (CAN),” Proc. of IEEE Real–Time System Symposium, pp. 259–263, 1994.Google Scholar
  18. [18]
    M. Ellims, S. Parker, and J. Zurlo, “Design and analysis of a robust real–time engine control network,” IEEE Micro, vol. 22, no. 4, pp. 20–27, 2002.CrossRefGoogle Scholar
  19. [19]
    S. Yu and S. Zhou, “A Survey on metric of software complexity,” Proc. of 2nd IEEE International Conference on Information Management and Engineering, pp. 352–356, 2010.Google Scholar
  20. [20]
    T. A. Tamba, B. Hond, and K. S. Hong, “A path following control of an unmanned autonomous forklift,” International Journal of Control, Automation, and Systems, vol. 7, no. 1, pp. 113–122, 2009.CrossRefGoogle Scholar

Copyright information

© Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Younghun Song
    • 1
  • Jeehun Park
    • 2
  • Kyung-Chang Lee
    • 3
    Email author
  • Suk Lee
    • 1
  1. 1.School of Mechanical EngineeringPusan National UniversityBusanKorea
  2. 2.Smart Car Technology R&D DivisionChungcheongnam-doKorea
  3. 3.Department of Control & Instrumentation EngineeringPukyong National UniversityBusanKorea

Personalised recommendations