Skip to main content
Log in

Real-time immune-inspired optimum state-of-charge trajectory estimation using upcoming route information preview and neural networks for plug-in hybrid electric vehicles fuel economy

  • Research Article
  • Published:
Frontiers of Mechanical Engineering Aims and scope Submit manuscript

    We’re sorry, something doesn't seem to be working properly.

    Please try refreshing the page. If that doesn't work, please contact support so we can address the problem.

Abstract

The main proposition of the current investigation is to develop a computational intelligence-based framework which can be used for the real-time estimation of optimum battery state-of-charge (SOC) trajectory in plug-in hybrid electric vehicles (PHEVs). The estimated SOC trajectory can be then employed for an intelligent power management to significantly improve the fuel economy of the vehicle. The devised intelligent SOC trajectory builder takes advantage of the upcoming route information preview to achieve the lowest possible total cost of electricity and fossil fuel. To reduce the complexity of real-time optimization, the authors propose an immune system-based clustering approach which allows categorizing the route information into a predefined number of segments. The intelligent real-time optimizer is also inspired on the basis of interactions in biological immune systems, and is called artificial immune algorithm (AIA). The objective function of the optimizer is derived from a computationally efficient artificial neural network (ANN) which is trained by a database obtained from a high-fidelity model of the vehicle built in the Autonomie software. The simulation results demonstrate that the integration of immune inspired clustering tool, AIA and ANN, will result in a powerful framework which can generate a near global optimum SOC trajectory for the baseline vehicle, that is, the Toyota Prius PHEV. The outcomes of the current investigation prove that by taking advantage of intelligent approaches, it is possible to design a computationally efficient and powerful SOC trajectory builder for the intelligent power management of PHEVs.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Manzie C, Watson H, Halgamuge S. Fuel economy improvements for urban driving: Hybrid vs. intelligent vehicles. Transportation Research Part C, Emerging Technologies, 2007, 15(1): 1–16

    Article  Google Scholar 

  2. Kohut N, Borrelli F, Hedrick J K, et al. Utilization of intelligent transport systems information to increase fuel economy through engine control. In: 15th World Congress on Intelligent Transport Systems and ITS America’s 2008 Annual Meeting. New York, 2008

    Google Scholar 

  3. Hellström E, Ivarsson M, Åslund J, et al. Look ahead control for heavy trucks to minimize trip time and fuel consumption. Control Engineering Practice, 2009, 17(2): 245–254

    Google Scholar 

  4. van Keulen T V, de Jager B D, Foster D, et al. Velocity trajectory optimization in hybrid electric trucks. In: American Control Conference. Baltimore: IEEE, 2010, 5074–5079

    Google Scholar 

  5. van Keulen T V, de Jager B, Serrarens A, et al. Optimal energy management in hybrid electric trucks using route information. Oil & Gas Science and Technology, 2010, 65(1): 103–113

    Google Scholar 

  6. Dib W, Serrao L, Sciarretta A. Optimal control to minimize trip time and energy consumption in electric vehicles. In: IEEE Vehicle Power and Propulsion Conference. Chicago: IEEE, 2011, 1–8

    Google Scholar 

  7. Vajedi M, Taghavipour A, Azad N L. Traction-motor power ratio and speed trajectory optimization for power-split PHEVs using route information. In: ASME International Mechanical Engineering Congress & Exposition. Houston, 2012, 301–308

    Google Scholar 

  8. Mensing F, Trigui R, Bideaux E. Vehicle trajectory optimization for hybrid vehicles taking into account battery state-of-charge. In: IEEE Vehicle Power and Propulsion Conference. Seoul: IEEE, 2012, 950–955

    Google Scholar 

  9. Prokhorov D. Computational Intelligence in Automotive Applications. Berlin: Springer, 2008

    Book  Google Scholar 

  10. Jia L, Yang L, Kong Q, et al. Study of artificial immune clustering algorithm and its applications to urban traffic control. International Journal of Information Technology, 2006, 12(3): 1–9

    Google Scholar 

  11. Huang G, Zhu Q, Siew C K. Extreme learning machine: Theory and applications. Neurocomputing, 2006, 70(1–3): 489–501

    Article  Google Scholar 

  12. Mozaffari A, Vajedi M, Chehresaz M, et al. Multi-objective component sizing of a power-split plug-in hybrid electric vehicle powertrain using Pareto-based natural optimization machines. Engineering Optimization, 2015, 1–19 (in press)

    Google Scholar 

  13. Mozaffari A, Azad N L. Optimally pruned extreme learning machine with ensemble of regularization techniques and negative correlation penalty applied to automotive engine coldstart hydrocarbon emission identification. Neurocomputing, 2014, 131: 143–156

    Article  Google Scholar 

  14. Liang N, Huang G, Saratchandran P, et al. A fast and accurate online sequential learning algorithm for feedforward networks. IEEE Transactions on Neural Networks, 2006, 17(6): 1411–1423

    Article  Google Scholar 

  15. Kuo R J, Chiang N L, Chen A Y. Integration of artificial immune system and K-means algorithm for customer clustering. Applied Artificial Intelligence, 2014, 28(6): 577–596

    Google Scholar 

  16. Gong M, Jiao L, Du H, et al. Multiobjective immune algorithm with non-dominated neighbor-based selection. Evolutionary Computation, 2008, 16(2): 225–255

    Article  Google Scholar 

  17. Mozaffari A, Emami M, Azad N L, et al. On the efficacy of chaosenhanced heuristic walks with nature-based controllers for robust and accurate intelligent search, Part A: An experimental analysis. Journal of Experimental & Theoretical Artificial Intelligence, 2014: 1–34

    Google Scholar 

  18. Emami M, Mozaffari A, Azad N L, et al. An empirical investigation into the effects of chaos on different types of evolutionary crossover operators for efficient global search in complicated landscapes. International Journal of Computer Mathematics, 2014, 1–24 (in press)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ahmad Mozaffari.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mozaffari, A., Vajedi, M. & Azad, N.L. Real-time immune-inspired optimum state-of-charge trajectory estimation using upcoming route information preview and neural networks for plug-in hybrid electric vehicles fuel economy. Front. Mech. Eng. 10, 154–167 (2015). https://doi.org/10.1007/s11465-015-0336-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11465-015-0336-z

Keywords

Navigation