Neural Network Technique for Hybrid Electric Vehicle Optimization

  • Carla Majed
  • Sami H. Karaki
  • Rabih Jabr

Abstract

Large inaccuracies exist in the car speed forecast due to the driver actions, the vehicle, and r2016oad conditions, which are not known a priori. Hence, real time scheduling using optimization methods is not feasible in general.  So, an energy management system based on artificial neural network looking one step ahead is presented to minimize the cost of hydrogen and battery degradation.  The optimum results of dynamic programming are used to provide a data set used to train an artificial neural network offline which would help solve the problem of real-time implementation. The inputs of the artificial neural network are fuel cell power, the battery state of charge, and the demand forecast; whereas, the output is the fuel cell power. The results obtained by the artificial neural network are compared to those obtained by dynamic programming and found to be very close.  The artificial neural network is trained using the standard Urban Dynamometer Driving Schedule, and is able to provide charge sustaining and charge depletion operations. It is also tested on ten percent faster and ten percent slower variations of the same cycle, as well as on the Highway Fuel Economy Test Cycle and New European Driving cycle.  The tests show a very good generalization capability of the developed artificial neural network on the different drive cycles.

References

1. A. Boyali and L. Güvenç, "Real-time controller design for a parallel hybrid electric vehicle using neuro-dynamic programming method," in Systems Man and Cybernetics (SMC), IEEE International Conference, pp. 4318-4324, October 2010.
https://doi.org/10.1109/icsmc.2010.5641785
2. J. Bernard, S. Delprat, T.M. Guerra, and F.N. Büchi, "Fuel efficient power management strategy for fuel cell hybrid powertrains," Control Engineering Practice, 4th ed., vol. 18, pp. 408-417, 2010.
3. C.P. Quigley, R.J. Ball, A.M. Vinsome, and R.P. Jones, "Predicting journey parameters for the intelligent control of a hybrid electric vehicle," in Intelligent Control, Proceedings of the 1996 IEEE International Symposium, pp. 402-407, Sep. 1996.
https://doi.org/10.1109/ISIC.1996.556235
4. L. Feldkamp, M. Abou-Nasr, and I.V. Kolmanovsky, "Recurrent neural network training for energy management of a mild hybrid electric vehicle with an ultra-capacitor," in Computational Intelligence in Vehicles and Vehicular Systems, CIVVS'09, IEEE Workshop, pp. 29-36, March 2009.
https://doi.org/10.1109/civvs.2009.4938720
5. T. Yi, Z. Xin, Z. Liang, and Z. Xinn, "Intelligent energy management based on driving cycle identification using fuzzy neural network," Computational Intelligence and Design, ISCID'09, Second International Symposium, Vol. 2, pp. 501-504, Dec. 2009.
https://doi.org/10.1109/iscid.2009.271
6. Y. Gurkaynak, A. Khaligh, and A. Emadi, "Neural adaptive control strategy for hybrid electric vehicles with parallel powertrain," in Vehicle Power and Propulsion Conference (VPPC), IEEE, pp. 1-6, September 2010.
https://doi.org/10.1109/vppc.2010.5729084
7. J. Bernard, S. Delprat, F. Buechi, and T.M. Guerra, "Global Optimisation in the power management of a Fuel Cell Hybrid Vehicle (FCHV)," in Vehicle Power and Propulsion Conference, VPPC'06, IEEE , pp. 1-6, September 2006.
https://doi.org/10.1109/vppc.2006.364289
8. R. Dinnawi, D. Fares, R. Chedid, S. Karaki, and R. Jabr, "Optimized energy management system for fuel cell hybrid vehicles," in Mediterranean Electro technical Conference (MELECON), 17th IEEE , pp. 97-102, April 2014.
9. R. Wang, and S.M. Lukic, "Dynamic programming technique in hybrid electric vehicle optimization," in Electric Vehicle Conference (IEVC), IEEE International, pp. 1-8, March 2012.
https://doi.org/10.1109/ievc.2012.6183284
10. S.H. Karaki, R. Jabr, R. Chedid, and F. Panik, "Optimal energy management of hybrid fuel cell electric vehicles," SAE Technical Paper, No. 2015-01-1359, 2015.
11. L. Pérez-Lombard, J. Ortiz, and C. Pout, "A review on buildings energy consumption information. Energy and buildings," 3rd ed., vol 40, 394-398, 2008.
12. S.H. Karaki, R. Dinnawi, R. Jabr, R. Chedid, and F. Panik, "Fuel cell hybrid electric vehicle sizing using ordinal optimization," SAE International Journal of Passenger Cars-Electronic and Electrical Systems, 8 (2015-01-0155), pp 60-69, 2015.
13. C. Majed, S.H. Karaki, R. Jabr, F. Panik, "Near optimal control of hybrid fuel cell electric vehicles in real-time," submitted to SAE conference, USA, 2016, "unpublished".

Neural Network Technique for Hybrid Electric Vehicle Optimization
Published
2017-02-28
How to Cite
MAJED, Carla; KARAKI, Sami H.; JABR, Rabih. Neural Network Technique for Hybrid Electric Vehicle Optimization. Journal of Civil Engineering, [S.l.], v. 1, n. 1, p. 11-23, feb. 2017. Available at: <http://www.archyworld.com/journals/index.php/jce/article/view/35>. Date accessed: 21 aug. 2017. doi: https://doi.org/10.22496/jce2016082345.
Section
Articles