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 Volume 5, Issue 2 (February 2018), Pages: 37-43


 Original Research Paper

 Title: Fuel consumption evaluation of a hybrid electric car over aggressive cycles for thermal engine optimization

 Author(s): Zainab Asus 1, *, El-Hassane Aglzim 2, Daniela Chrenko 3, Zul Hilmi Che Daud 1, Luis Le-Moyne 2


 1Faculty of Mechanical Engineering, Universiti Teknologi Malaysia, Johor, Malaysia
 2Institut Supérieur de l’Automobile et des Transports, id-motion DRIVE Laboratory, University of Burgundy, Nevers, France
 3FEMTO-ST, Université de Technologie de Belfort-Montbéliard, 90010 Belfort, France

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This paper investigates fuel consumption of a thermal engine in a hybrid electric vehicle system over aggressive cycles in order to optimize its energy consumption. The model of the system is first developed using data obtained from experiments over two aggressive driving cycles and then used to validate the model and further used as a platform to test other control strategies. The car’s actual control strategy operates on high torque region of the engine to sustain battery charge and caused high fuel consumption. Based on previous researches, there are two optimal control strategies that can be implemented for a series hybrid electric vehicle system; the dynamic programming control method and the optimal torque control method. Result analysis shows that the operations of the engine are different between the two control methods; it is concentrated on high speed region for the dynamic programming control method and at the middle of the engine map for the optimal torque control method. 

 © 2017 The Authors. Published by IASE.

 This is an open access article under the CC BY-NC-ND license (

 Keywords: Hybrid electric vehicle, Fuel consumption, Control strategy, Optimization, Thermal engine

 Article History: Received 17 July 2017, Received in revised form 6 December 2017, Accepted 9 December 2017

 Digital Object Identifier:


 Asus Z, Aglzim E, Chrenko D, Daud ZHC, and Le-Moyne L (2018). Fuel consumption evaluation of a hybrid electric car over aggressive cycles for thermal engine optimization. International Journal of Advanced and Applied Sciences, 5(2): 37-43

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 References (30)

  1. Bouscayrol A, Lhomme W, Delarue P, Lemaire-Semail B, and Aksas S (2006). Hardware-in-the-loop simulation of electric vehicle traction systems using energetic macroscopic representation. In the 32nd IEEE Annual Conference on Industrial Electronics (IECON '06), IEEE, Paris, France: 5319-5324. 
  2. Carbot-Rojas DA, Escobar-Jiménez RF, Gómez-Aguilar JF, and Téllez-Anguiano AC (2017). A survey on modeling, biofuels, control and supervision systems applied in internal combustion engines. Renewable and Sustainable Energy Reviews, 73: 1070–1085. 
  3. Chen K, Bouscayrol A, and Lhomme W (2008). Energetic macroscopic representation and inversion-based control: Application to an electric vehicle with an electrical differential. Journal of Asian Electric Vehicles, 6(1): 1097-1102. 
  4. Chen K, Bouscayrol A, Delarue P, and Berthon A (2009). Simulation of an unified control scheme for different Hybrid Electric Vehicles. In the 35th Annual Conference of IEEE Industrial Electronics, IEEE, Porto, Portugal: 3842-3847. 
  5. Cheng Y, Chen K, Chan CC, Bouscayrol A, and Cui S (2009). Global modeling and control strategy simulation. IEEE Vehicular Technology Magazine, 4(2): 73–79. 
  6. Dimitrova Z and Maréchal F (2015a). Energy integration on multi-periods and multi-usages for hybrid electric and thermal powertrains. Energy, 83: 539–550. 
  7. Dimitrova Z and Maréchal F (2015b). Techno-economic design of hybrid electric vehicles using multi objective optimization techniques. Energy, 91: 630–644. 
  8. Enang W and Bannister C (2017). Modelling and control of hybrid electric vehicles (A comprehensive review). Renewable and Sustainable Energy Reviews, 74: 1210–1239. 
  9. Gauchia L, Bouscayrol A, Sanz J, Trigui R, and Barrade P (2011). Fuel cell, battery and supercapacitor hybrid system for electric vehicle: Modeling and control via energetic macroscopic representation. In the Vehicle Power and Propulsion Conference, IEEE, Chicago, IL, USA: 1-6. 
  10. Gökce K and Ozdemir A (2014). An instantaneous optimization strategy based on efficiency maps for internal combustion engine/battery hybrid vehicles. Energy Conversion and Management, 81: 255–269. 
  11. Horrein L, Bouscayrol A, Cheng Y, and Fassi MEl (2015). Dynamical and quasi-static multi-physical models of a diesel internal combustion engine using Energetic Macroscopic Representation. Energy Conversion and Management, 91: 280–291. 
  12. Hou C, Ouyang M, Xu L, and Wang H (2014). Approximate Pontryagin's minimum principle applied to the energy management of plug-in hybrid electric vehicles. Applied Energy, 115: 174–189. 
  13. Hu X, Murgovski N, Johannesson L, and Egardt B (2013). Energy efficiency analysis of a series plug-in hybrid electric bus with different energy management strategies and battery sizes. Applied Energy, 111: 1001–1009. 
  14. Li L, Wang X, and Song J (2017). Fuel consumption optimization for smart hybrid electric vehicle during a car-following process. Mechanical Systems and Signal Processing, 87: 17–29. 
  15. Moura SJ, Fathy HK, Callaway DS, and Stein JL (2011). A stochastic optimal control approach for power management in plug-in hybrid electric vehicles. IEEE Transactions on Control Systems Technology, 19(3): 545–555. 
  16. Opila DF, Wang X, McGee R, Gillespie RB, Cook JA, and Grizzle JW (2012). An energy management controller to optimally trade off fuel economy and drivability for hybrid vehicles. IEEE Transactions on Control Systems Technology, 20(6): 1490-1505. 
  17. Saxena S, Phadke A, and Gopal A (2014). Understanding the fuel savings potential from deploying hybrid cars in China. Applied Energy, 113: 1127–1133. 
  18. Serge K, Hissel D, Sorrentino M, Chauvet F, and Pouget J (2016). Reverse engineering of a railcar prototype via energetic macroscopic representation approach. Energy Conversion and Management, 112: 61–80. 
  19. Serrao L, Onori S, and Rizzoni G (2011). A comparative analysis of energy management strategies for hybrid electric vehicles. Journal of Dynamic Systems, Measurement, and Control, 133(3): 031012. 
  20. Silva LI, Bouscayrol A, DeAngelo CH, and Lemaire-semail B (2014). Mechatronics coupling bond graph and energetic macroscopic representation for electric vehicle simulation. Mechatronics, 24(7): 906–913. 
  21. Sorrentino M, Rizzo G, and Arsie I (2011). Analysis of a rule-based control strategy for on-board energy management of series hybrid vehicles. Control Engineering Practice, 19(12): 1433–1441. 
  22. Stockar S, Marano V, Canova M, Rizzoni G, and Guzzella L (2011). Energy-optimal control of plug-in hybrid electric vehicles for real-world driving cycles. IEEE Transactions on Vehicular Technology, 60(7): 2949–2962. 
  23. Taymaz I and Benli M (2014). Emissions and fuel economy for a hybrid vehicle. Fuel, 115: 812–817. 
  24. Torres JL, Gonzalez R, Gimenez A, and Lopez J (2014). Energy management strategy for plug-in hybrid electric vehicles: A comparative study. Applied Energy, 113: 816–824. 
  25. Trovão JP and Antunes CH (2015). A comparative analysis of meta-heuristic methods for power management of a dual energy storage system for electric vehicles. Energy Conversion and Management, 95: 281–296. 
  26. Wang H, Huang Y, Khajepour A, and Song Q (2016). Model predictive control-based energy management strategy for a series hybrid electric tracked vehicle. Applied Energy, 182: 105–114. 
  27. Wirasingha SG and Emadi A (2011). Classification and review of control strategies for plug-in hybrid electric vehicles. IEEE Transactions on Vehicular Technology, 60(1): 111–122. 
  28. Wu J, Peng J, He H, and Luo J (2016). Comparative analysis on the rule-based control strategy of two typical hybrid electric vehicle powertrain. Energy Procedia, 104: 384–389. 
  29. Zhang S, Wu Y, Liu H, Huang R, Yang L, Li Z, Hao J (2014). Real-world fuel consumption and CO2 emissions of urban public buses in Beijing. Applied Energy, 113: 1645–1655. 
  30. Zhou F, Joshi SN, Rhote-vaney R, and Dede EM (2017). A review and future application of Rankine Cycle to passenger vehicles for waste heat recovery. Renewable and Sustainable Energy Reviews, 75: 1008–1021.