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An optimal energy management development for various configuration of plug-in and hybrid electric vehicle

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Abstract

Due to soaring fuel prices and environmental concerns, hybrid electric vehicle (HEV) technology attracts more attentions in last decade. Energy management system, configuration of HEV and traffic conditions are the main factors which affect HEV’s fuel consumption, emission and performance. Therefore, optimal management of the energy components is a key element for the success of a HEV. An optimal energy management system is developed for HEV based on genetic algorithm. Then, different powertrain system component combinations effects are investigated in various driving cycles. HEV simulation results are compared for default rule-based, fuzzy and GA-fuzzy controllers by using ADVISOR. The results indicate the effectiveness of proposed optimal controller over real world driving cycles. Also, an optimal powertrain configuration to improve fuel consumption and emission efficiency is proposed for each driving condition. Finally, the effects of batteries in initial state of charge and hybridization factor are investigated on HEV performance to evaluate fuel consumption and emissions. Fuel consumption average reduction of about 14% is obtained for optimal configuration data in contrast to default configuration. Also results indicate that proposed controller has reduced emission of about 10% in various traffic conditions.

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References

  1. CHAU K T, CHAN C C. Emerging energy-efficient technologies for hybrid electric vehicles [J]. Proceedings of the IEEE, 2007, 95(4): 709–726.

    Article  Google Scholar 

  2. WIRASINGHA S G, EMADI A. Classification and review of control strategies for plug-in hybrid electric vehicles [J]. IEEE Transactions on Vehicular Technology, 2011, 60(1): 111–122.

    Article  Google Scholar 

  3. BOUKEHILI A, ZHANG Y T, ZHAO Q, NI C Q, SU H F, HUANG G J. Hybrid vehicle power management modeling and refinement [J]. International Journal of Automotive Technology, 2012, 13(6): 987–998.

    Article  Google Scholar 

  4. TRIBIOLI L, BARBIERI M, CAPATA R, SCIUBBA E, JANNELLI E, BELLA G. A real time energy management strategy for plug-in hybrid electric vehicles based on optimal control theory [J]. Energy Procedia, 2014, 45: 949–958.

    Article  Google Scholar 

  5. KIM T S, MANZIE C, SHARMA R. Model predictive control of velocity and torque split in a parallel hybrid vehicle [C]// IEEE International Conference on Systems, Man and Cybernetics, SMC. San Antinio: IEEE, 2009.

    Google Scholar 

  6. TIE S F, TAN C H. A review of energy sources and energy management system in electric vehicles [J]. Renewable and Sustainable Energy Reviews, 2013, 20: 82–102.

    Article  Google Scholar 

  7. JALIL N, KHEIR N, SALMAN M. Fuzzy logic control for parallel hybrid vehicles [J]. IEEE Transactions on Vehicular Technology, 2002, 10(3): 460–468.

    Google Scholar 

  8. FANG L, QIN S, XU G, LIM T, ZHU K. Simultaneous optimization for hybrid electric vehicle parameters based on multi-objective genetic algorithms [J]. Energies, 2011, 4: 532–544.

    Article  Google Scholar 

  9. MARTÍNEZ J S, MULOTA J, HARELA F, HISSELA D, PÉRAA M C, JOHN R I, AMIET M. Experimental validation of a type-2 fuzzy logic controller for energy management in hybrid electrical vehicles [J]. Engineering Applications of Artificial Intelligence, 2013, 26(7): 1772–1779.

    Article  Google Scholar 

  10. HEMI H, GHOUILI J, CHERITI A. A real time fuzzy logic power management strategy for a fuel cell vehicle [J]. Energy Conversion and Management, 2014, 80: 63–70.

    Article  Google Scholar 

  11. WU J. Fuzzy energy management strategy for plug-in HEV based on driving cycle modeling [C]// Control Conference (CCC). Naning: IEEE, 2014: 4472–4476.

    Google Scholar 

  12. MARTÍNEZ J S, JOHN R I, HISSEL D, PÉRA M C. A survey-based type-2 fuzzy logic system for energy management in hybrid electrical vehicles [J]. Information Sciences, 2012, 190: 192–207.

    Article  Google Scholar 

  13. WU J, ZHANG C H, CUI N X. Fuzzy energy management strategy for a hybrid electric vehicle based on driving cycle recognition [J]. International Journal of Automotive Technology, 2012, 13(7): 1159–1167.

    Article  Google Scholar 

  14. NING Q, XUAN D, KIM Y. Modeling and control strategy development for fuel cell hybrid vehicles [J]. International Journal of Automotive Technology, 2010, 11(2): 229–238.

    Article  Google Scholar 

  15. XIONG W, ZHANG Y, YIN C. Optimal energy management for a series-Parallel hybrid electric bus [J]. Energy Conversion and Management, 2009, 50: 1730–1738.

    Article  Google Scholar 

  16. MONTAZERI M, POURSAMAD A, GHALICHI B. Application of genetic algorithm for optimization of control strategy in parallel hybrid electric vehicles [J]. Journal of the Franklin Institute, 2006, 343: 420–435.

    Article  MATH  Google Scholar 

  17. WU J, ZHANG C H, CUI N X. PSO algorithm based parameter optimization for HEV powertrain and its control strategy [J]. International Journal of Automotive Technology, 2008, 9(1): 53–69.

    Article  Google Scholar 

  18. CHEN Z, MIC C, XIONG R, XU J, YOU C. Energy management of a power-split plug-in hybrid electric vehicle based on genetic algorithm and quadratic programming [J]. Journal of Power Sources, 2014, 248: 416–426.

    Article  Google Scholar 

  19. DESAI C. Design and optimization of hybrid electric vehicle drive train and control strategy parameters using evolutionary algorithms [D]. Montréal, Québec, Canada: Department of Electrical and Computer Engineering, Concordia University, 2010.

    Google Scholar 

  20. DOSTHOSSEINI R, KOUZANI A Z, SHEIKHOLESLAM F. Direct method for optimal power management in hybrid electric vehicles [J]. International Journal of Automotive Technology, 2001, 12(6): 943–950.

    Article  Google Scholar 

  21. HUI S. Multi-objective optimization for hydraulic hybrid vehicle based on adaptive simulated annealing genetic algorithm [J]. Engineering Applications of Artificial Intelligence, 2010, 23: 27–33.

    Article  Google Scholar 

  22. WANG Hei-jun, E Jia-qiang, DENG Fei-qi. A novel adaptive mutative scale optimization algorithm based on chaos genetic method and its optimization efficiency evaluation [J]. Journal of Central South University, 2012, 19(9): 2554–2560.

    Article  Google Scholar 

  23. SOTO R M, CASTILLO O, AGUILAR L T, MELIN P. Fuzzy logic controllers optimization using genetic algorithms and particle swarm optimization [J]. Advances in Soft Computing Lecture Notes in Computer Science, 2010, 38(64): 475–486.

    Google Scholar 

  24. Enova systems, hybrid and electric commercial vehicles [EB/OL]. [2014-04-01]. http://www.slideshare.net/accessio/enova-systems-hybrid-and-electric-commercial-vehicles.

  25. KATRAŠNIK T. Hybridization of powertrain and downsizing of IC engine-A way to reduce fuel consumption and pollutant emissions [J]. Energy Convers Manage, 2007, 48(5): 1411–1423.

    Article  Google Scholar 

  26. BARLOW T J, LATHAM S, MCCRAE I S, BOULTER P G. A reference book of driving cycles for use in the measurement of road vehicle emissions [M]. Berks, UK: Department of Transport, Cleaner Fuels & Vehicles, 2009.

    Google Scholar 

  27. HUNG W T, TONG H Y, LEE C P. Development of a practical driving cycle construction methodology: A case study in Hong Kong [J]. Transportation Research, Part D: Transport & Environment, 2007, 12: 115–128.

    Article  Google Scholar 

  28. SHARMA R, MANZIE C, BESSEDE M, BREAR M J, CRAWFORD R H. Conventional, hybrid and electric vehicles for Australian driving conditions — Part 1: Technical and financial analysis [J]. Transportation Research Part C, 2012, 25: 238–249.

    Article  Google Scholar 

  29. MONTAZERI M, MAHMOODI M. Experimental driving data collection in Tehran city for simulation of plug-in hybrid vehicle [C]// The Bi-Annual International Conference on Experimental Solid Mechanics (X-Mech). Tehran, Iran: Iran University of Science and Technology, 2014.

    Google Scholar 

  30. MUDGAL A. Modeling driving behavior at traffic control devices [D]. Iowa State University, 2011.

    Google Scholar 

  31. KAMBLE S H, MATHEW T V, SHARMA G K. Development of real-world driving cycle: Case study of Pune, India [J]. Transportation Research, Part D, 2009, 14(2): 132–140.

    Article  Google Scholar 

  32. TZIRAKIS E, PITSAS K, ZANNIKOS F, STOURNAS S. Vehicle emissions and driving cycles: Comparison of the Athens driving cycle (ADC) with ECE-15 and European driving cycle (EDC) [J]. Global Nest Journal, 2006, 8(3): 282–290.

    Google Scholar 

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Authors and Affiliations

  1. Systems Simulation and Control Laboratory, School of Mechanical Engineering, Iran University of Science and Technology, Tehran, P.O. Box 16846-13114, Iran

    Morteza Montazeri-Gh & Mehdi Mahmoodi-K

Authors
  1. Morteza Montazeri-Gh
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  2. Mehdi Mahmoodi-K
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Correspondence to Mehdi Mahmoodi-K.

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Montazeri-Gh, M., Mahmoodi-K, M. An optimal energy management development for various configuration of plug-in and hybrid electric vehicle. J. Cent. South Univ. 22, 1737–1747 (2015). https://doi.org/10.1007/s11771-015-2692-6

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  • DOI: https://doi.org/10.1007/s11771-015-2692-6

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