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Design and Optimization of Hybrid Electric Vehicle Drivetrain and Control Strategy Parameters Using Evolutionary Algorithms

Title:

Design and Optimization of Hybrid Electric Vehicle Drivetrain and Control Strategy Parameters Using Evolutionary Algorithms

Desai, Chirag (2010) Design and Optimization of Hybrid Electric Vehicle Drivetrain and Control Strategy Parameters Using Evolutionary Algorithms. Masters thesis, Concordia University.

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Abstract

Advanced propulsion technologies such as hybrid electric vehicles (HEVs) have demonstrated improved fuel economy with lower emissions compared to conventional vehicles. Superior HEV performance in terms of higher fuel economy and lower emissions, with satisfaction of driving performance, necessitates a careful balance of drivetrain component design as well as control strategy parameter monitoring and tuning. In this thesis, an evolutionary global optimization-based derivative-free, multi-objective genetic algorithm (MOGA) is proposed, to optimize the component sizing of a NOVA® parallel hybrid electric transit bus drivetrain. In addition, the proposed technique has been extended to the design of an optimal supervisory control strategy for effective on-board energy management. The proposed technique helps find practical trade off-solutions for the objectives. Simulation test results depict the tremendous potential of the proposed optimization technique in terms of improved fuel economy and lower emissions (nitrous-oxide, NOx, carbon monoxide, CO, and hydrocarbons, HC). The tests were conducted under varying drive cycles and control strategies

Divisions:Concordia University > Faculty of Engineering and Computer Science > Electrical and Computer Engineering
Item Type:Thesis (Masters)
Authors:Desai, Chirag
Institution:Concordia University
Degree Name:M.A. Sc.
Program:Electrical and Computer Engineering
Date:13 December 2010
Thesis Supervisor(s):Williamson, Sheldon
Keywords:Hybrid electric vehicle, control strategy, evolutionary algorithm, multi-objective optimization.
ID Code:7496
Deposited By:CHIRAG DESAI
Deposited On:08 Jun 2011 14:36
Last Modified:21 Nov 2012 15:27
References:
[1] Hybrid Transit Bus Certification Workgroup, “Engine certification recommendations report,” Northeast Advanced Vehicle Consortium (NAVC0599-AVP009903), Sept. 2000.
[2] C. C. Chan, “The state of the art of electric, hybrid, and fuel cell vehicles,” Proceedings of the IEEE, vol. 95, no. 4, pp. 704-718, April 2007.
[3] M. Ehsani, Y. Gao, S. Gay, and A. Emadi, Modern Electric, Hybrid Electric, and Fuel Cell Vehicles: Fundamentals, Theory and Design, FL: CRC Press, 2004.
[4] A. Emadi, K. Rajashekara, S. S. Williamson, and S. M. Lukic, “Topological overview of hybrid electric and fuel cell vehicular power system architectures and configurations,” IEEE Trans. on Vehicular Technology, vol. 54, no. 3, pp. 763-770, May 2005.
[5] G. Maggetto and J. Van Mierlo, “Electric and hybrid electric vehicle: a survey,” IEE Seminar on Electric, Hybrid Electric, and Fuel Cell Vehicles, Durham, UK, April 2005, pp. 1-11.
[6] Z. Rahman, K. L. Butler, and M. Ehsani, “A comparison study between two parallel hybrid control concepts,” in Proc. of SAE World Congress, Detroit, MI, March 2000, Paper No. 2000-01-0994.
[7] A. Kimura, T. Abe, and S. Sasaki, “Drive force control of a parallel-series hybrid system,” JSAE Review, vol. 20, no. 3, pp. 337-341, July 1999.
[8] V. Galdi, L. Ippolito, A. Piccolo, and A. Vaccaro, “A genetic-based methodology for hybrid electric vehicles sizing,” Soft Computing, vol. 6, pp. 451-457, 2001.
[9] A. Sciarretta, M. Back, and L. Guzzella, “Optimal control of parallel hybrid electric vehicles,” IEEE Trans. on Control Systems Technology, vol. 12, no. 3, pp. 352-363, May 2004.
[10] C. A. Coello Coello, D. A. Van Veldhuizen, and G. B. Lamont, Evolutionary Algorithms for Solving Multi-Objective Problems, Springer, 2007.
[11] K. Deb, Multi-Objective Optimization Using Evolutionary Algorithms, Wiley, 2001.
[12] D. Assanis, G. Delagrammatikas, R. Fellini, Z. Filipi, J. Liedtke, N. Michelena, P. Papalambros, D. Reyes, D. Rosenbaum, A. Sales, and M. Sasena, “An optimization approach to hybrid electric propulsion system design,” Mechanics Based Design of Structures and Machines, vol. 27 no. 4, pp. 393-421, 1999.
[13] S. Fish and T. B. Savoie, “Simulation-based optimal sizing of hybrid electric vehicle components for specific combat missions,” IEEE Trans. on Magnetics, vol. 37, no. 1, pp. 485-488, Jan. 2001.
[14] R. Fellini, N. Michelena, P. Papalambros, and M. Sasena, “Optimal design of automotive hybrid powertrain systems,” in Proc. of Environmentally Conscious Design and Inverse Manufacturing, Tokyo, Japan, Feb. 1999, pp.400-405.
[15] K. Wipke, T. Markel, and D. Nelson, “Optimizing energy management strategy and degree of hybridization for a hydrogen fuel cell SUV,” in Proc. 18th Electric Vehicle Symposium, Berlin, Germany, 2001.
[16] D. R. Jones, “DIRECT global optimization algorithm,” Encyclopaedia of Optimization, Kluwer Academic Publishers, 2001.
[17] P. Caratozzolo, M. Serra, and J. Riera, “Energy management strategies for hybrid electric vehicles,” in Proc. IEEE International Electric Machines and Drives Conf., Madison, WI, June 2003, vol. 1, pp. 241-248.
[18] C. C. Lin, H. Peng, J. W. Grizzle, and J. M. Kang, “Power management strategy for a parallel hybrid electric truck,” IEEE Trans. on Control Systems Technology, vol. 11, no. 6, pp. 839-849, Nov. 2003.
[19] B. M. Baumann, G. Washington, B. C. Glenn, and G. Rizzoni, “Mechatronic design and control of hybrid electric vehicles,” IEEE Trans. on Mechatronics, vol. 5, no. 1, pp. 58-71, March 2000.
[20] National Renewable Energy Laboratory, “Advanced vehicle simulator (ADVISOR) documentation,” see: http://www.ctts.nrel.gov.
[21] L. Wang, A Course in Fuzzy Systems and Control, Upper Saddle River, NJ, 1997.
[22] G. Shi, Y. Jing, A. Xu, and J. Ma, “Study and simulation of based-fuzzy-logic parallel hybrid electric vehicles control strategy,” in Proc. IEEE International Conf. on Intelligent Systems Design and Applications, Jinan, China, Oct. 2006, vol. 1, pp. 280-284.
[23] H. D. Lee and S. K. Sul, “Fuzzy-logic-based torque control strategy for parallel-type hybrid electric vehicle,” IEEE Trans. on Industrial Electronics, vol. 45, no. 4, pp. 625-632, Aug. 1998.
[24] C. Y. You, W. Y. Park, G. M. Jeong, C. Moon, and H. S. Ahn, “A comparative study of fuzzy logic-based control strategies for a parallel mild hybrid electric vehicle,” in Proc. IEEE International Conf. on Control, Automation and Systems, Seoul, Korea, Oct. 2008, pp. 1042-1046.
[25] A. Rajagopalan, G. Washington, G. Rizzoni, and Y. Guezennec, “Development of fuzzy logic control and advanced emissions modeling for parallel hybrid vehicles,” NREL/SR-540-32919, Dec. 2003.
[26] Y. Zhu, Y. Chen, G. Tian, H. Wu, and Q. Chen, “A four-step method to design an energy management strategy for hybrid vehicles,” in Proc. IEEE American Control Conf., Boston, MA, June 2004, pp. 156-161.
[27] E. D. Tate and S. P. Boyd, “Finding ultimate limits of performance for hybrid electric vehicles,” in Proc. SAE Future Transportation Technology Conf., Paper No. 00FTT-50.
[28] C. C Lin, H. Peng, J. W. Grizzle, J. M Kang “Power Management Strategy for a Parallel Hybrid Electric Truck,” IEEE Trans. on Control Systems Technology, vol. 11, no. 6, pp. 839-849, Nov. 2006.
[29] L. C. Fang and S. Y. Qin, “Concurrent optimization for parameters of powertrain and control system of hybrid electric vehicle based on multi-objective genetic algorithms,” in Proc. IEEE International Joint Conf. SICE-ICASE, Busan, Korea, Oct. 2006, pp. 2424-2429.
[30] A. Piccolo, L. Ippolito, V. zo Galdi, and A. Vaccaro, “Optimisation of energy flow management in hybrid electric vehicles via genetic algorithms,” in Proc. IEEE International Conf. on Advanced Intelligent Mechatronics, Corno, Italy, July 2001, vol. 1, pp. 434-439.
[31] J. S. Won, R. Langari, and M. Ehsani, “An energy management and charge sustaining strategy for a parallel hybrid vehicle with CVT,” IEEE Trans. on Control Systems Technology, vol. 13, no. 2, pp. 313-320, March 2005.
[32] M. Jain, C. Desai, N. Kharma, and S. S. Williamson, “Optimal powertrain component sizing of a fuel cell plug-in hybrid electric vehicle using multi-objective genetic algorithm,” in Proc. IEEE Annual Conf. of the Industrial Electronics Society, Porto, Portugal, Nov. 2009, pp. 3741-3746.
[33] C. Desai and S. S. Williamson, “Optimal design of a parallel hybrid electric vehicle using multi-objective genetic algorithms,” in Proc. IEEE Vehicle Power and Propulsion Conf., Dearborn, MI, Sept. 2009, pp. 871-876.
[34] C. Musarado, G. Rizzoni, and B. Staccia, “A-ECMS: an adaptive algorithm for hybrid electric vehicle energy management,” in Proc. IEEE Conf. on Decision and Control, Seville, Spain, Dec. 2005, pp. 1816-1823.
[35] G. Paganelli, S. Delpart, T. M. Guerra, J. Rimaux, and J. J. Santin, “Equivalent consumption minimization strategy for parallel hybrid powertrains,” in Proc. IEEE Vehicular Technology Conf., Birmingham, AL, May 2002, pp. 2076-2080.
[36] A. Sciarretta, M. Back, and L. Guzzella, “Optimal control of parallel hybrid electric vehicles,” IEEE Trans. on Control Systems Technology, vol. 12, no. 3, pp. 352-363, May 2004.
[37] M. Salman, M. F. Chang, and J. S. Chen, “Predictive energy management strategies for hybrid vehicles,” in Proc. IEEE Conf. on Decision and Control, Seville, Spain, Dec. 2005, pp. 21-25.
[38] C. Desai and S. S. Williamson, “Comparative study of hybrid electric vehicle control strategies for improved drivetrain efficiency analysis,” in Proc. IEEE Electrical Power and Energy Conference Conf., Montreal, QC, Oct. 2009, pp. 1-6.
[39] N. Srinivas and K. Deb, “Multiobjective optimization using non-dominated sorting genetic algorithms,” Journal of Evolutionary Computation, vol. 2, no. 3, pp 221-248, 1994.
[40] K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, “A fast and elitist multiobjective genetic algorithm, NSGA-II,” IEEE Trans. on Evolutionary Computation, vol. 6, no. 2, pp 182-197, April 2002.
[41] E. Zitzler, K. Deb, and L. Thiele, “Comparison of multiobjective evolutionary algorithms: Empirical results,” Evolutionary Computation Journal, vol. 8, no. 2, pp. 125-148, 2000.
[42] J. Knowles and D. Corne, “The Pareto archived evolution strategy: a new baseline algorithm for multiobjective optimization,” in Proc. IEEE Congress on Evolutionary Computation, Washington, D. C., July 1999, pp. 98-105.
[43] E. Zitzler and L. Thiele, “Multiobjective optimization using evolutionary algorithms: a comparative case study and the strength Pareto approach,” IEEE Trans. on Evolutionary Computation, vol. 3, no. 4, pp. 257-271, Nov. 1999.
[44] K. Deb, “Multi-objective genetic algorithms: problem difficulties and construction of test Functions,” Evolutionary Computation, vol. 7, no. 3, pp 205-230, 1999.
[45] M. S. Manas, “Graphical methods of multi-criterion optimization,” Zeitschrift fur Angewandte Methematic und Mechanik, vol. 62, no. 5, pp 375-377, 1982.
[46] T. Markel, “Platform engineering applied to plug-in hybrid electric vehicles,” in Proc. SAE World Congress, Detroit, MI, April 2007, Paper No. 2007-01-0292.
[47] C. Desai, M. Jain, and S. S. Williamson, “Genetic algorithm based optimal powertrain component sizing and control strategy design for a fuel cell hybrid electric bus,” in Proc. IEEE Vehicle Power and Propulsion Conf., Dearborn, MI, Sept. 2009, pp. 980-985.
[48] T. Markel and A. Simpson, “Plug-in hybrid electric vehicle energy storage system design,” in Proc. Advanced Automotive Battery Conf., Baltimore, MD, May 2006, NREL/CP-540-39614.
[49] Matlab Stateflow user guide, see www.mathworks.com.
[50] D. Harel, “Statecharts: A Visual Formalism for Complex Systems,” Science of Computer Programming, vol. 8, pp. 231–274, 1987.
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