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Impact of Hilly Road Profile on Optimal Energy Management Strategy for FCHEV with Various Battery Sizes
ISSN: 0148-7191, e-ISSN: 2688-3627
Published October 14, 2013 by SAE International in United States
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This study investigates how hilly road profiles affect the optimal energy management strategy for fuel cell hybrid electric vehicle (FCHEV) with various battery sizes. First, a simplified FCHEV model is developed to describe power and energy flows throughout the powertrain and evaluate hydrogen consumption. Then, an optimal control problem is formulated to find the globally optimal energy management strategy of FCHEV over driving cycles with road elevation profile. In order to solve the optimal energy management problem of the FCHEV, Dynamic Programming, a dynamic optimization method, is used, and their results are analyzed to find out how hilly road conditions affect the optimal energy management strategies. The results show that the optimal energy management with a smaller battery tends to actively prepare (e.g. pre-charge/pre-discharge) for uphill/downhill roads in order not to violate the battery state of charge (SoC) bounds. On the other hand, when the battery is large enough to handle a deep SoC swing due to hilly road profile, the optimal energy management strategy is not significantly affected by various battery sizes. In conclusion, when an energy management strategy is designed for FCHEV, the designer needs to utilize the road altitude information in order to achieve near-optimal fuel economy with charge-sustenance.
CitationHan, J., Park, Y., Kum, D., Ryu, S. et al., "Impact of Hilly Road Profile on Optimal Energy Management Strategy for FCHEV with Various Battery Sizes," SAE Technical Paper 2013-01-2542, 2013, https://doi.org/10.4271/2013-01-2542.
- Feroldi, D., Serra, M., and Riera, J., “Energy Management Strategies based on efficiency map for Fuel Cell Hybrid Vehicles,” Journal of Power Sources 190(2):387-401, 2009, doi:10.1016/j.jpowsour.2009.01.040.
- Schouten, N. J., Salman, M. A., and Kheir, N. A., “Fuzzy logic control for parallel hybrid electric vehicles,” IEEE Trans. on Contr. Syst. Technol. 10(3):460-468, 2002, doi:10.1109/87.998036.
- Brahma, A., Guezennec, Y., and Rizzoni, G., “Optimal energy management in series hybrid electric vehicles,” Proc. of American control conference 2000, 2000, doi:10.1109/ACC.2000.878772.
- Lin, C., Peng, H., Grizzle, J. W., and Kang, J., “Power Management Strategy for a Parallel Hybrid Electric Truck,” IEEE Trans. on Contr. Syst. Technol. 11(6):839-849, 2003, doi:10.1109/TCST.2003.815606.
- Paganelli, G., Ercole, G., Brahma, A., Guezennec, Y., et al., “General supervisory control policy for the energy optimization of charge-sustaining hybrid electric vehicles,” JSAE Review 22(4):511-518, 2001, doi:10.1016/S0389-4304(01)00138-2.
- Sciarretta, A., Back, M., and Guzzella, L., “Optimal control of parallel hybrid electric vehicles,” IEEE Trans. Contr. Syst. Technol. 12(3):352-363, 2004, doi:10.1109/TCST.2004.824312.
- Rodatz, P., Paganelli, G., Sciarretta, A., and Guzzella, L., “Optimal power management of an experimental fuel cell/supercapacitor-powered hybrid vehicle,” Control Eng. Pract. 13(1):41-53, 2005, doi:10.1016/j.conengprac.2003.12.016.
- Hellström, E., Ivarsson, M., Åslund, J., and Nielsen, L., “Look-ahead control for heavy trucks to minimize trip time and fuel consumption,” Control Eng. Pract. 17(2):245-254, 2009, doi:10.1016/j.conengprac.2008.07.005.
- Zhang, C., Vahidi, A., Pisu, P., Li, X., et al., “Role of terrain preview in energy management of hybrid electric vehicles,” IEEE Trans. Veh. Technol. 59(3):1139-1147, 2010, doi:10.1109/TVT.2009.2038707.
- Bellman, R. E., Dynamic programming, Princeton University Press, 1957
- Bertsekas, D. P., Dynamic Programming and Optimal Control, Belmont, Athena Scientific, 2007
- Guzzella, L., Sciarretta, A., Vehicle propulsion Systems, Springer, 2007
- Serrao, L., Onori, S., and Rizzoni, G., “A Comparative Analysis of Energy Management Strategies for Hybrid Electric Vehicles”, ASME J. Dyn. Syst., Meas. Control., 133(3) doi:10.1115/1.4003267.