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World Electric Vehicle Journal is published by MDPI from Volume 9 issue 1 (2018). Articles in this Issue were published by The World Electric Vehicle Association (WEVA) and its member the European Association for e-Mobility (AVERE), the Electric Drive Transportation Association (EDTA), and the Electric Vehicle Association of Asia Pacific (EVAAP). They are hosted by MDPI on mdpi.com as a courtesy and upon agreement with AVERE.
Open AccessArticle

Optimal Control Strategy for PHEVs Using Prediction of Future Driving Schedule

1
Seoul National University, Seoul, South Korea
2
Argonne National Laboratory, Argonne, Illinois, USA
*
Author to whom correspondence should be addressed.
World Electr. Veh. J. 2012, 5(1), 149-158; https://doi.org/10.3390/wevj5010149
Published: 30 March 2012
PDF [662 KB, uploaded 17 May 2018]

Abstract

Optimization-based control methods for plug-in hybrid electric vehicles require knowledge about an entire driving cycle and an elevation profile to obtain optimal performance over a fixed driving route. This paper details our investigation into the method of using traffic information to predict the future driving cycle, as well as an examination of the optimal control strategy based on Pontryagin’s Minimum Principle, in order to minimize fuel consumption on a given trip distance and to develop a real-time implementable control strategy. To predict future driving patterns, the Dynamic Programming theory is proposed for the calculation of vehicle speed with respect to driving distance, under the assumption that data about traffic conditions are obtained from external traffic information, such as Intelligent Transportation Systems. Prediction of future driving speed is achieved by minimizing the proposed cost function on each segment. The results of the generated speed profile can properly estimate the driving pattern of the driver. Also, a co-state generation algorithm is applied to determine the parameters with respect to the required power deduced from the predicted driving cycle. The proposed co-state generation model can find the estimated initial co-state that is similar to the optimal co-state. Simulation results indicate that this approach guarantees the best efficiency under reasonable conditions and the minimization of fuel consumption on the trip distance between the origin and destination.
Keywords: PHEV (plug-in hybrid electric vehicle); EREV (extended range electric vehicle); power management; control system; city traffic PHEV (plug-in hybrid electric vehicle); EREV (extended range electric vehicle); power management; control system; city traffic
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Lee, D.; Cha, S.W.; Rousseau, A.; Kim, N. Optimal Control Strategy for PHEVs Using Prediction of Future Driving Schedule. World Electr. Veh. J. 2012, 5, 149-158.

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