Modern traffic prediction technologies enable real-time velocity planning of vehicles for less fuel consumption and polluting emissions by reducing the frequency of acceleration/deceleration, idle time, the number of stop, and variation of vehicle speeds. The fuel economy could be further improved if the optimal control strategy parameter could be used in the real-time velocity planning. However, it is difficult to find the optimal value of the control strategy parameter in this real-time velocity planning of vehicles. This paper aims to develop an advising system for control strategy parameters of HEVs in velocity planning. With this aim, the characteristics of the optimal control strategy parameters for various velocity profiles obtained from predictive velocity planning are studied in a parallel HEV. The optimal control strategy parameters with the effect of the average speed, stop frequency, and the traveling distance are investigated. The observed characteristics of the optimal parameters are obtained and can be used in the advising system to improve fuel economy in real-time velocity planning of HEVs.
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ASME 2012 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
August 12–15, 2012
Chicago, Illinois, USA
Conference Sponsors:
- Design Engineering Division
- Computers and Information in Engineering Division
ISBN:
978-0-7918-4505-9
PROCEEDINGS PAPER
Characterizing Optimal Control Strategy Parameters for Improving Fuel Economy of Hybrid Electric Vehicles in Velocity Planning
Jinling Wang,
Jinling Wang
National University of Singapore, Singapore
Search for other works by this author on:
Wen F. Lu
Wen F. Lu
National University of Singapore, Singapore
Search for other works by this author on:
Jinling Wang
National University of Singapore, Singapore
Wen F. Lu
National University of Singapore, Singapore
Paper No:
DETC2012-70831, pp. 441-450; 10 pages
Published Online:
September 9, 2013
Citation
Wang, J, & Lu, WF. "Characterizing Optimal Control Strategy Parameters for Improving Fuel Economy of Hybrid Electric Vehicles in Velocity Planning." Proceedings of the ASME 2012 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. Volume 6: 1st Biennial International Conference on Dynamics for Design; 14th International Conference on Advanced Vehicle Technologies. Chicago, Illinois, USA. August 12–15, 2012. pp. 441-450. ASME. https://doi.org/10.1115/DETC2012-70831
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