NDLI: Multi-information integrated trip specific optimal power management for plug-in hybrid electric vehicles

Content Provider	IEEE Xplore Digital Library
Author	Yang Bin Yaoyu Li Qiuming Gong Zhong-Ren Peng
Copyright Year	2009
Description	Author affiliation: University of Wisconsin-Milwaukee, 53211 USA (Yang Bin; Yaoyu Li; Qiuming Gong) \|\| University of Florida at Gainesville, 32611 USA (Zhong-Ren Peng)
Abstract	Plug-in hybrid electric vehicles (PHEV) are widely received as a promising means of green mobility by utilizing more battery power. Recently, we have proposed a scheme of two-scale spatial-domain dynamic programming (DP) as a nearly global optimization approach to trip based optimal power management for PHEV through the combination with traffic data and trip modeling. Previously, the segment-wise power demand and SOC change was calculated through numerical integration based on the average speed and acceleration of the segment, and lookup tables were obtained. When more parameters are involved into power management, such as road grade and load change, such process becomes very tedious. In this paper, the spatial-domain DP is improved by calculating the power demand and SOC change in an analytical manner. The power demand is first calculated based on length, initial speed, acceleration, road grade, payload and wind of a road segment. The SOC change is then calculated for different PSR. An adjustable segment scheme used of analytical function is developed in order to improve the computation efficiency of the optimal power management without losing much of fuel economy. Simulation study shows that incorporating additional trip information such as road grade and predictable payload change into the optimization can significantly improve the fuel economy. The computational efficiency is also evaluated. The proposed method can greatly facilitate the development of optimal power management strategy for PHEV with multiple information inputs.
Starting Page	4607
Ending Page	4612
File Size	481729
Page Count	6
File Format	PDF
ISBN	9781424445233
ISSN	07431619
DOI	10.1109/ACC.2009.5160626
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-06-10
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	American Automatic Control Council(AACC)
Subject Keyword	Energy management Hybrid electric vehicles Roads Power demand Acceleration Payloads Fuel economy Batteries Dynamic programming Traffic control
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering

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