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Trip Based Power Management of Plug-in Hybrid Electric Vehicle with Two-Scale Dynamic Programming

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3 Author(s)
Qiuming Gong ; Department of Mechanical Engineering, University of Wisconsin-Milwaukee, Milwaukee, WI 53211 ; Yaoyu Li ; Zhong-Ren Peng

The plug-in HEV (PHEV), utilizing more battery power, has become the next-generation HEV with great promise of higher fuel economy. The charge-depletion mode is more appropriate for the power management of PHEV, i.e. the state of charge (SOC) is expected to drop to a low threshold when the vehicle reaches the destination of the trip. Globally optimized charge-depletion power management would be desirable. However, this has so far been hampered due to the a priori nature of the trip information and the prohibitive computational cost of global optimization techniques such as dynamic programming (DP). This situation can be changed by the current advancement of intelligent transportation systems (ITS) based on the use of on-board GPS, GIS, real-time and historical traffic flow data and advanced traffic flow modeling techniques. In this paper, charge-depletion control of PHEV is nearly globally optimized with a two-scale dynamic programming approach based on trip modeling with real-time and historical traffic data. For DP based charge-depletion control of PHEV, the SOC is desired to drop to a specific terminal value at the end of the trip. By specifying the origin and destination of a trip, the trip model, i.e. the driving cycle, is first obtained with the average of the historic traffic data, and the globally optimized SOC profile can be obtained by solving the overall or the macro-scale DP problem. The actual power management can be adapted during real-time vehicle operation with a micro-scale DP framework. The whole trip is divided into a number of segments, and for each segment, a smaller DP will be solved using the on-line traffic data transmitted to the vehicle from the traffic flow sensors within the segment. The SOC obtained in the macro-scale DP solution at the terminal location is reinforced to be the final value. Simulation study has been performed on a hybrid SUV model from ADVISOR, and a defined trip in the greater Milwaukee area. The simulation results demo- nstrated significant improvement in fuel economy using DP based charge-depletion control compared to rule based control, and also the benefit of adaptation using the two-scale DP method.

Published in:

2007 IEEE Vehicle Power and Propulsion Conference

Date of Conference:

9-12 Sept. 2007