Research Papers

State of Charge Management for Plug-In Hybrid Vehicles With Uncertain Trip Information

[+] Author and Article Information
Chris Manzie

Department of Mechanical Engineering,
The University of Melbourne,
Parkville, Victoria 3010, Australia
e-mail: manziec@unimelb.edu.au

Prakash Dewangan, Gilles Corde, Olivier Grondin, Antonio Sciarretta

IFP Energies Nouvelles,
1 et 4, avenue de Bois-Preau,
Rueil-Malmaison 92500, France

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received March 22, 2013; final manuscript received April 22, 2015; published online June 4, 2015. Assoc. Editor: Luis Alvarez.

J. Dyn. Sys., Meas., Control 137(9), 091005 (Sep 01, 2015) (7 pages) Paper No: DS-13-1129; doi: 10.1115/1.4030428 History: Received March 22, 2013; Revised April 22, 2015; Online June 04, 2015

Efficient state of charge management of plug-in hybrid electric vehicles (PHEVs) differs from their nonplug-in counterparts through the utilization of a charge depleting (CD) mode of operation. Several studies have shown that a blended mode of CD holds fuel economy advantages over a CD and charge sustaining (CS) combination, however, these approaches assume knowledge of the total journey distance. Here, this assumption is relaxed and the state of charge trajectory was recalculated online using a weaker assumption that only a probability distribution accumulated over past trips is available. The importance of other contributing factors to the state of charge profile such as vehicle velocity and altitude is also assessed. Simulation results on a prototype plug-in hybrid are presented with an adaptive equivalent consumption minimization strategy (ECMS) used by the powertrain management to track the proposed state of charge trajectory. The financial and environmental benefits of the proposed approach relative to other state of charge management strategies are then calculated over a number of different cycles and conditions.

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Fig. 1

Parallel hybrid powertrain architecture

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Fig. 2

Drive cycles used: (Top) WLTP cycle and (Bottom) A repeated modem-Hyzem base urban cycle

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Fig. 3

Velocity and altitude profiles for two consecutive WLTP cycles

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Fig. 4

Effect of gain Kp on time varying Lagrange variable

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Fig. 5

The bidelta probability distribution with p = 0.8

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Fig. 6

Different state of charge strategies for bidelta probability distribution with p = 0.8

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Fig. 7

Discretized PDF of journeys for cycles considered in Sec. 4.2




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