Optimal Operation of a Plug-In Hybrid Vehicle

Platt, Jason; Moehle, Nicholas; Fox, John D.; Dally, William

doi:10.1109/tvt.2018.2866801

Title: Optimal Operation of a Plug-In Hybrid Vehicle

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Abstract

In this work, we present a convex optimization control method that has been shown in simulations to increase the fuel efficiency of a plug-in hybrid electric vehicle by over 10%. Using information on energy demand and energy use profiles, the problem is defined to preferentially use battery resources sourced from the grid over petroleum resources. We pose the general nonlinear optimal resource management problem over a predetermined route as a convex optimization problem using a reduced model of the vehicle. This problem is computationally efficient enough to be optimized “on the fly” on the on-board vehicle computer and is thus able to adapt to changing vehicle conditions in real time. Lastly, using this reduced model to generate control inputs for the detailed vehicle simulator autonomie, we record efficiency gains of over 10% as compared to the industry standard charge depleting charge sustaining controller over synthetic mixed urban-suburban routes.

Authors:

^[1]; Moehle, Nicholas ^[2];

^[3]; Dally, William ^[2]

Univ. of California, San Diego, CA (United States)
Stanford Univ., CA (United States)
Stanford Univ., CA (United States); SLAC National Accelerator Lab., Menlo Park, CA (United States)

Publication Date:: Thu Aug 23 00:00:00 EDT 2018

Research Org.:: SLAC National Accelerator Lab., Menlo Park, CA (United States)

Sponsoring Org.:: USDOE

OSTI Identifier:: 1490463

Grant/Contract Number:: AC02-76SF00515

Resource Type:: Accepted Manuscript

Journal Name:: IEEE Transactions on Vehicular Technology

Additional Journal Information:: Journal Volume: 67; Journal Issue: 11; Journal ID: ISSN 0018-9545

Publisher:: IEEE

Country of Publication:: United States

Language:: English

Subject:: 32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION; 42 ENGINEERING; 02 PETROLEUM; Vehicles; optimal control; optimization methods

Citation Formats


                    Platt, Jason, Moehle, Nicholas, Fox, John D., and Dally, William. Optimal Operation of a Plug-In Hybrid Vehicle.  United States: N. p., 2018. 
Web.  doi:10.1109/tvt.2018.2866801.

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                    Platt, Jason, Moehle, Nicholas, Fox, John D., & Dally, William. Optimal Operation of a Plug-In Hybrid Vehicle.  United States.  https://doi.org/10.1109/tvt.2018.2866801

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                    Platt, Jason, Moehle, Nicholas, Fox, John D., and Dally, William. Thu .  
"Optimal Operation of a Plug-In Hybrid Vehicle".  United States.  https://doi.org/10.1109/tvt.2018.2866801.  https://www.osti.gov/servlets/purl/1490463.

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@article{osti_1490463,

  title        = {Optimal Operation of a Plug-In Hybrid Vehicle},

  author       = {Platt, Jason and Moehle, Nicholas and Fox, John D. and Dally, William},

  abstractNote = {In this work, we present a convex optimization control method that has been shown in simulations to increase the fuel efficiency of a plug-in hybrid electric vehicle by over 10%. Using information on energy demand and energy use profiles, the problem is defined to preferentially use battery resources sourced from the grid over petroleum resources. We pose the general nonlinear optimal resource management problem over a predetermined route as a convex optimization problem using a reduced model of the vehicle. This problem is computationally efficient enough to be optimized “on the fly” on the on-board vehicle computer and is thus able to adapt to changing vehicle conditions in real time. Lastly, using this reduced model to generate control inputs for the detailed vehicle simulator autonomie, we record efficiency gains of over 10% as compared to the industry standard charge depleting charge sustaining controller over synthetic mixed urban-suburban routes.},

  doi          = {10.1109/tvt.2018.2866801},

  journal      = {IEEE Transactions on Vehicular Technology},

  number       = 11,

  volume       = 67,

  place        = {United States},

  year         = {Thu Aug 23 00:00:00 EDT 2018},

  month        = {Thu Aug 23 00:00:00 EDT 2018}

}

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