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Combined Design and Control Optimization of Autonomous Plug-In Hybrid Electric Vehicle Powertrains

Amoussougbo, Thibaut
http://orcid.org/0000-0003-4857-2297
http://rave.ohiolink.edu/etdc/view?acc_num=ucin1623241895255747

Abstract Details

2021, MS, University of Cincinnati, Engineering and Applied Science: Mechanical Engineering.
A major emphasis within the automotive industry today is autonomous driving. Many recent studies in this area deal with the development of real-time optimal control strategies to improve overall vehicle energy efficiency. Although such research is critically important, it overlooks the potential need to reevaluate the design of an autonomous vehicle itself, especially as it relates to the powertrain. Failing to thoroughly examine the impact of autonomous driving on vehicle powertrain design could limit the potential opportunities to augment the energy-efficiency gains from optimal powertrain control (power demand) strategies. Therefore, this thesis addresses this situation by investigating the impact of autonomous driving on the design (sizing) and control strategies (energy management + power demand) of a plug-in hybrid-electric vehicle (PHEV) powertrain. In particular, a dynamic optimization method known as multidisciplinary dynamic system design optimization (MDSDO) is used to formulate and solve a combined optimal design and control optimization (or control co-design) problem for an autonomously-driven PHEV powertrain under two simulation conditions: in the first, only an autonomous driving cycle represented by a hypothetical lead (HL) duty cycle is considered, whereas the second also includes acceleration and all-electric range (AER) performance along with the HL duty cycle in order to generate an overall powertrain design solution. The optimal solutions for both simulation conditions are then compared to those corresponding to a control co-design problem for a human-driven PHEV powertrain, with the results indicating that autonomous driving does indeed have a significant impact on both powertrain design and control. Therefore, this implies a compelling need to reevaluate current powertrain design conventions when developing autonomous vehicles.
Michael Alexander-Ramos, Ph.D. (Committee Chair)
Manish Kumar, Ph.D. (Committee Member)
David Thompson, Ph.D. (Committee Member)
57 p.
Engineering
Control-design; Autonomous-driving; Hybrid electric vehicle; optimal control

Recommended Citations

Citations

  • Amoussougbo, T. (2021). Combined Design and Control Optimization of Autonomous Plug-In Hybrid Electric Vehicle Powertrains [Master's thesis, University of Cincinnati]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1623241895255747

    APA Style (7th edition)

  • Amoussougbo, Thibaut. Combined Design and Control Optimization of Autonomous Plug-In Hybrid Electric Vehicle Powertrains. 2021. University of Cincinnati, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ucin1623241895255747.

    MLA Style (8th edition)

  • Amoussougbo, Thibaut. "Combined Design and Control Optimization of Autonomous Plug-In Hybrid Electric Vehicle Powertrains." Master's thesis, University of Cincinnati, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1623241895255747

    Chicago Manual of Style (17th edition)

ucin1623241895255747
81
© 2021, all rights reserved.
This open access ETD is published by University of Cincinnati and OhioLINK.