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Look-Ahead Energy Management Strategies for Hybrid Vehicles.

Hegde, Bharatkumar
http://orcid.org/0000-0001-9447-8862
http://rave.ohiolink.edu/etdc/view?acc_num=osu153199304661774

Abstract Details

2018, Doctor of Philosophy, Ohio State University, Mechanical Engineering.
Hybrid electric vehicles are a result of a global push towards cleaner and fuel-efficient vehicles. They use both electrical and traditional fossil-fuel based energy sources, which makes them ideal for the transition towards much cleaner electric vehicles. A key part of the hybridization effort is designing effective energy management algorithms because they are crucial in reducing fuel consumption and emission of the hybrid vehicle. In the automotive industry, energy management systems are designed, prototyped, and validated in a software simulation environment before implementation on the hybrid vehicle. The software simulation uses model-based design techniques which reduce development time and cost. Traditionally, the design of energy management systems is based on statutory drive-cycles. Drive-cycle based solutions to energy management systems improve fuel economy of the vehicle and are well suited for statutory certification of fuel economy and emissions. In recent times however, the fuel economy and emissions over real-world driving is being considered increasingly for statutory certification. In light of these developments, methodologies to simulate and design new energy management strategies for real-world driving are needed. The work presented in this dissertation systematically addresses the challenges faced in the development of such a methodology. This work identifies and solves three sub-problems which together form the methodology for model-based real-world look-ahead energy management system development. First, a simulation framework to simulate real-world driving and look-ahead sensor emulation is developed. The simulation framework includes traffic simulation and powertrain simulation capabilities. It is termed traffic integrated powertrain co-simulation. Second, a comprehensive algorithm is developed to utilize look-ahead sensor data to accurately predict the vehicle's future velocity trajectories. Finally, through the use of optimal control algorithms, a look-ahead energy management system is developed to understand the utility of different look-ahead technologies in the improvement of fuel economy.
Giorgio Rizzoni, PhD (Advisor)
Shawn Midlam-Mohler, PhD (Committee Member)
David Hoelzle, PhD (Committee Member)
Abhishek Gupta, PhD (Committee Member)
Qadeer Ahmed, PhD (Committee Member)
252 p.
Mechanical Engineering; Transportation
energy management; hybrid vehicle; look ahead control; traffic simulation; velocity prediction; electric vehicle; range extender; series hybrid; co-simulation;

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Citations

  • Hegde, B. (2018). Look-Ahead Energy Management Strategies for Hybrid Vehicles. [Doctoral dissertation, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu153199304661774

    APA Style (7th edition)

  • Hegde, Bharatkumar. Look-Ahead Energy Management Strategies for Hybrid Vehicles. 2018. Ohio State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu153199304661774.

    MLA Style (8th edition)

  • Hegde, Bharatkumar. "Look-Ahead Energy Management Strategies for Hybrid Vehicles." Doctoral dissertation, Ohio State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=osu153199304661774

    Chicago Manual of Style (17th edition)

osu153199304661774
194
© 2018, all rights reserved.
This open access ETD is published by The Ohio State University and OhioLINK.