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Look-Ahead Optimization of a Connected and Automated 48V Mild-Hybrid Electric Vehicle

Gupta, Shobhit
http://orcid.org/0000-0001-5169-3263
http://rave.ohiolink.edu/etdc/view?acc_num=osu1554478434629481

Abstract Details

2019, Master of Science, Ohio State University, Mechanical Engineering.
Increasing cost of fuel and global regulatory targets are driving the automotive industry towards fuel efficient vehicles. Hybrid electric vehicles (HEVs) can significantly improve the fuel economy by the application of an efficient control strategy. Additionally, the look-ahead information available from advanced driver assistance systems and cloud applications in a connected and automated vehicle can make the powertrain more predictive in nature. This would enable the implementation of a global optimization algorithm such as Dynamic Programming (DP). In this thesis, DP is implemented to co-optimize the vehicle velocity and energy management of a 48V mild-HEV over real world driving scenarios. Velocity optimization is performed by considering the look-ahead route characteristics such as the speed limit constraints along with the position of traffic lights and stop signs. To enable close to real-time implementation of DP, efforts have been put to alleviate the well-known "Curse of Dimensionality." A variable step size strategy is adopted instead of a constant step size. Furthermore, this thesis aims at building the Rollout Algorithm using Approximate Dynamic Programming for the 48V optimal control problem. This algorithm yields a look-ahead suboptimal control policy and under certain conditions, the sub-optimality can be minimized which is shown in this thesis. To compare the benefits obtained from the rollout, an experimentally validated driver model is developed which serves as the baseline for this project.
Marcello Canova (Advisor)
Giorgio Rizzoni (Committee Member)
Punit Tulpule (Committee Member)
154 p.
Engineering; Mechanical Engineering
Connected and Autonomous Vehicles, Hybrid Electric Vehicles, Optimization and Control, Dynamic Programming, Model Predictive Control, Rollout Algorithm, Driver Behavior

Recommended Citations

Citations

  • Gupta, S. (2019). Look-Ahead Optimization of a Connected and Automated 48V Mild-Hybrid Electric Vehicle [Master's thesis, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1554478434629481

    APA Style (7th edition)

  • Gupta, Shobhit. Look-Ahead Optimization of a Connected and Automated 48V Mild-Hybrid Electric Vehicle. 2019. Ohio State University, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1554478434629481.

    MLA Style (8th edition)

  • Gupta, Shobhit. "Look-Ahead Optimization of a Connected and Automated 48V Mild-Hybrid Electric Vehicle." Master's thesis, Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1554478434629481

    Chicago Manual of Style (17th edition)

osu1554478434629481
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This open access ETD is published by The Ohio State University and OhioLINK.