Skip to Main Content
 

Global Search Box

 
 
 
 

Files

File List


Zeng_dissertation 0815.pdf (5.23 MB)

ETD Abstract Container

Abstract Header

Optimally-Personalized Hybrid Electric Vehicle Powertrain Control

Zeng, Xiangrui
http://rave.ohiolink.edu/etdc/view?acc_num=osu1471342105

Abstract Details

2016, Doctor of Philosophy, Ohio State University, Mechanical Engineering.
One of the main goals of hybrid electric vehicle technology is to improve the energy efficiency. In industry and most of academic research, the powertrain control is designed and evaluated under standard driving cycles. However, the situations that a vehicle may encounter in the real world could be quite different from the standard cycles. Studies show that the human drivers have a great influence on the vehicle energy consumptions and emissions. The actual operating conditions that a vehicle faces are not only dependent on the roads and traffic, but also dependent on the drivers. A standard driving cycle can only represent the typical and averaged driving style under the typical driving scenarios, therefore the control strategies designed based on a standard driving cycle may not perform well for all different driving styles. This motivates the idea to design optimally-personalized hybrid electric vehicle control methods that can be adaptive to individual human driving styles and their driving routes. Human-subject experiments are conducted on a driving simulator to study the driving behaviors. A stochastic driver pedal model that can learn individual driver’s driving style is developed first. Then a theoretic investigation on worst-case relative cost optimal control problems, which is closely related to vehicle powertrain optimal control under real-world uncertain driving scenarios, is presented. A two-level control structure for plug-in hybrid electric vehicles is proposed, where the parameters in the lower-level controller can be on-line adjusted via optimization using historical driving data. The methods to optimize these parameters are designed for fixed-route driving first, and then extended to multi-routes driving using the idea similar to the worst-case relative cost optimal control. The performances of the two proposed methods are shown through simulations using human driving data and stochastic driver model data respectively. The energy consumption results in both situations are close to the posteriori optimal result and outperform other existing methods, which show the effectiveness of applying optimally-personalized energy management strategy on hybrid electric vehicles. Finally, a route-based global energy-optimal speed planning method is also proposed. This off-line method provides a useful tool to evaluate the potential of other speed planning methods, for either eco-driving guidance applications or future automated vehicle controls. The contributions of this dissertation include 1) a novel stochastic driver pedal behavior model which can learn independent drivers’ driving styles is created, 2) a new worst-case relative cost optimal control method is proposed, 3) a real-time implementable stochastic optimal energy management strategy for hybrid electric vehicles running on fixed routes is designed using the statistics of history driving data, 4) the fix-route strategy is extended to the multi-route situation, and 5) an off-line global energy-optimal speed planning solution for road vehicles on a given route is presented.
Junmin Wang (Advisor)
Ryan Harne (Committee Member)
Chia-Hsiang Menq (Committee Member)
Haijun Su (Committee Member)
161 p.
Automotive Engineering; Mechanical Engineering
Hybrid electric vehicle; energy management strategy; optimal control; speed planning; driver model

Recommended Citations

Citations

  • Zeng, X. (2016). Optimally-Personalized Hybrid Electric Vehicle Powertrain Control [Doctoral dissertation, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1471342105

    APA Style (7th edition)

  • Zeng, Xiangrui. Optimally-Personalized Hybrid Electric Vehicle Powertrain Control. 2016. Ohio State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1471342105.

    MLA Style (8th edition)

  • Zeng, Xiangrui. "Optimally-Personalized Hybrid Electric Vehicle Powertrain Control." Doctoral dissertation, Ohio State University, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=osu1471342105

    Chicago Manual of Style (17th edition)

osu1471342105
394
© 2016, some rights reserved.Creative Commons License Optimally-Personalized Hybrid Electric Vehicle Powertrain Control by Xiangrui Zeng is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License. Based on a work at etd.ohiolink.edu.
This open access ETD is published by The Ohio State University and OhioLINK.