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Driving Profile Optimization for Everyday Driving

Ozatay, Engin
http://rave.ohiolink.edu/etdc/view?acc_num=osu1408915508

Abstract Details

2014, Doctor of Philosophy, Ohio State University, Electrical and Computer Engineering.
Driving style, road geometry, and traffic conditions have significant impact on the vehicle fuel economy. Many studies have shown that with Eco Driving a single vehicle can save in the range of 5-45% depending on the available traffic information. Moreover, as studied by some other researchers the Eco Driving vehicle also indirectly improves the fuel economy of the surrounding vehicles. Unfortunately, the calculation of the optimal velocity profile for a given route along with the geographical and dynamically changing traffic condition is very complex. In this thesis the goal is to calculate the global optimal velocity profile of a vehicle with the real time dynamic traffic and geographical information by taking into account the limitations of the on board computing units. This thesis tackles the problem by considering the following three aspects: 1-) Collecting static/dynamic traffic information and generating a global optimal (open loop) velocity profile, 2-) Development of a fast computing algorithm to update the optimal velocity profile with a closed loop feedback for the external disturbances, e.g., quick variations in traffic flow (accidents, etc.), 3-) Real time estimation of traffic parameters. The first aspect of the problem is handled by employing the so called “Cloud Computing”, where we could store all the static geographic and traffic information and could run computationally extensive algorithms to calculate a route based on the desired destination point and the optimal velocity profile. A connection via internet to vehicle is established and the information transfer between the cloud and the vehicle is achieved. The generated optimal velocity profile is advised to the driver. The results have shown that with this framework a significant amount of fuel can be saved. In the case of dynamically varying traffic conditions, however, the system’s performance reduces. Therefore, additionally a very efficient algorithm based on Pontryagin’s Maximum Princinple (PMP) is developed. This algorithm uses the static information that is obtained from the cloud as well as the real time on board sensor information to detect variations in the traffic and then updates the advised speed trajectory with the new calculated optimal velocity profile. Therefore, it closes the loop between the speed advisory system (SAS) and the driver’s reaction (Closed loop feedback). Finally, by using the power of the Cloud Computing and user data, we propose the estimation of the real traffic information. In this thesis an algorithm for accurate and fast detection of the traffic light parameters is proposed. The algorithm is simulated in two simulation environments as well as with real time traffic information. The results were impressive in terms of convergence speed to the correct value. The estimated traffic light parameters are used to generate optimal velocity trajectories that avoids stopping at the traffic lights and in the range of 40% fuel economy improvement is obtained.
Umit Ozguner, Prof. (Advisor)
Giorgio Rizzoni, Prof. (Committee Member)
Vadim Utkin, Prof. (Committee Member)
235 p.
Electrical Engineering

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Citations

  • Ozatay, E. (2014). Driving Profile Optimization for Everyday Driving [Doctoral dissertation, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1408915508

    APA Style (7th edition)

  • Ozatay, Engin. Driving Profile Optimization for Everyday Driving. 2014. Ohio State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1408915508.

    MLA Style (8th edition)

  • Ozatay, Engin. "Driving Profile Optimization for Everyday Driving." Doctoral dissertation, Ohio State University, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=osu1408915508

    Chicago Manual of Style (17th edition)

osu1408915508
969
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This open access ETD is published by The Ohio State University and OhioLINK.