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Use of Connected Vehicle Technology for Improving Fuel Economy and Driveability of Autonomous Vehicles

Tamilarasan, Santhosh
http://orcid.org/0000-0002-1527-9150
http://rave.ohiolink.edu/etdc/view?acc_num=osu1543787677995516

Abstract Details

2019, Doctor of Philosophy, Ohio State University, Mechanical Engineering.
Connected vehicles promise to increase transportation options and reduce travel times while improving the safety of road users. Convoying/platooning are the common use case of connected vehicles technology and the driveability performance impact of such convoy has never been researched before. The vehicles when following each other in a convoy, using adaptive cruise control (ACC), is augmented by the lead vehicle information (vehicle acceleration) through the vehicle to vehicle communication as a feedforward control is called Cooperative Adaptive Cruise Control (CACC). This dissertation analyses the impact of the desired velocity profile on the driveability characteristics of a convoy of vehicles. In order to assess the driveability performance, a framework consisting of various metrics has been developed. The parameter space robust control methodology has been used to design the controller that improves the convoy's driveability and the performance is compared to the convoy that is being tuned for maintaining the time gap. These simulation results were verified in a real-time setting using a Hardware-in-the-Loop (HIL) setup using a CARSIM high-fidelity car model. With the use of the V2X technology, the fuel economy of the connected vehicle can be improved and it is called Eco-Driving. This dissertation proposes a framework for Eco-driving that is comprised of Eco-Cruise, Greenwave algorithm, and Eco-CACC. The Eco-Cruise is the algorithm which calculates the optimal velocity profile based on the route information such as speed limit, stop sign and traffic sign location and the vehicle powertrain model. A Dynamic programming based algorithm which minimizes the fuel economy is developed. The Eco-Cruise algorithm stops at all the stop signs and traffic light (assuming red light) optimally. Driving scenario has a very big impact on the Eco-cruise algorithm, and a new methodology has been proposed in this dissertation, that formulates a metric based route selection that evaluates the potential of the Eco-cruise in the different driving scenario. When the vehicle approaches the traffic light intersection, V2X technology is used, where the Signal Phase and Timing information (SPaT) information from the traffic light is communicated via DSRC communication modem to the vehicle. The green wave algorithm utilizes the SPaT information to calculate a velocity profile that allows the vehicle to pass in green and overrides the Eco-cruise velocity profile. Although the current greenwave algorithms save fuel by not stopping at the traffic light, the explicit fuel economy optimization is not considered in the velocity profile generation. The dissertation uses an MPC methodology with non-linear optimization that generates the velocity profile that minimizes the fuel economy and satisfies the constraints and allows the vehicle to pass through greenlight. In case of the traffic situation, where there is a lead vehicle, the maximum vehicle velocity of the host vehicle is limited by the speed of the lead vehicle, and may not follow the Eco-Cruise vehicle speed. In such cases of car-following mode, the host vehicle follows the lead vehicle optimally by using the V2V communication, by varying the gap to save fuel economy. An MPC based controller has been designed for this algorithm. Thus this dissertation presents the optimal control algorithm that uses the connected vehicle technology that achieves improvement in driveability and fuel economy
Levent Guvenc (Advisor)
Vadim Utkin (Committee Member)
Bilin Aksun-Guvenc (Committee Member)
Abhishek Gupta (Committee Member)
179 p.
Automotive Engineering
Connected Vehicle; V2X; V2V; Autonomous Vehicle Fuel Economy; Optimal Control Fuel Economy; Driveability Control Autonomous Vehicle; Fuel Economy Framework Autonomous Vehicle; Driveability Framework;

Recommended Citations

Citations

  • Tamilarasan, S. (2019). Use of Connected Vehicle Technology for Improving Fuel Economy and Driveability of Autonomous Vehicles [Doctoral dissertation, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1543787677995516

    APA Style (7th edition)

  • Tamilarasan, Santhosh. Use of Connected Vehicle Technology for Improving Fuel Economy and Driveability of Autonomous Vehicles. 2019. Ohio State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1543787677995516.

    MLA Style (8th edition)

  • Tamilarasan, Santhosh. "Use of Connected Vehicle Technology for Improving Fuel Economy and Driveability of Autonomous Vehicles." Doctoral dissertation, Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1543787677995516

    Chicago Manual of Style (17th edition)

osu1543787677995516
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This open access ETD is published by The Ohio State University and OhioLINK.