Date of Award


Document Type


Degree Name

Doctor of Philosophy (PhD)

Legacy Department

Automotive Engineering

Committee Member

Dr. Pierluigi Pisu, Committee Chair

Committee Member

Dr. Beshah Ayalew

Committee Member

Dr. Robert Prucka

Committee Member

Dr. Ardalan Vahidi


Environmental concerns along with stronger governmental regulations regarding automotive fuel-economy and greenhouse-gas emissions are contributing to the push for development of more sustainable transportation technologies. Furthermore, the widespread use of the automobile gives rise to other issues such as traffic congestion and increasing traffic accidents. Consequently, two main goals of new technologies are the reduction of vehicle fuel consumption and emissions and the reduction of traffic congestion. While an extensive list of published work addresses the problem of fuel consumption reduction by optimizing the vehicle powertrain operations, particularly in the case of hybrid electric vehicles (HEV), approaches like eco-driving and traffic coordination have been studied more recently as alternative methods that can, in addition, address the problem of traffic congestion and traffic accidents reduction. This dissertation builds on some of those approaches, with particular emphasis on autonomous vehicle coordination control. In this direction, the objective is to derive an optimization approach for energy efficient and safe coordination control of vehicles in merging highways. Most of the current optimization-based centralized approaches to this problem are solved numerically, at the expense of a high computational load which limits their potential for real-time implementation. In addition, closed-form solutions, which are desired to facilitate traffic analysis and the development of approaches to address interconnected merging/intersection points and achieve further traffic improvements at the road-network level, are very limited in the literature. In this dissertation, through the application of the Pontryagin’s minimum principle, a closed-form solution is obtained which allows the implementation of a real-time centralized optimal control for fleets of vehicles. The results of applying the proposed framework show that the system can reduce the fuel consumption by up to 50% and the travel time by an average of 6.9% with respect to a scenario with not coordination strategy. By integrating the traffic coordination scheme with in-vehicle energy management, a two level optimization system is achieved which allows assessing the benefits of integrating hybrid electric vehicles into the road network. Regarding in-vehicle energy optimization, four methods are developed to improve the tuning process of the equivalent consumption optimization strategy (ECMS). First, two model predictive control (MPC)-based strategies are implemented and the results show improvements in the efficiency obtained with the standard ECMS implementation. On the other hand, the research efforts focus in performing analysis of the engine and electric motor operating points which can lead to the optimal tuning of the ECMS with reduced iterations. Two approaches are evaluated and even though the results in fuel economy are slightly worse than those for the standard ECMS, they show potential to significantly reduce the tuning time of the ECMS. Additionally, the benefits of having less aggressive driving profiles on different powertrain technologies such as conventional, plug-in hybrid and electric vehicles are studied.



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