Access Type

Open Access Dissertation

Date of Award

January 2015

Degree Type

Dissertation

Degree Name

Ph.D.

Department

Industrial and Manufacturing Engineering

First Advisor

Ratna Babu Chinnam

Abstract

Electric vehicles (EVs) hold many promises including diversification of the transportation energy feedstock and reduction of greenhouse gas and other emissions. However, achieving large-scale adoption of EVs presents a number of challenges resulting from a current lack of supporting infrastructure and difficulties in overcoming technological barriers. This dissertation addresses some of these challenges by contributing to the advancement of theories in the areas of network optimization and mechanism design.

To increase the electric driving range of plug-in hybrid electric vehicles (PHEVs), we propose a powertrain energy management control system that exploits energy efficiency dif- ferences of the electric machine and the internal combustion engine during route planning. We introduce the Energy-Efficient Routing problem (EERP) for PHEVs, and formulate this problem as a new class of the shortest path problem. We prove that the EERP is NP-complete. We then propose two exact algorithms that find optimal solutions by exploiting the transitive structure inherent in the network. To tackle the intractability of the problem, we proposed a Fully Polynomial Time Approximation Scheme (FPTAS). From a theoretic perspective, the proposed two-phase approaches improve the state-of-the-art to optimally solving shortest path problems on general constrained multi-graph networks. These novel approaches are scalable and offer broad potential in many network optimization problems. In the context of vehicle routing, this is the first study to take into account energy efficiency difference of different operating modes of PHEVs during route planning, which is a high level powertrain energy management procedure.

Another challenge for EV adoption is the inefficiency of current charging systems. In addition, high electricity consumption rates of EVs during charging make the load manage- ment of micro grids a challenge. We proposed an offline optimal mechanism for scheduling and pricing of electric vehicle charging considering incentives of both EV owners and utility companies. In the offline setting, information about future supply and demand is known to the scheduler. By considering uncertainty about future demand, we then designed a family of online mechanisms for real-time scheduling of EV charging. A fundamental problem with significant economic implications is how to price the charging units at different times under dynamic demand. We propose novel bidding based mechanisms for online scheduling and pricing of electric vehicle charging. The proposed preemption-aware charging mechanisms consider incentives of both EV drivers and grid operators. We also prove incentive-compatibility of the mechanisms, that is, truthful reporting is a dominant strategy for self-interested EV drivers. The proposed mechanisms demonstrate the benefits of electric grid load management, revenue maximization, and quick response, key attributes when providing online charging services.

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