Sustainable Transportation with Electric Vehicles

Abstract

Electric vehicles are gaining more and more popularity due to lowoil dependency and low emission. Their deep penetration will significantlybenefit the environment, but meanwhile will cause two crucialconsequences. First, electric vehicles introduce heavy load impact intothe power grid by shifting energy demand from gasoline to electricity.The surging load will compromise the grid's reliability and jeopardizeits power supply quality. Second, charging stations become indispensableinfrastructure to support large deployment of electric vehicles. Theavailability in public destinations comes with electric vehicles competingfor both power supply and service points of charging stations. Thecompetition degrades quality of service and thus can compromise theoriginal intent of advocating electric vehicles.There are many research efforts addressing either of the two consequencesabove. Different with them, we consider both and jointly studyquality of service for electric vehicle users and reliability of the powergrid. We review recent developments on this topic in this article. InChapter 1, we introduce the ecosystem of electric vehicles and discussmotivations for managing charging load. This chapter further presentsa systematic solution framework for smart electric vehicle charging. Thefollowing chapters then study each block of the framework. Specially, inChapter 2, we investigate charging rate control, which handles how toallocate power supply to electric vehicles within a charging station. InChapter 3, we address electric vehicle demand response, which is howto make electric vehicles follow the power supply of charging stationsand the power grid. In Chapter 4, we study electric vehicle scheduling,which copes with how to schedule electric vehicles to multiple chargingpoints within a charging station. In Chapter 5, we discuss chargingdemand balancing, which deals with how to balance electric vehiclesamong multiple charging stations.In these chapters, we first present deployable algorithms and mechanismsthat are designed for each framework blocks. Then, we evaluatethe proposed approaches by two complementary ways. One way isleveraging theoretical analysis to demonstrate their performance guarantees,while the other is using extensive simulations based on realisticdata traces and simulation tools. We also review studies that alignwith the corresponding framework blocks and consider additional dimensionsand/or different optimization goals. Finally, in Chapter 6,we conclude the article with summaries of main ideas discussed in theprevious chapters.