The Electric Vehicle (EV) is emerging as state-of-the-art technology vehicle addressing the continually pressing energy and environment concerns. The benefits of EV emerge from these vehicles’ capability of sustaining their energy demands through electric grid rather than fossil fuel consumption. Wellto-Wheel studies have shown that electric drive (E-drive) offers the highest fuel efficiency and consequently the lowest emission of green house gases. Grid electricity in the United States of America has been shown to be four times cheaper than fuel given gasoline prices at $3/gallon. Consequently, it is crucial to further optimize the electric-drive mode for EV. Battery capacity should be designed to allow EV drivers reach their destination while avoiding unnecessary stops to recharge their vehicles. However, this additional battery capacity would impact the vehicle’s space, weight and cost. In view of these limitations, we propose integrating EVs with the vision of Intelligent Transportation Systems (ITS). This chapter starts out by putting the design of EVs into a broader perspective by proposing a Predictive Intelligent Battery Management System (PIBMS), which will enhance the overall performance of EVs including energy consumption and emissions using the ITS infrastructure. At the end of this chapter, the reader should have an understanding of the capabilities and limitations of the PIBMS. It lays out the design foundation for the future implementation of an interconnected EV equipped with PIBMS, which further contributes to the optimization of energy efficiency and reduced emissions.
[1]
M J Lighthill,et al.
On kinematic waves II. A theory of traffic flow on long crowded roads
,
1955,
Proceedings of the Royal Society of London. Series A. Mathematical and Physical Sciences.
[2]
Michael Wang,et al.
Well-to-Wheels Analysis of Advanced Fuel/Vehicle Systems — A North American Study of Energy Use, Greenhouse Gas Emissions, and Criteria Pollutant Emissions
,
2005
.
[3]
Mohamad Abdul-Hak,et al.
ITS based Predictive Intelligent Battery Management System for plug-in Hybrid and Electric vehicles
,
2009,
2009 IEEE Vehicle Power and Propulsion Conference.
[4]
Tiago T. V. Vinhoza,et al.
Modeling and Simulation of Vehicular Networks: towards Realistic and Efficient Models
,
2011,
AdHocNets 2011.
[5]
Loren Bloomberg,et al.
Comparison of VISSIM and CORSIM Traffic Simulation Models on a Congested Network
,
2000
.
[6]
A. Frank,et al.
Hybrid vehicles gain traction.
,
2006,
Scientific American.