Mobility Model for the Estimation of the Spatiotemporal Energy Demand of Battery Electric Vehicles in Singapore

In order to facilitate detailed research on charging infrastructure, battery energy content and charging strategies, we generate a model to illustrate the spatiotemporal energy consumption in Singapore. The model considers the influence of realistic travel and driving behavior patterns in Singapore. Furthermore, it employs a conceivable future population of battery electric vehicles (BEVs), based on the current vehicle population in Singapore as well as the spectrum of commercially available BEVs. We showcase the possible applications of the model by using a basic charging behavior model to analyze the additional spatiotemporal energy demand, generated by the introduction of BEVs.

[1]  Sascha Moecker Driving Profile and Energy Demand Analysis for Electrical Vehicles based on GPS Trajectories , 2014 .

[2]  M. Heinig,et al.  The “Price” of Information , 2003, Journal of human lactation : official journal of International Lactation Consultant Association.

[3]  Alexis Kwasinski,et al.  Spatial and Temporal Model of Electric Vehicle Charging Demand , 2012, IEEE Transactions on Smart Grid.

[4]  Zhili Zhou,et al.  Spatial and Temporal Model for Electric Vehicle rapid charging demand , 2012, 2012 IEEE Vehicle Power and Propulsion Conference.

[5]  Thomas Hamacher,et al.  Effects of large scale EV and PV integration on power supply systems in the context of Singapore , 2012, 2012 3rd IEEE PES Innovative Smart Grid Technologies Europe (ISGT Europe).

[6]  Ryoji Hiwatari,et al.  A road traffic simulator to analyze layout and effectiveness of rapid charging infrastructure for electric vehicle , 2011, 2011 IEEE Vehicle Power and Propulsion Conference.