Geodemographic analysis and estimation of early plug-in hybrid electric vehicle adoption

Electric vehicles and hybrids are expected to become increasingly common in the coming years. The implications of growing adoption depend on its geographical extent. For instance, vehicles that are chargeable from the electrical grid, such as plug-in hybrids, can introduce problems for the distribution network especially if the vehicle adoption is spatially concentrated. In this paper, the adoption of hybrid electric vehicles is analysed in heterogeneous areas. The main purpose is to study the interrelationships between early hybrid electric vehicle adoption and different demographic and socio-economic characteristics of the areas. It is further discussed how the results can be applied to estimate the upcoming plug-in hybrid adoption. As there is a vast amount of information in the various registers of the society, slowly being opened for free usage but not fully utilised so far, it is also of interest to study and demonstrate the usability of public register data in this context. Our analysis suggests that certain characteristics of the areas strongly correlate with the hybrid electric vehicle adoption. The results of this study could be relevant, e.g., for electric distribution network planning, targeting policies to support cleaner vehicle adoption, marketing hybrid vehicles and locating charging stations.

[1]  Donald W. Bouldin,et al.  A Cluster Separation Measure , 1979, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  K. Kurani,et al.  Car buyers and fuel economy , 2007 .

[3]  Frank M. Bass,et al.  DIRECTV: Forecasting Diffusion of a New Technology Prior to Product Launch , 2001, Interfaces.

[4]  Teuvo Kohonen,et al.  Self-Organizing Maps , 2010 .

[5]  Chris Gearhart,et al.  A statistical approach to estimating acceptance of electric vehicles and electrification of personal transportation , 2013 .

[6]  Andreas Poullikkas,et al.  Sustainable options for electric vehicle technologies , 2015 .

[7]  Jukka Lassila,et al.  NETWORK EFFECTS OF ELECTRIC VEHICLES - CASE FROM NORDIC COUNTRY , 2011 .

[8]  Esa Alhoniemi,et al.  Clustering of the self-organizing map , 2000, IEEE Trans. Neural Networks Learn. Syst..

[9]  Saifur Rahman,et al.  Challenges of PHEV penetration to the residential distribution network , 2009, 2009 IEEE Power & Energy Society General Meeting.

[10]  H. L. Le Roy,et al.  Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability; Vol. IV , 1969 .

[11]  Esa Alhoniemi,et al.  SOM Toolbox for Matlab 5 , 2000 .

[12]  J. MacQueen.  Some methods for classification and analysis of multivariate observations , 1967 .

[13]  Tim Herzog.  World Greenhouse Gas Emissions in 2005 , 2009 .

[14]  Reid R. Heffner,et al.  Symbolism in California’s Early Market for Hybrid Electric Vehicles , 2007 .

[15]  C. Erdem,et al.  Identifying the factors affecting the willingness to pay for fuel-efficient vehicles in Turkey: A case of hybrids , 2010 .

[16]  F. J. Soares,et al.  Identifying management procedures to deal with connection of Electric Vehicles in the grid , 2009, 2009 IEEE Bucharest PowerTech.

[17]  Sean Lyons,et al.  The diffusion of electric vehicles: An agent-based microsimulation , 2014 .