Axel Ensslen

Methods for forecasting the market penetration of electric drivetrains in the passenger car market

ABSTRACT Current car technologies will not solve upcoming challenges of mitigating greenhouse gas emissions in road transport. Projections of the market penetration by alternative drive train technologies are controversial regarding both forecast market shares and applied scientific methods. Accepting this latter challenge, we provide a (so far missing) overview of methods applied in this field and give some recommendations for further work. Our focus is to classify the applied methods into a convenient pattern and to analyse models from the recent scientific literature which consider the electrification of light-duty vehicles. We differentiate the following bottom-up approaches: Econometric models with disaggregated data (such as discrete choice), and agent-based simulation models. The group of top-down models are subdivided into econometric models with aggregated data (e.g. vehicle stock data), system dynamics, as well as integrated assessment models with general equilibrium models. It becomes obvious that some methods have a stronger methodological background whereas others require comprehensive data sets or can be combined more flexibly with other methods. Even though there is no dominant method, we can identify a trend in the literature towards data-driven hybrid approaches, which considers micro and macro aspects influencing the market penetration of electric vehicles.

On the road to an electric mobility mass market - How can early adopters be characterized?

Different field trials and corresponding acceptance studies with new technologies have been carried out between 2010 and 2013 at the Chair of Energy Economics at the Karlsruhe Institute of Technology (KIT). Those involved Elec-tric Vehicle (EV) users, Liquefied Petroleum Gas (LPG) and Compressed Natural Gas (CNG) vehicle users as well as persons with strong interest in EV and smart home technologies. In order to characterize early adopters the same item-sets con-cerning attitudes regarding climate change, prices and innovations as well as cor-responding socio-demographic characteristics, were used throughout all these studies and have been joined now and analyzed together. Additionally, regression methods have been applied in order to characterize early EV adopters based on a subsample of EV company car users in the French-German context. A binary logit model explaining private EV purchase intention has been developed. According to this model, early private EV adopters are likely to have a higher level of income, to have a household equipped with two or more cars and to travel more than 50 kilometers a day, not necessarily by car. This model additionally shows that possi-bilities to experience EV (e.g. by test drives) are important leverages to support adoption of EV by private car buyers. Respondents who already decided to pri-vately purchase an EV show significantly lower general price sensitivities than the LPG and CNG vehicle users.