Modelling market diffusion of electric vehicles with real world driving data – German market and policy options

Gnann, Till; Plötz, Patrick; Kühn, André; Wietschel, Martin · 2015 · OpenAlex-citations

DOI: 10.1016/j.tra.2015.04.001

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Summary

This study addresses the uncertainty surrounding the market diffusion of electric vehicles (EVs) in Germany through 2020, motivated by the limitations of existing models that rely on average driving patterns and ignore non-monetary decision factors. The authors aim to determine how EV market share will evolve, identify key influencing factors, and evaluate effective policy options. To achieve this, they utilize the ALADIN (Alternative Automobiles Diffusion and Infrastructure) model, which simulates market diffusion based on individual user behavior rather than aggregate averages. The methodology integrates real-world driving data from three distinct user groups: private vehicle users, fleet vehicles, and company cars. Driving profiles were sourced from the German Mobility Panel for private and company cars and from GPS-tracked commercial data for fleets, capturing variations in trip length and frequency. The model calculates the utility of five propulsion technologies (gasoline, diesel, PHEV, REEV, and BEV) for each user by combining Total Cost of Ownership (TCO) with non-monetary factors, specifically the willingness to pay more for new technology and the limited availability of EV models. TCO calculations include capital expenditures, operating costs, and charging infrastructure costs, adjusted for individual driving shares and socio-demographic attributes. The analysis employs three scenarios—pro-EV, medium, and contra-EV—varying battery prices, fuel costs, and electricity prices to assess sensitivity. The results indicate significant uncertainty in EV market evolution, with the projected share of EVs in the German passenger car stock ranging from 0.4% to nearly 3% by 2020. Energy prices proved to be a critical determinant; a 25% increase in fuel prices was found to double the number of EVs in stock compared to the reference scenario. The analysis of TCO gaps revealed that EVs are already economically efficient for a subset of users with specific driving profiles, particularly those with moderate annual mileage, while high-mileage users often favor diesel due to range limitations of early EVs. Private users showed higher economic attractiveness for EVs than commercial users, largely due to VAT differences on fuel. The study concludes that the high uncertainty in market evolution necessitates dynamically adaptable policies rather than static measures. The authors identify specific monetary interventions as the most effective and efficient options, highlighting a special depreciation allowance for commercial vehicles and a subsidy of 1,000 Euros. These findings suggest that policy success depends on addressing both the economic competitiveness of EVs through cost reductions and the behavioral nuances of different user segments.

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