Modelling market diffusion of electric vehicles with real world driving data — Part I: Model structure and validation

Plötz, Patrick; Gnann, Till; Wietschel, Martin · 2014 · OpenAlex-citations

DOI: 10.1016/j.ecolecon.2014.09.021

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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 Total Cost of Ownership (TCO) models that rely on average driving patterns. The authors argue that individual driving profiles vary significantly, affecting EV viability, and that previous models often neglect non-monetary factors like consumer willingness to pay. The research aims to determine how EV markets will evolve, identify key influencing factors, and evaluate policy options to foster adoption. The authors utilize the ALADIN (Alternative Automobiles Diffusion and Infrastructure) model, which simulates market diffusion based on real-world driving data. The model incorporates three user groups—private, fleet, and company car users—using data from the German Mobility Panel and GPS-tracked commercial profiles. It distinguishes between five propulsion technologies: gasoline, diesel, plug-in hybrid (PHEV), range-extended electric (REEV), and battery electric vehicles (BEV). The methodology involves three steps: simulating battery state-of-charge for each user’s driving profile to determine electric driving shares; calculating individual utility by combining TCO, willingness to pay more, and limited vehicle choice; and aggregating these into a stock model. Parameters for battery prices, fuel costs, and electricity prices were defined across three scenarios: pro-EV, medium, and contra-EV. Results indicate significant uncertainty in EV market evolution, with the future 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 would double the EV stock by 2020 compared to the reference scenario. TCO analysis revealed that EVs are already economically efficient for some users, particularly those with high annual mileage, though diesel remains competitive for very high-mileage users due to EV range limitations. Private users showed higher economic attractiveness for EVs than commercial users, largely due to VAT differences on fuel. The study found that large passenger cars and private owners had the highest potential for EV adoption. The significance of these findings lies in the recommendation for dynamically adaptable policies, given the high sensitivity of EV diffusion to external conditions. The authors identify a special depreciation allowance for commercial vehicles and a subsidy of 1,000 Euro as the most effective and efficient monetary policy options. The study underscores that successful EV market penetration depends not only on technological advancements and cost reductions but also on individual user behavior and targeted policy interventions that account for varying user segments and driving patterns.

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