Plug-in electric vehicles (PEV) are considered to reduce oil dependency, noise, and local air pollution as well as greenhouse gas emissions caused by road transportation. Today, the early market penetration phase has started and can be observed in many countries. But how could the diffusion and adoption of PEV be modeled to create consistent scenarios? With which PEV driving and charging behavior can these scenarios be associated and what load-shifting potentials can be derived? This work provides an answer to these questions by describing a hybrid modeling approach of a PEV diffusion scenario consisting of a top-down macro-econometric Bass model, answering the question as to at what point in time how many PEV will be on the market, and a bottom-up micro-econometric binary logistic PEV adoption model answering who is likely to adopt. This set of methods is applied to representative mobility data sets available for France and Germany in order to simulate driving and charging behaviors of potential French and German PEV adopters. In addition, a sampling method is presented, which reduces computational times while intending to remain representative of the population of PEV adopters considered. This approach enables the consideration of PEV at a detailed level in an agent-based energy system model focusing on European day-ahead markets. Results show that PEV diffusion dynamics are slightly higher in France than in Germany. Furthermore, average plug-in times, average active charging periods, average load-shifting potentials, and average energy charged per PEV differ slightly between France and Germany. Computational times can be reduced by our approach, resulting in the ability to better integrate PEV diffusion, adoption, and representative charging demand in bottom-up energy system models that simulate European wholesale electricity markets
