Meeting the 21st century’s challenges of climate change and scarcity of crude oil requires a better understanding of the forces that ensure a successful market introduction of electric vehicles. Against this background, we develop a hybrid simulation approach to estimate the evolution of market shares of electric vehicles. The approach integrates a system dynamics model with an agent-based discrete choice model. System dynamics is used to model the interdependencies between consumer choice, consumer awareness, evolution of powertrain technologies, and service station availability on the macro level. By integrating the agent-based discrete choice model we refine the model parts of consumer choice and consumer awareness. This allows for considering individual consumer choices. Based on real-world data, we apply the model to the established and saturated German market, where replacement purchases dominate. This way we study the interdependencies between the evolution on the macro level and (1) individual replacement purchases as well as (2) heterogeneous consumer behavior. We show that both effects have a clear influence on the market share evolution of electric vehicles. The results indicate that neglecting individual consumer choices in aggregated system models may lead to systematically wrong estimations of the evolution of the market shares of electric vehicles.
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Today's automotive markets are characterized by high demand volatility. As a consequence, flexibility to re-allocate production volume within the production networks is required, to match the production capacity for specific models with market demand. The sustained re-allocation of models to production lines to adjust to market demand is no longer a vision, but virtually industry standard. Hence, model plant allocation decisions are no longer a one time long term decision but a more frequent mid term planning task, for which current strategic decision support models are inadequate. Modified planning models are required to handle the increased flexibility. A modeling framework will be presented.
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