A Hybrid Simulation Approach for Estimating the Market Share Evolution of Electric Vehicles

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.

[1]  K. Train,et al.  Joint mixed logit models of stated and revealed preferences for alternative-fuel vehicles , 1999, Controlling Automobile Air Pollution.

[2]  M. Ben-Akiva,et al.  Discrete choice models with latent choice sets , 1995 .

[3]  J. Sterman.  Business Dynamics , 2000 .

[4]  K. Train,et al.  Forecasting new product penetration with flexible substitution patterns , 1998 .

[5]  Mehdi Amini,et al.  Alternative supply chain production-sales policies for new product diffusion: An agent-based modeling and simulation approach , 2012, Eur. J. Oper. Res..

[6]  K. Train.  Discrete Choice Methods with Simulation , 2003 .

[7]  J. G. Wissema,et al.  Trends in technology forecasting , 1982 .

[8]  John D. Sterman,et al.  Business dynamics : systems thinking and modelling for acomplex world , 2002 .

[9]  P. Haan,et al.  How much do incentives affect car purchase? Agent-based microsimulation of consumer choice of new cars—Part I: Model structure, simulation of bounded rationality, and model validation , 2009 .

[10]  G. Nigel Gilbert,et al.  Simulation for the social scientist , 1999 .

[11]  Sonja Gensler,et al.  A Study of the Diffusion of Alternative Fuel Vehicles: An Agent‐Based Modeling Approach* , 2011 .

[12]  S. K. Ribeiro.  Transport and its infrastructure , 2007 .

[13]  John D. Sterman,et al.  System Dynamics: Systems Thinking and Modeling for a Complex World , 2002 .

[14]  Hazhir Rahmandad,et al.  Heterogeneity and Network Structure in the Dynamics of Diffusion: Comparing Agent-Based and Differential Equation Models , 2004, Manag. Sci..

[15]  V. Mahajan,et al.  Innovation diffusion and new product growth models: A critical review and research directions , 2010 .

[16]  Rosanna Garcia.  Uses of Agent-Based Modeling in Innovation/New Product Development Research , 2005 .

[17]  Thomas Volling,et al.  Supporting Strategic Product Portfolio Planning by Market Simulation , 2012 .

[18]  John R. Hauser,et al.  Prelaunch forecasting of new automobiles , 1990 .

[19]  Patrick E. Meyer,et al.  Modeling technology diffusion of complementary goods: The case of hydrogen vehicles and refueling infrastructure , 2009, Technovation.

[20]  Alain L. Kornhauser,et al.  A GAME THEORETIC/NETWORK EQUILIBRIUM SOLUTION APPROACH FOR THE RAILROAD FREIGHT CAR MANAGEMENT PROBLEM , 1993 .

[21]  Frank H. Maier.  New product diffusion models in innovation management—a system dynamics perspective , 1998 .

[22]  Todd K. BenDor,et al.  Simulating a combination of feebates and scrappage incentives to reduce automobile emissions , 2006 .

[23]  Michael Wildpaner,et al.  An agent-based simulation approach for the new product diffusion of a novel biomass fuel , 2011, J. Oper. Res. Soc..

[24]  K. Train,et al.  Forthcoming in the International Economic Review. , 2006 .

[25]  Simon Shepherd,et al.  Factors affecting future demand for electric vehicles: A model based study , 2012 .

[26]  David B. Montgomery,et al.  Stochastic Models of Buying Behavior. , 1970 .

[27]  M. Raberto,et al.  An agent-based modeling approach to predict the evolution of market share of electric vehicles: A case study from Iceland , 2012 .

[28]  J. Sterman,et al.  Transition challenges for alternative fuel vehicle and transportation systems , 2006 .

[29]  Panos Y. Papalambros,et al.  On the Suitability of Econometric Demand Models in Design for Market Systems , 2010 .

[30]  Kate S. Whitefoot,et al.  Consequential Life Cycle Assessment With Market‐Driven Design , 2011 .

[31]  D. McFadden.  Conditional logit analysis of qualitative choice behavior , 1972 .

[32]  Adamantios Diamantopoulos,et al.  Quantitative Marketing and Marketing Management , 2012 .

[33]  Georg Bühler,et al.  The impact of fuel availability on demand for alternative-fuel vehicles , 2012 .

[34]  William Rand,et al.  Agent-Based Modeling in Marketing: Guidelines for Rigor , 2011 .

[35]  Vijay Mahajan,et al.  New Product Diffusion Models in Marketing: A Review and Directions for Research: , 1990 .

[36]  Moshe Ben-Akiva,et al.  Discrete Choice Analysis: Theory and Application to Travel Demand , 1985 .

[37]  Jeroen Struben,et al.  Identifying Challenges for Sustained Adoption of Alternative Fuel Vehicles and Infrastructure , 2006 .

[38]  Shunsuke Managi,et al.  Willingness-to-pay for infrastructure investments for alternative fuel vehicles , 2013 .

[39]  John R. Hauser,et al.  Premarket Forecasting of Really-New Products , 1996 .

[40]  Ryuichi Kitamura,et al.  Demand for clean-fuel vehicles in California: A discrete-choice stated preference pilot project , 1993 .

[41]  Martin Achtnicht,et al.  Impact of Service Station Networks on Purchase Decisions of Alternative-Fuel Vehicles , 2008 .

[42]  Jeremy J. Michalek,et al.  A Study of Fuel Efficiency and Emission Policy Impact on Optimal Vehicle Design Decisions , 2004 .

[43]  Peter Mock,et al.  Entwicklung eines Szenariomodells zur Simulation der zukünftigen Marktanteile und CO2-Emissionen von Kraftfahrzeugen (VECTOR21) , 2010 .

[44]  J. G. Wissema.  Trends in Technology Forecasting : R&D Management , 1987 .

[45]  Frank M. Bass,et al.  A New Product Growth for Model Consumer Durables , 2004, Manag. Sci..

[46]  Eckehard Schnieder,et al.  Impact assessment in the automotive industry: mandatory market introduction of alternative powertrain technologies , 2010 .