Wansart, J.; Schnieder, E.:
Simulating market development of battery electric vehicles.
FOVUS - 5. Internationales Symposium "Networks for Mobility", Oktober 2010.
Worldwide, the automobile industry faces two intertwined trends, urging it to enhance and change traditional powertrain technologies. On the one hand, crude oil reserves are finite and the maximum of production capacity seems to have been reached. On the other hand, regulatory requirements force manufacturers to reduce their products’ CO2 emissions.
One option to cope with this problem is the electric powertrain that does not emit any emissions during operation. As long as driving energy is generated from renewable sources electric powertrains promise to be a large step forward to sustainable transportation. Basically two mobile energy storage systems can be used: batteries or hydrogen fuel cells. So far, compared to conventional internal combustion engines, none of those technologies seems to any competitive advantage today that could lead to significant market shares. Hence, the question arises how a substantial long-lasting market share of electric vehicles may be achieved.
Therefore, the goal of this contribution is to analyze market mechanisms that drive or block market diffusion of electric powertrain technologies in order to understand under which conditions those technologies can be introduced successfully into the market. A dynamic simulation model is developed that contains a view on socio-economic mechanisms of the market and their interrelationship with technology-related developments. Thus it is possible to develop consistent scenarios of market diffusion.
Modeling approach and simulation
Following the conceptual approach of the System Dynamics methodology developed by Forrester , a number of mechanisms have been identified as drivers of innovation diffusion . Basically, five reinforcing feedback effects appear to be important in the context of alternative powertrains. In Figure 1, these are illustrated by a causal loop diagram, where an arrow is directed from cause to effect.
Figure 1: Important feedback loops for innovation diffusion
This conceptualization of a dynamic model can be expressed as a system of differential equations which is implemented into standard simulation software. A number of simulation runs is analyzed. Especially sensitivity analysis is of high interest in the given context, in order to identify parameters with high leverage. One such very strong effect is WoM, see Table 1. A time-lag between awareness diffusion and the actual purchase decision can be expected. Without a certain strength of WoM the number of potential customers stays low and does not develop to a level necessary for self-sustaining market penetration. This is the case even in the presence of a reasonably successful supporting marketing strategy that is continued for a long time, here for 10 years. In the lower figure it becomes clear that a high market share can be achieved in the next twenty years, if customers’ attitude towards BEV is sufficiently positive, but, as mentioned earlier, this result is highly uncertain so far: The curves cover a span of almost fifty percentage points of market share.
Table 1: Sensitivity of BEV market share to word-of-mouth
Customer awareness and market share conditional to awareness
Exogenous diffusion effect:
ηEV = 0.2, duration = 10 years
Choice parameters: βPrice = -0.5, βRange = 0.5 Endogenous diffusion effect:
φBEV = 0.1 (pink), φBEV = 0.15 (violet),
φBEV = 0.2 (green), φBEV = 0.25 (red),
φBEV = 0.3 (blue)
To be summarized, there does not seem to be one single critical factor, but rather a combination of different factors that account for a successful implementation of new vehicle energy technologies. Further analyses are in progress in order to understand these effects more deeply.