Prospective time-resolved LCA of fully electric supercap vehicles in Germany

The ongoing transition of the German electricity supply toward a higher share of renewable and sustainable energy sources, called Energiewende in German, has led to dynamic changes in the environmental impact of electricity over the last few years. Prominent scenario studies predict that comparable dynamics will continue in the coming decades, which will further improve the environmental performance of Germany's electricity supply. Life cycle assessment (LCA) is the methodology commonly used to evaluate environmental performance. Previous LCA studies on electric vehicles have shown that the electricity supply for the vehicles' operation is responsible for the major part of their environmental impact. The core question of this study is how the prospective dynamic development of the German electricity mix will affect the impact of electric vehicles operated in Germany and how LCA can be adapted to analyze this impact in a more robust manner. The previously suggested approach of time-resolved LCA, which is located between static and dynamic LCA, is used in this study and compared with several static approaches. Furthermore, the uncertainty issue associated with scenario studies is addressed in general and in relation to time-resolved LCA. Two scenario studies relevant to policy making have been selected, but a moderate number of modifications have been necessary to adapt the data to the requirements of a life cycle inventory. A potential, fully electric vehicle powered by a supercapacitor energy storage system is used as a generic example. The results show that substantial improvements in the environmental repercussions of the electricity supply and, consequentially, of electric vehicles will be achieved between 2020 and 2031 on the basis of the energy mixes predicted in both studies. This study concludes that although scenarios might not be able to predict the future, they should nonetheless be used as data sources in prospective LCA studies, because in many cases historic data appears to be unsuitable for providing realistic information on the future. The time-resolved LCA approach improves the assessment's robustness substantially, especially when nonlinear developments are foreseen in the future scenarios. This allows for a reduction of bias in LCA-based decision making. However, a deeper integration of time-resolved data in the life cycle inventory and the implementation of a more suitable software framework are desirable.

The study describes how life cycle assessment's (LCA) robustness can be improved by respecting prospective fluctuations, like the transition of the German electricity mix, in the modeling of the life cycle inventory. It presents a feasible and rather simple process to add time-resolved data to LCA. The study selects 2 different future scenarios from important German studies and processes their data systematically to make them compatible with the requirements of a life cycle inventory. The use of external scenarios as basis for future-oriented LCA is reflected critically. A case study on electric mobility is presented and used to compare historic, prospective static, and prospective time-resolved electricity mix modeling approaches. The case study emphasizes the benefits of time-resolved LCA in direct comparison with the currently used approaches.