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The end-of-life event of the battery system of an electric vehicle is defined by a fixed end-of-life threshold value. However, this kind of end-of-life threshold does not capture the application and battery characteristics and, consequently, it has a low accuracy in describing the real end-of-life event. This paper proposes a systematic methodology to determine the end-of-life threshold that describes accurately the end-of-life event. The proposed methodology can be divided into three phases. In the first phase, the health indicators that represent the aging behavior of the battery are defined. In the second phase, the application specifications and battery characteristics are evaluated to generate the end-of-life criteria. Finally, in the third phase, the simulation environment used to calculate the end-of-life threshold is designed. In this third phase, the electric-thermal behavior of the battery at different aging conditions is simulated using an electro-thermal equivalent circuit model. The proposed methodology is applied to a high-energy electric vehicle application and to a high-power electric vehicle application. The stated hypotheses and the calculated end-of-life threshold of the high-energy application are empirically validated. The study shows that commonly assumed 80 or 70% EOL thresholds could lead to mayor under or over lifespan estimations. The iModBatt project has received funding from the European Union’s Horizon 2020 Programme for research and innovation under Grant Agreement No. 770054.

The presented investigation analysed the environmental impact of introducing electric cargo vehicles into logistics concepts for last mile delivery of parcels and groceries in urban areas. In order to evaluate the environmental impact of substituting an electric cargo vehicle as e.g. an electric light duty vehicle (LDV), a batterv-electric passenger car (BEV) or an e-cargobike for a diesel light duty vehicle, a comparison based on a simplified Life Cycle Analysis (LCA) was conducted. Data from existing LCA studies of vehicles were combined with information on the actual transport service, i.e. the average cargo weight of vehicles. The expected change of the four chosen indicators cumulated primary energy consumption (for energy efficiency), CO2 equivalents (for climate impact), NOx and PM (for air quality) and noise level was estimated. Results show that application of all investigated electric cargo vehicles has a potential to reduce the environmental impact of the logistics chain and that application of e-cargobikes has an especially high reduction potential.