FAT study: Loading and H2 infrastructure for trucks

Challenge

In order to counteract the high level of emissions both in cities and in long-distance traffic, the players in commercial transport are also increasingly called upon to use new forms of mobility. Battery-powered electric vehicles and vehicles with hydrogen technology are currently the most frequently discussed alternatives for locally low-emission freight transport.

Method

Against this background, the Fraunhofer IAO is working on the following on behalf of the Research Association for Automotive Technology (FAT) in the German Association of the Automotive Industry (VDA), the Fraunhofer IAO develops scenarios for the charging and H2 infrastructure of the future. The research team of Fraunhofer IAO considers different industry and user groups in order to be able to make specific statements about the future design and use of infrastructures and vehicles in commercial traffic. In the first step, the research team uses guideline-based interviews to determine the requirements for charging and refuelling infrastructures for the different industries. At the same time, the team is preparing a market and technology overview for charging stations and hydrogen infrastructure and illustrating possible business and operator models. On this basis, the research team develops site-specific scenarios for the year 2030, which are then used as a basis for a technical analysis of the charging infrastructure and energy system requirements. The analysis is carried out using the simulation models developed at Fraunhofer IAO for locally concentrated charging infrastructures.

Result

The results of the analysis will be presented and evaluated in a technical workshop at the end of the project duration. The effects of the scenarios on commercial transport will be discussed and published in the FAT publication series. The study, which will be published in the FAT publication series, will also include a market and technology overview as well as the presentation of possible business and operator models for charging infrastructures.