###### Abstract
The earlier the development phase, the more the knowledge gaps have to be closed by scientific-technological models in order to back them up step by step with experimental data. This was the approach taken in the BET studies of [11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23] in order to provide predictions for consumption and GHG emissions. BETs and FCETs have been compared by the authors of [24; 25; 26; 27; 28; 29]. The literature also pays close attention to the cost side. Both the operating and lifecycle costs as well as the total costs of ownership (TCO) are analyzed in [14; 19; 25]. Figure 1: Share of BEVs and BETs (>16 t) among new registrations in their respective class (EU, 2023) [4]. Besides costs, the question of driving range also plays a significant role, especially with BETs. The authors of [11; 18; 20; 25; 30] give helpful support for finding suitable battery sizes according to the specified length of the transport route. Unfortunately, it is all too rare to find information on dimensioning tolerances so that the truck can be brought home safely even on hilly terrain and/or during the cold season. Using two examples from the scientific literature, we want to show that the lack of validated data for medium-duty road freight transport leaves a gap in our actual knowledge. Reference [29] is a meta-study that developed an economic model and arrived at results using consumption data from other studies. For both BETs and FCETs, large ranges in consumption are shown, in some cases without providing any comprehensible information on where these consumption data come from or which application/vehicle size they correspond to. Reference [15] is one of the few studies that provides precise consumption data for FCETs for medium-duty road freight transport. However, it also presents a meta-study that derived economic results from adopted consumption values. In contrast, a key feature of our work is to obtain consumption and emission values for BETs and FCETs that are as reliable as possible using application-specific, real-world data and a double-validated model. The particular challenge facing the current development of drive technology for medium and heavy goods transportation is therefore not so much the lack of scientifically based, differentiated models for trucks, freight, and tours; rather, it is the lack of opportunities to test the model results against reality. This resulting requirement was of particular importance for our work. In the absence of practical data, we postulated the need to verify the model in several stages. The basic technological assumptions of the model can be verified using the extensive database of passenger vehicles. In contrast, the transport-specific model assumptions regarding trucks, freight, and tours can only be compared with real data obtained with the existing ICEV technology. There is a lack of such sound, multi-stage model verifications in the literature. As a result, key questions remain partially or completely unanswered by the current literature:  
1. How does the FCET's longer chain of components affect consumption and GHG emissions? 2. How does the significantly higher weight of the BET affect consumption and GHG emissions? 3. How do real-world conditions (hilly terrain, cold seasons, non-optimal driver behavior, etc.) affect fuel consumption and GHG emissions? 4. How can emissions/consumption be predicted beyond the short-term perspective and limited as quantitatively as possible? 5. Which consumption shares of the physically acting forces influence the energy balance of the BET and FCET, and how? What conclusions can be drawn from this for the development of new truck drive technologies? It is precisely these scientific gaps and the resulting questions that provide the motivation for our present work. In our previous work, we investigated the realistic consumption and greenhouse gas (GHG) emissions of BEVs and FCEVs [5; 31].