## 3 Results and Discussion
### Discussion  
In our earlier work [5; 31], we were able to verify the basic technological assumptions of our model for passenger cars and light trucks. The excellent agreement of the simulation results for ICET trucks with observed real-world data (Figure 8) means that the model extension to medium-duty trucks up to a total weight of 25 t can now also be regarded as verified. We emphasize that we did not optimize any one of the many parameters a posteriori to improve the agreement between simulation and measurement irrespective of physics and causality. The model does not perform a functional approximation but is based on physics and the technological state of the art and all of its parameters are given theoretically known, measured or physically plausible values prior to simulation. On this basis, we were now able to carry out simulations with BETs and FCETs and make physically objective predictions for the future up to 2050. The specific consumption of trucks depends on truck details, specific terrain profiles, climate, etc. ICETs are highly developed with a relatively stable state of the art. The situation is different for BETs and FCETs in that they are in an early phase of development, which is always associated with a highly time-variant state of the art. It was therefore all the more important for us to look for comparisons with other scientific studies that are as up to date as possible and originate from different countries. Table 12 shows such a comparison for truck weights around the weights we analyzed. Basically, taking into account differences in weight, application, and year, the values in the literature correspond well with our results. This applies to all three vehicle types  
Figure 12: Consumption shares in 20-ton trucks (long tour, 2024) (for details, see Figure S6 of [41]). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Data Source**} & \multirow{2}{*}{**Year**} & \multirow{2}{*}{**Truck Gross Weight (t)**} & \multicolumn{3}{c}{**Specific Consumption**} \\ \cline{4-5}  & & & **(kWh/100 km)** & \\ \cline{3-5}  & & & **BET** & **FCET** & **ICET-CNG** \\ \hline This work—Short Tour, 2024 & 2024 & 20 & 110 & 152 & 293 \\ \hline This work—Long Tour, 2024 & 2024 & 20 & 123 & 168 & 282 \\ \hline [PERSON] et al. [23] & 2018 & 40 & 123–194 & - & - \\ \hline [PERSON] et al. [14] & 2018 & 40 & 115–144 & - & 220–330 \\ \hline [PERSON]. [PERSON] et al. [29] & 2024 & 16–49 & 45–125 & 65–180 & - \\ \hline [PERSON] et al. [15] & 2022 & \textgreater{}13 & 140 & 210 & 300 \\ \hline [PERSON] et al. [13] & 2021 & 36 & 131 & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Comparison of the results with the published literature [13; 14; 15; 23; 29]. analyzed (BET, FCET, and ICET-CNG). This consistency represents comprehensive verification of our truck model. The model confirmation for passenger vehicles and light trucks by [31] has now been extended to medium freight transportation and trucks with a total weight of up to 25 t.  
As shown in Table 12, the largest deviation between our own and other results occur for Ref. [29], which covers a broad weight range of trucks in the Chinese market. The consumption figures stated in [29] are otherwise known from light trucks in regional delivery. As light trucks make up a substantial fraction in Chinese traffic, it can be assumed that lighter vehicles with a total weight below 16 t were included in a source not further specified in Section 2.3.2 of [29]. The consumption range given by [PERSON] et al. [23] begins at values we also found and extends to much greater values. Obviously, the effective weight of the trucks considered by [23] exceeded our values, reaching up to 40 t (full load). As the CO\({}_{2}\) factors for electricity vary significantly from country to country, there are limited relevant data for a scientific comparison of GHG emissions. Any further comparison of GHG emissions from FCETs involves large uncertainties as the hydrogen production chains are not yet established.