## 2 Materials and Methods
Moreover, besides EoL batteries returning from the market, we include two additional sources of batteries that directly enter recycling: firstly, a fixed share of production waste occurring at battery-production facilities, and secondly, pre-serial samples occurring at OEM's testing facilities. For both together, we define a constant share of 2% of the battery production, which is based on previously published data [34; 35]. While the share of production waste at the time of this study can be assumed to be higher, significant efforts to reduce the scrap rate at the LIB-production stage justify this assumption. Meanwhile, we assume that scrap occurring at the cell-production stage is also directly reused locally and is thus not considered as recycled material. Lastly, the key driver of the effectiveness of providing secondary raw material is the selection of the recycling method. In this case, the efficiency rates per raw material for the applied recycling methods are modelled as part of the simulation [1; 36; 37; 38]. The combination of a mechanical and a hydrometallurgical recycling process promises the highest efficiency rates to recover up to 99% for Ni and Co, 100% for Mn, and 94% for Li, which together correspond to the range of 90-100% found by [PERSON] et.al. [3; 36; 38]. The recycling yield in general, i.e., for all battery materials as discussed by [PERSON] et al., can reach up to 75% [39]. Meanwhile, several research projects and new recycling companies are using a mechanical-hydrometallurgical battery-recycling process. In addition to reaching higher recycling efficiencies, this process is characterized by a lower carbon footprint due to lower energy demand [40; 41; 42]. Therefore, this method is used as the preferred process in the simulation. In order to answer the research question, we obtain different results from the simulation:  
* The number of batteries entering recycling in GWh per cell chemistry and per year. * The amount of recovered material in metric tons in total per year and in tones for Co, Ni, Li, and Mn per year. * The projected demand for batteries in GWh and in tons per year. Regarding the analyzed scenarios, the study follows the approach taken by [PERSON] et al. of differentiating between four possible scenarios for premium OEMs using high-energy cell chemistries of LIBs [17]. The base scenario A assumes a LIB chemistry with Ni, Mn and Co in the proportion 6-2-2, called NMC622, as major technology in the early 2020s. The scenario includes NMC622 as the preferred chemistry today, with NMC811 being used in 2024/2025 and NMC9055 beginning to play a role from 2025 onwards. While being used as a baseline, it needs to be mentioned that Scenario A represents an ambitious uptake of novel battery cell chemistries before 2030. Therefore, other scenarios need to be taken into account, which describe less favorable adoption rates of new technologies. In this regard, the \"high Mn scenario B\" assumes that NMC9055 is substituted with high energy NMC (HE-NMC). Furthermore, the \"Co scenario C\" assumes that NMC622 remains the mainstream for a longer time and is replaced by NMC811 only in 2030. As the last alternative, the \"NCA scenario D\" assumes that NMC811 is substituted with a currently used NCA chemistry [43]. Together, these rollout scenarios for premium OEMs (different NMC and NCA chemistries) are combined with other expected cell technologies, e.g., LFP, LMNO, LMO, and LMnP [44]. The resulting battery-technology rollout is presented in Figure 2. Additionally, Table 1 provides a summary of the data sources used in the material-flow analysis. The main goal is to determine the break-even points of the annual material demand and secondary-material availability. For that, we define the following parameters.