What if manufacturers could forecast and optimize every aspect of battery cell production costs before setting foot in the factory? This model was developed using insights from 14 projects within the ProZell competence cluster, gathered through an extensive survey and parameterization phase. It is adaptable to various cell chemistries, including solid-state batteries (SSB) and supports flexible configurations for both wound and stacked cells. These reference standards give the model robustness and flexibility for diverse industry applications. Stakeholders can apply it across multiple production scenarios and adapt to evolving technological demands and production goals.
Recent updates enhance the model’s relevance by incorporating specialized process routes for newer chemistries such as LFP and polymer SSB.
The model operates through two main calculations: anterograde material flow and retrograde value flow. The anterograde process uses backward calculations to determine the material inputs needed at each stage to achieve the final production targets. Since material losses occur at various production stages, this approach estimates the additional material required at each production step to compensate for losses, beginning with the annual production volume goal and adjusting inputs to ensure targets are met.
In the retrograde process, costs for each production step are calculated once facility sizing has been determined through the anterograde material flow. This calculation considers seven primary cost factors:
Direct material costs (based on the quantities and prices of raw materials), direct personnel costs (reflecting labor needs at each production step) and personnel overhead which includes additional staffing for management and cleaning.
Direct energy costs are calculated from the energy consumed by production machinery, while energy overhead captures facility heating costs.
Maintenance costs (estimated as a percentage of the initial equipment investment) and investment costs, which account for facility space requirements (e.g., dry rooms, labs) and ongoing equipment expenses. Collectively, these factors allow the model to compute full, levelized and marginal costs, delivering a comprehensive picture of production expenses.
With these cost projection capabilities, the model serves as a valuable tool for long-term production planning. It offers stakeholders the insights needed to evaluate production strategies and respond effectively to market dynamics. Ultimately, this model supports battery manufacturers in aligning production costs with emerging technologies and shifting market demands.