This paper presented an approach for battery production design based on a machine learning model for the determination of IPFs in order to obtain desired FPPs of lithium-ion battery cells.
Battery cell production is a crucial part of the value chain, accounting for 46 % of value-creation and macroeconomic opportunities by 2030. 2 The production process chain consists of multiple interconnected process steps with a large number of parameters that can influence the final cell characteristics.
Figure 1 introduces the current state-of-the-art battery manufacturing process, which includes three major parts: electrode preparation, cell assembly, and battery electrochemistry activation. First, the active material (AM), conductive additive, and binder are mixed to form a uniform slurry with the solvent.
Battery production design is deployed with a connection to the quality prediction model. Furthermore, a production process simulation is used to predict PPs based on IPFs derived from battery production design. Fig. 7. Decision support in planning and operation of battery production.
The main activities developing battery domain ontologies today utilize the elementary multiperspective material ontology (EMMO) as the top-level ontology. The EMMO is a multidisciplinary top- and middle level ontological framework for applied sciences and engineering.
Two battery applications driving demand growth are electric vehicles and stationary forms of energy storage. Consequently, established battery production networks are increasingly intersecting with – and being transformed by – actors and strategies in the transport and power sectors, in ways that are important to understand.