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.
Decision support in the planning of battery production starts with the customer and production planner defining the desired FPPs/target FPPs that are used by the quality prediction model and battery production design to generate potential IPFs that are needed to produce a battery cell with desired FPPs (see Fig. 7 ).
The new manufacturing technologies such as high-efficiency mixing, solvent-free deposition, and fast formation could be the key to achieve this target. Besides the upgrading of battery materials, the potential of increasing the energy density from the manufacturing end starts to make an impact.
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.
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.
However, the potential for battery integration technology has not been depleted. Increasing the size and capacity of the cells could promote the energy density of the battery system, such as Tesla 4680 cylindrical cells and BMW 120 Ah prismatic cells.
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