Abstract
The useful life and durability of battery systems strongly depend on the high and uniform quality of the manufactured battery cells. However, heterogeneous information infrastructures in manufacturing still limit the knowledge on cause and effect relationships between process and product quality parameters. Based on a literature review, the prospects of soft sensoring and sensor fusion in the data-driven modelling and prediction of product quality in battery cell manufacturing is analyzed. It is suggested that soft sensoring and sensor fusion is able to improve the accuracy of product quality models by overcoming the restrictions imposed by a limited number of hardware sensors. Besides, a general lack of data availability along the process chain in battery cell manufacturing is identified, which can be addressed by a new method to detect, correct and process deficits in battery cell manufacturing with missing hardware sensors.
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