Method for quality parameter identification and classification in battery cell production quality planning of complex production chains for battery cells

This paper focuses on the identification of quality relevant process parameters in the production of high energy lithium-ion battery cells. Today there is still a high level of uncertainty about the effects of manufacturing processes on the quality of high energy lithium-ion cells-in industry as well as in research. Compared to consumer cells, high energy cells used for automotive applications or grid power supply are subject to much higher quality requirements, especially regarding life time and safety issues as well as different conditions of operation. Consequently, a quality planning approach for the identification of process-product-interactions in the field of high energy lithium-ion cell production is presented. The described methodology is applicable from early design stages to the ramp-up of lithium-ion cell production lines. On the one hand, knowledge about these correlations helps to estimate product quality in dependency of intermediate product properties and process parameters. On the other, required set points for the processes employed in cell manufacturing can be derived. Thus, results of the described methodology contribute to overcome existing challenges for equipment manufacturers, cell manufacturers as well as cell costumers. Therefore, the paper describes and characterizes the entire production chains of hard case cells and pouch cells partially implemented in the High-Energy Battery Cell Production Research Center of the Institute for Machine Tools and Industrial Management (iwb) ([1], [2]). The presented methodology for the identification of process-product-interactions consists of five steps. Step 1 is described in this paper, while step 2 to 5 are outlined. Based on a modified process Failure Mode and Effects Analysis (FMEA) step 1 collects in an unrestrained but oriented way possible failures in cell manufacturing and provides evaluated process-product-interactions. With this information, in step 2 process and material properties are classified employing complexity management methods. Based on the classification, production parameters with significant influence on the product quality features can be selected for the quantification by means of experiments.