New automatic error detection process that is implemented at the AQua Battery Competence Cluster.
Lithium ion batteries are high energy density batteries used in a variety of applications like mobile phones, laptops, drones, electric vehicles, etc. These batteries are prone to degradation and issues like thermal runaway due to internal short circuits. For a high quality commercial lithium-ion battery, errors during the manufacturing process must be detected and corrected. For this, researchers from Karlsruhe Institute of Technology, along with the AQua battery research cluster are developing new approaches toward quality assurance and analytics in production of lithium-ion batteries.
The researchers aim to develop automatic error detection tools for detecting manufacturing at each stage. After detection, the process has to be optimized and automated to ensure a high-quality product at the end of the process. “In production, every step has to be flawless. All steps are designed to work together, and any error can affect the later performance of the cells,” says Professor Helmut Ehrenberg from the Institute for Applied Materials (IAM-ESS) of KIT, who coordinates the research.
The scientists at the AQua research center use the principle of Failure Mode and Effects Analysis (FMEA), a method used to evaluate the possibilities for failures, reasons for failure, and failure impacts on a process. The FMEA process reviews the steps in the process, failure modes, failure causes, and failure effects. Automatic error detection capability rejects any piece that is found defected , which allows it to draw conclusions about the causes of errors. Therefore, the manufacturing process can be optimized such that the errors are minimized and further costs due to rejections can be avoided.
Li-ion batteries malfunction during the operation. The malfunctions that can occur are internal short circuit, over-charge, over-discharge, or external such as sensor faults and cooling system faults. Those faults can be minimized by the automatic error detection during manufacturing.