For the utilization of batteries in electric vehicles understanding and prediction of ageing mechanisms and the state of health of the batteries is vitally important.
Lithium plating is an ageing mechanism that has been investigated at the chair using test-cells, electrical measurements and post mortem analysis with a focused-ion beam and XPS. In the course of the OSLiB project methods will be developed to detect varying safety risks in LIBs during operation and the chair EET will focus on the in-operando detection of lithium plating.
With advanced ageing of a battery drastic increases of the ageing rate can occur that can lead to premature loss of driving range in an electrical vehicle. This point is often called knee point. To be able to react to this effect in time a prediction of the onset is necessary. Over the course of the FeBaL project the chair EET will investigate this behavior in different cell-technologies and develop a method to predict the onset of the knee point based on the prior aging history of the battery. The developed method will be integrated into the “battery neural network” also developed during this project.