BALd – Accelerated ageing tests and lifetime predictions

In the Battery Utilisation Concepts competence cluster under the BMBF umbrella concept of the Battery Research Factory, the aim of the BALd project is to accelerate the evaluation of lithium-ion battery (LIB) lifetime prediction for both existing and future technologies. LIBs already achieve such long lifetimes today that classical tests to determine their lifetimes can take years. In this project, concepts are being developed that make it possible to make forecasts for the service life after just a few weeks. The aim is to provide a rapid feedback for the development process and a considerable acceleration of the latter. Accelerated ageing tests serve as a basis, which on the one hand are accelerated by increasing stress factors (temperature, pause times, cycle depth, etc.) and on the other hand provide early results through the precise examination of ageing indicators. Concepts are developed and measurements carried out by various outstanding research institutes in Germany in close cooperation. Partners include the Institute for Thermal Process Engineering at the KIT in Karlsruhe, the Ingolstadt University of Technology with the Electromobility and Adaptive Systems Research Group, the Technical University of Munich with the Chair of Electrical Energy Storage Technology and the Chair of Automotive Engineering, the Centre for Solar Energy and Hydrogen Research with the Accumulators Group, as well as RWTH Aachen University with the Institute for Power Electronics and Electrical Drives and the Institute for Statistics and Business Mathematics.

Key points in the project are:

  • The exact detection of undesired side reactions by float current measurements and high-precision coulombmetry
  • The development of so-called step-stress experiments to shorten the test duration
  • Understanding the additional ageing effects in the lithium ion cell caused by increased pressure and temperature
  • Building an automated evaluation and modelling platform that allows the application of machine learning

All data are brought together in this project and result in an “electronic cell passport”, which documents all technical capabilities of a cell. The rapid prognosis allows very direct feedback to production. This increases the speed of LIB development and thus significantly advances cell production in Germany and the Battery Research Factory.

© BALd


RWTH Aachen University

Institute for Power Electronics and Electrical Drives (ISEA)

Chair for Electrochemical Energy Conversion and Storage Systems

Prof. Dr. rer. nat. Dirk Uwe Sauer
Tel.: 0241/80 96977

Jägerstr. 17-19
D-52066 Aachen

Project duration

01.12.2020 – 30.11.2023

Involved partners