MADAM4Life – Mechanical aging diagnostics with strain gauges, dilation, pressure, acoustics and modal analysis for lifetime prediction

The overall objective of the MADAM4Life project is to monitor different cell states such as state-of-charge (SoC) and aging processes in lithium-ion batteries (LIBs) in-operando using different non-invasive mechanical measurement methods. For round cells, monitoring of cell expansion (US, RWTH1) is considered. For pouch cells, experimental modal and ultrasonic diagnostics (TUM, ISC, RWTH2) are being investigated to monitor mechanical material parameters. Furthermore, the US plans to develop a measurement method for the internal pressure of the cell and to compare the measurements with results of the expansion. By combining this with other mechanical sensors, such as for 2D/3D dilation and via modal analyses, as already used today at Fraunhofer ISIT, ISC and TUM, a comprehensive understanding of the mechanical changes in the cell due to different aging processes will now be created, which does not exist today. To create a comprehensive database, ISIT is creating pouch cells of different cell chemistries and acquiring commercial round cells. Adapted aging tests will be performed on both cell geometries.
Detailed models of the considered cell geometries will be developed for an improved understanding of the dependence of ultrasound signal propagation/change on aging phenomena (TUM) and for the spatially resolved physical description of expansion, pressure and temperature change (US). These models will further support investigations of the transferability of the measurement methods to the respective other cell geometry.
From the mechanical measurement methods, novel diagnostic methods for battery management systems (BMS) are to be developed (RWTH2, ISC, TUM), which extend the current state determination in secondary battery storage systems such as LIBs by means of electrical quantities and temperature, to include mechanical-electrochemical interactions. In particular, a higher accuracy in the determination of the State-of-Health (SoH) shall be achieved and a data-based optimization of the lifetime prognosis shall be enabled.
An empirical aging model will be used to predict the battery cell lifetime based on expansion data (RWTH1). In addition, the long-term stability of the online measurement methods will be investigated.
The findings from MADAM4life will complement the accelerated non-invasive electrochemical aging investigations from focus B1 and B2 and help detect mechanical causes that are otherwise inaccessible due to missing structure-property relationships. These new mechanical measurement methods will also be used to provide a more comprehensive assessment of new cell technologies emerging from the research factory in order to actively support targeted new developments in the field of materials science and cell production, thereby creating a competitive advantage over other cell manufacturers.
In addition, mechanical-electrochemical diagnostics should enable the identification of alternative end-of-life (EoL) criteria (apart from 70-80% SoH) or criteria that signal the transition to non-linear aging. An alternative definition should make it possible to operate batteries beyond the previous EoL limit under constant load requirements as long as capacity loss continues to be linear. By deriving an alternative EoL criterion, 2nd life applications can be realized more reliably and the CO2 footprint can be significantly reduced due to the lifetime extension, thus conserving resources.
By adapting new signal processing methods, this data is implemented in the BMS and supports predictive control for optimized battery operation with constant load profiles. This would further extend the usability of the batteries to achieve the goal of a Green Battery.

© MADAM4Life


Fraunhofer Institute for Silicate Research ISC
Fraunhofer R&D Center for Electromobility Bavaria

Dr. Sarah Hartmann
Neunerplatz 2, 97082 Würzburg

Telefon: +49 (0)931 4100 244

Project duration

01.03.2021 – 29.02.2024

Involved partners

RWTH Aachen University
Technical University of Munich (TUM)
University of Stuttgart