The Support of Failure Analysis for the Development of Battery Simulation

Monday, 27 April 2026

The development of advanced batteries requires a deep understanding of failure mechanisms—both to effectively manage them and to accurately inform users when a critical state is reached.
At SERMA, we are developing predictive models using a methodology that combines physical analysis with data-driven approaches. In the first part of this work, we will present an overview of the degradation mechanisms affecting Li-ion batteries, supported by results obtained from aged cells using state-of-the-art characterization techniques. In the second part, we will describe our data-driven approach and explain how it is integrated with physical insights to enhance the accuracy of predictive models for estimating the State of Health (SOH) of Li-ion batteries.

Session Objectives:

• Failure mechanisms of Li-ion batteries
• The way to probe failure mechanisms in a complex system (such as a Li-ion cell)
• A methodology to provide a predictive model for SOH estimation