With the rapid growth of electric mobility and stationary energy storage solutions, accurately assessing a battery’s condition has become a major challenge. Performance, safety, lifespan and reuse all depend on having access to fast and reliable battery diagnostics.
This is the challenge addressed by DIAGBAT, a research and development project bringing together CRITT M2A and LGI2A, an artificial intelligence research laboratory.
Improving Battery State-of-Health Assessment
Launched on 1 November 2025 and endorsed by the i-Trans competitiveness cluster, the DIAGBAT project aims to develop new methods for the fast, accurate and robust assessment of battery State of Health (SOH).
Existing battery diagnostic methods can require lengthy testing or provide only a partial picture of a battery’s actual condition.
DIAGBAT therefore seeks to address a key industrial challenge: how can a battery’s actual condition and performance be assessed faster and more accurately for industrial applications?
Combining Physics-Based Models, Artificial Intelligence and Experimental Data
To address this challenge, DIAGBAT combines physics-based models, artificial intelligence and experimental data.
By bringing these approaches together, the project aims to develop diagnostic methods capable of providing a more accurate understanding of a battery’s actual condition while reducing the time required for its characterisation.
In the long term, this work could help to:
- optimise battery performance and use;
- anticipate potential failures and degradation;
- improve system safety;
- better assess battery lifespan and State of Health;
- facilitate battery second-life, reuse and recovery strategies.
First Experimental Tests Underway
Since the launch of DIAGBAT, the teams have completed a state-of-the-art review of existing battery diagnostic methods to identify current approaches, their performance and their limitations.
The project is now entering a new phase, with the first experimental tests underway. The data collected will support the modelling and artificial intelligence work carried out as part of the project.
A dedicated Project Manager will also join the team at the end of September to support the next stages of DIAGBAT and the continuation of the project’s research activities.
CRITT M2A Supporting Innovation in Battery Technologies
Through DIAGBAT, CRITT M2A continues to strengthen its R&D activities in battery technologies and electromobility, combining its expertise in testing and characterisation with innovative diagnostic approaches.
The objective is to contribute to a better understanding of battery behaviour and ageing while helping industrial players address new challenges related to performance, reliability, safety and sustainability.
Stay tuned for the next DIAGBAT updates and the upcoming stages of the project.
The DIAGBAT project is co-funded by the European Union.
