CIFRE PhD – IMS / Kurybees
Rapid and Non-Intrusive Diagnosis of the Physical Causes of Li-ion Battery Aging: Application to Performance and Reliability Prediction in Second-Life Use
The energy transition and the increasing electrification of transportation are driving a massive demand for high-performance and sustainable energy storage systems. In this context, lithium-ion batteries have emerged as the reference technology. However, given the scarcity of critical materials and the environmental impact of production, battery refurbishment and second-life reuse have become essential strategies to extend their service life and reduce their carbon footprint.
Current diagnostic methods have been developed for the automotive sector, targeting high-capacity cells with significant market value. They rely on long, costly protocols and require extensive experimental resources, suitable for industrial environments where safety can be controlled by design. However, these approaches are not directly applicable to small cylindrical cells with low unit value, originating from diverse applications such as light mobility or residential storage. They do not account for the variety of usage profiles, fail to provide a rapid and reliable estimation of remaining lifetime, and remain too expensive for mass diagnostics. In this context, developing fast, non-intrusive, and cost-effective methods represents a key challenge to accelerate the refurbishment of these cells while maintaining adequate safety levels.
The objective of this PhD is to design an innovative diagnostic and prognostic method for lithium-ion batteries. The method should enable the identification of the physical aging mechanisms related to usage history, assess the suitability of cells for a second-life application, and predict their performance and reliability in a new use. The work will involve developing a reduced-order numerical model, combining fast pulse- or frequency-based testing with multi-physics simulation, to estimate the state of health and remaining lifetime of the cells. The approach will rely on correlating experimental indicators with numerical models to provide a rapid, reliable, and industrially scalable diagnostic method.
The PhD will be conducted as a joint supervision between the IMS Bordeaux laboratory (UMR CNRS 5218) and the company Kurybees. The Reliability Group at IMS has recognized expertise in aging modeling, characterization, and monitoring of energy storage systems. It possesses state-of-the-art equipment within the CACYSSÉE platform, dedicated to electrochemical characterization and cycling of Li-ion batteries. On its side, Kurybees, recognized as a Young Innovative Company by the French Ministry of Higher Education and Research, will provide its unique experimental database (over 20 technologies tested and 1,000 cells per year) as well as its 100 m² testing laboratory in Écquevilly, equipped with six climatic chambers, 112 cycling channels, and advanced numerical simulation facilities.
The candidate should hold an engineering degree or a Master’s degree (Master 2) in electrochemistry, energy, applied physics, or numerical modeling. They should have solid knowledge of lithium-ion batteries and the processing of experimental data, as well as a strong interest in applied research, simulation, and the analysis of physical phenomena. The PhD will offer a stimulating work environment at the interface between academic research and industrial innovation, with concrete opportunities for scientific and practical valorization.
The PhD will begin in early 2026 under a three-year CIFRE contract, in collaboration between the IMS laboratory (Bordeaux) and the company Kurybees (Écquevilly, Yvelines).
For any additional information, candidates may contact Issam Baghdadi (issam.baghdadi@kurybees.com) and Olivier Briat (olivier.briat@ims-bordeaux.fr)
