Description du sujet de thèse

The main objective of this thesis is to design advanced diagnostic and prognostic tools for Li-ion batteries, adapted to real-world AC/DC grid conditions and integrable into real-time monitoring. The aim is to address three challenges:
1. Storage reliability in complex, multi-level architectures;
2. Degradation prediction under dynamic usage conditions;
3. Energy management system (EMS) optimization taking aging into account.
To this end, the thesis will propose hybrid approaches, combining:
• Parameterized physical equivalent models (e.g., R-C, Thevenin, simplified electrochemical),
• Optimization algorithms for extracting dynamic parameters,
• Supervised and unsupervised machine learning methods,
• And symbolic regression tools to produce interpretable and generalizable models.
The diagnosis will be based on common measurements (current, voltage, temperature), usable in real-world conditions, with the ultimate goal of embedded implementation.
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Research Work
1. Detailed Literature Review
• Li-ion battery aging mechanisms,
• Dynamic equivalent models (R-C, simplified electrochemical models),
• Classical and intelligent SoH/RUL estimation methods,
• Optimization techniques (PSO, evolutionary algorithms),
• Advanced machine learning methods (neural networks, random forest, LSTM),
• Recent applications of symbolic regression to energy modeling.
2. Development of Parameterized Physical Models
• Implementation of models using equivalent electrical circuits,
• Extraction of dynamic parameters from synthetic or semi-real data,
• Use of optimization algorithms (PSO, least squares, heuristics) for calibration.
3. Machine Learning and Symbolic Regression
• Construction of datasets (real or simulated),
• Estimation of SoH/RUL from field measurements using ML/DL models,
• Use of libraries such as PySR or Eureqa to generate explicit expressions.
4. System Simulation and Network Evaluation
• Integration of battery aging into a multi-port AC/DC converter simulation,
• Assessment of the impact of degradation on quality of service and energy efficiency,
• Proposal of operational indicators (SoH, CEP) for embedded management.
5. Towards Comprehensive Supervision
• Development of a prototype energy management strategy (EMS) integrating SoH/RUL,
• Preparation for integration into the DC Architect consortium's test platforms.
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Environment and Collaborations
The doctoral student will work in an interdisciplinary research environment, interacting with:
• LAAS-CNRS (Toulouse, primary supervisor),
• IMS (Bordeaux, additional supervisor),

The project is integrated into the DC Architect consortium (more than 16 electrical engineering laboratories), with regular technical exchanges, access to simulation platforms and experimental data, and participation in group workshops. The candidate will be required to have some mobility, both for regular progress meetings and related to supervision from two research teams in two laboratories.
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Required Qualifications
• Master's degree or engineering degree (EEA, energy, automation, AI),
• Good knowledge of storage systems, electrical networks, and power electronics is a plus for this role,
• Skills in modeling, optimization, and machine learning,
• Proficiency in Python (scikit-learn, PyTorch, PySR) and Matlab/Simulink,
• Keen interest in energy reliability and explainable AI. ________________________________________
Supervisors and Laboratory
• Main supervisor: Corinne ALONSO (LAAS-CNRS, Toulouse)
• Co-supervisor: Jean-Michel Vinassa (IMS, Bordeaux)
• Duration: 3 years – PhD start date: Fall 2025
• Funding: PEPR TASE project – France 2030
References
Références [1] Berecibar M. et al., Critical review of state of health estimation methods of Li-ion batteries for real applications, Renewable and Sustainable Energy Reviews, vol. 56, pp. 572–587, 2016. [2] Han D. et al., Improving the state-of-health estimation of lithium-ion batteries based on limited labeled data, Journal of Energy Storage, vol. 100, 2024. [3] Chen L. et al., State of health estimation of lithium-ion batteries based on equivalent circuit model and data-driven method, Journal of Energy Storage, vol. 73, 2023. [4] Qi Q. et al., Battery pack capacity estimation for electric vehicles based on enhanced machine learning and field data, Journal of Energy Chemistry, vol. 92, 2024. [5] Xiong R. et al., Battery State of Health Monitoring Methods: A Review, Journal of Power Sources, vol. 405, pp. 18–29, 2018. [6] Li G. et al., Equivalent circuit modeling and state-of-charge estimation of lithium titanate battery under low ambient pressure, Journal of Energy Storage, vol. 77, 2024. [7] Gaetani-Liseo M., Thèse de doctorat, Université Paul Sabatier, 2021. [8] Barré A. et al., A review on lithium-ion battery ageing mechanisms and estimations for automotive applications, Journal of Power Sources, vol. 241, 2013. [9) Maures M., « Modélisation des performances et du vieillissement des assemblages parallèles de cellules lithium-ion pour la détermination de l'état de santé et de la durée de vie des batteries », Thèse de doctorat, Université de Bordeaux, 2021.

The results of the work will be shared with the other partner members of the project.

Contexte de travail

The main objective of the DC-Architect project, included in the PEPR TASE of the France 2030 plan, is to design distribution networks capable of transporting energy in the form of direct current, at medium voltage. These networks are located between local micro-grids (often isolated or resilient) and large interconnected networks, which are also evolving towards DC infrastructures in certain cases.
The proposed thesis is therefore situated in the national context of the France 2030 recovery plan and in particular in the PEPR project named DC-Architect including more than 16 research laboratories of Electrical Engineering. This thus implies a regular presentation and discussion of the progress of the work with all the partners. The planned research work is in the field of the study of energy storage systems (ESS) in a harsh environment of conversion architectures such as distribution networks. With the rise of renewable energy, often connected via power electronic converters (PECs), distribution networks are becoming AC/DC hybrid, dynamic, and increasingly complex. These sources often lack inertia or are subject to weather conditions, making them difficult to control. In this context, ESSs, whether stationary or onboard (e.g., electric vehicles), play a key role by providing flexibility and stability.
However, these ESSs also represent a vulnerable area of the grid, as their reliability is poorly understood and their behavior can vary greatly depending on usage, temperature, converter configuration, etc. Their reliable integration therefore requires detailed monitoring of their state of health (SoH) and remaining lifetime (RUL).
The DC-Architect project also aims to simultaneously address the design issues of power conversion modules using the primary energy coefficient, which is an indicator that identifies energy loss between the time it is produced and the time it is consumed and accurately reflects the operational needs of the distribution network. The candidate will thus be required to fully understand the context of electrical grids and their evolution with the massive integration of converters. By the end of their thesis, they will be expected to propose potential solutions for combining converters with ESSs and their associated energy management systems (EMS), including SoH indicators and other monitoring methods.

https://www.pepr-tase.fr/projet/dc-architect/

Le poste se situe dans un secteur relevant de la protection du potentiel scientifique et technique (PPST), et nécessite donc, conformément à la réglementation, que votre arrivée soit autorisée par l'autorité compétente du MESR.

Contraintes et risques

The work will be carried out at the LAAS laboratories in Toulouse and IMS in Bordeaux on the experimental cycling sections and test benches. The environment requires a B2 electrical qualification.
Knowledge of electrical testing will be required.