Thèse CIFRE - Modèle vieillissement batterie via Machine Learning (F/H)

Job Title: Thèse CIFRE - Battery Aging Model via Machine Learning (F/H)

Company: Renault Group

Work Environment

Join Renault Group's collaborative project with the renowned Solid Reactivity and Chemistry Laboratory (LRCS) in Amiens, under the guidance of Prof. Alejandro FRANCO. This partnership aims to leverage cutting-edge multiphysical modeling research, supported by our advanced testing facilities and high-quality supervision. Regular progress meetings will be held monthly, either face-to-face or remotely, involving the doctoral student and advisors.

Job Description

As a doctoral candidate, you will engage in a range of research activities including:

• Updating the bibliography on Machine Learning models and their application in statistical approaches for model training and SOHE accuracy optimization.
• Mastering various post-mortem analysis techniques to identify and analyze defects.
• Improving and refining open-source Machine Learning models to predict battery health and lifespan with high precision.
• Developing empirical equations for integrating into the basic AMPERE aging model stored within the vehicle's BMS (Battery Management System).

Research Methods

You will use statistical methods to cover all use cases for training Machine Learning models, define precision levels for model calculations across different stages of aging, and employ electrochemical cycling and storage methods for aging cells until their end-of-life to correlate predictive model results.

Key Deliverables

• PhD thesis
• Patents
• Dissemination in technical committee AMV-C meetings
• Publications in peer-reviewed journals and conferences

Candidate Profile

Required knowledge:

• Solid grasp of electrochemistry, numerical and statistical modeling of electrochemical systems.
• Experience with numerical simulation methods such as Matlab, Comsol, Python, and familiarity with languages like Maple, Fortran.
• Proficiency in electrochemical analysis methods (Impedance Spectroscopy, charge/discharge cycling, etc.), solid-state chemistry, and physical analysis studies (X-Ray Diffraction, FT-IR Spectroscopy, Scanning Electron Microscopy, EDX).

Desired Education

General Engineer, Computer Science, Applied Mathematics, or Big Data Engineering degrees preferred.

Personal Skills

Autonomy, diligence, initiative, synthesis skills, and the ability to work collaboratively.

Job Family

Transverse

Contract Duration

36 months

Inclusion and Diversity at Renault Group

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