Engineering Internship - Estimation of Lithium-Ion Battery Ageing Using Electrochemical Models

Engineering Internship - Estimation of Lithium-Ion Battery Ageing Using Electrochemical Models

The global demand for electrochemical storage systems is increasing, mostly due to the rise of hybrid and electric vehicles (Hybrid-Electric Vehicle, Plug-in Hybrid Electric Vehicle, and Battery-Electric Vehicle) and the growing energy storage market linked to renewable energies and grid management. SAFT is a major player in this field, producing lithium-ion batteries in Poitiers, Nersac, and Bordeaux. This internship, funded by SAFT, will be conducted at CRAN in Vandœuvre-lès-Nancy.

Subject Description:

Lithium-ion batteries are widely used in everyday applications such as laptops and mobile phones. They provide several advantages including high specific energy, high specific power, low self-discharge, and no memory effect. However, they require a Battery Management System (BMS) to ensure safety and prevent premature ageing.

The BMS plays a key role in performance and lifetime by relying on accurate knowledge of the internal state of the battery. Unfortunately, only a few variables are directly measurable: current, voltage, and sometimes temperature. To estimate internal states (state of charge, state of health, functional states), a mathematical model of battery dynamics is developed, on which an observer is designed. Several approaches have been developed, particularly by CRAN, GREEN, and SAFT, based on local electrochemical models and nonlinear observers.

The aim is to design and numerically validate estimation algorithms (observers) for the lithium quantity; a variable closely linked to battery ageing. The work will rely on reduced-order electrochemical models formulated as nonlinear ODEs. These models often remove one state variable using the classical assumption that total lithium quantity remains constant. This assumption breaks down over long-time horizons.

The main challenge is to remove this assumption and explicitly estimate this slow variable.

Plan

1. Literature review and selection of one or several estimation methods.
2. Study of the estimation methods using MATLAB-Simulink on a given model.
3. Validation in MATLAB-Simulink using experimental data.

Master’s or engineering school final-year student in control engineering or electrical engineering. MATLAB skills expected and good command of English.

Duration

5 to 6 months, starting between February 1st and March 31st, 2026.

Keywords

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