Internship - Development and Calibration of Battery aging models

Join us and be a pioneer for a green transport revolution.
Automotive Cells Company (ACC) is one of Europe’s newest and most exciting high-tech companies. Backed by Saft-Total, Stellantis-Opel and Mercedes-Benz, ACC is set to power the future of the automotive industry by innovating in battery technology.
 
We aim to produce sustainable, affordable, high-capacity, longer-life batteries as a cleaner alternative to current energy sources. We create an R&D center and new state-of-the art facility in France (New Aquitaine), with gigafactories to be built in Hauts de France and Germany. 
We need a range of skilled and agile people to bring our vision to life, especially in the areas of industrialization, mechanical design, testing/prototyping and any other function of a new-born company. If you’re looking to take your career further than you imagined, if you’re passionate about creating cleaner transport, we’d like to hear from you.
Accelerating sustainable mobility for all.

Durability of the battery pack is one of the primary issues in the expansion of the battery electric vehicles market. Indeed, the Li-ion batteries suffer parasitic electrochemical reactions over time and use, which alter their autonomy and their performances. Furthermore, some aging mechanisms can involve safety issues leading to the destruction of the battery in a very short time. Predicting the aging behavior of the battery is then a key point to optimize the lifespan of the electric vehicles.
Empirical models are widely used to estimate the aging behavior of a battery. However, these models lack physical consideration, which prevents the differentiation of multiple contributions to aging on the one hand, and the correlation of specific operating conditions to a specific aging mechanism on the other. The goal of this internship is to improve the understanding and the modelling of the aging phenomena to better predict the long-term performances of ACC cells. PyBaMM (Python Battery Mathematical Modelling) is an open-source battery simulation package written in Python which proposes a modelling tool at microscopical level. It gathers an active community which continuously brings insights and improvement in the field of battery modelling.

The trainee will be integrated in ACC’s expertise center in Bordeaux, working with the Cell management Simulation Team. The internship is composed of 2 main milestones:
·      State of the art of aging modelling in PyBaMM
o  Synthesis of the different aging models available in PyBaMM.
o  Understand and document how the aging models are mathematically formulated and numerically solved.
o  Identify the operating conditions that trigger the aging models.
·      Model calibration
o  Define the experimental data that are required to calibrate the models.
o  Propose a method to calibrate the models based on these experimental data.

• You are a student in the 2nd or last year of engineering school, or you are preparing a M1 or M2 degree in one of these fields: electrochemistry, energy, applied mathematics engineering or data analysis.
• You are proficient in algorithms, you like coding and are interested in physics modeling.
• Basics skills in Python is mandatory.
• Beyond your scientific skills, your rigor, open-mindedness, and curiosity will contribute to your success within ACC.