Lancement de la Chaire industrielle ANR TOPAZE


Je dcouvre l'vnement "BATMAN" sur Fortnite ! (Pack Batman + Gotham City)


Bio-based aerogels: new eco-friendly porous materials for thermal insulation and controlled release




La chaire industrielle ANR DIGIMU

+ Toutes les vidéos


Data-driven learning of a battery ageing model for electric vehicles

Thesis proposal

Area of expertiseMechanics
Doctoral SchoolISMME - Systems Engineering, Materials, Mechanics, Energy
SupervisorM. Pierre KERFRIDEN
Co-supervisorM. David RYCKELYNCK
Research unitCentre of materials
Starting dateOctober 2nd 2023
KeywordsIA, Big Data, system modeling, battery health, electric cars
AbstractIn the context of new forms of mobility, knowledge of battery health is a major factor in the electric vehicle ecosystem. Future regulations will impose new health status criteria on automakers, to guarantee controlled ageing at the end of a kilometre and time threshold. The aim of this thesis is to accurately estimate and predict these health criteria using telemetry data from vehicles on the road. Advanced numerical approaches (multiphysics domain, multifrequency, AI on BigData...) will be used during the modeling phases. The predicted states of health will be validated by on-board diagnostics on different vehicles.
ProfileEngineer and / or Master of Science - Good level of general and scientific culture. Good level of knowledge of French (B2 level in french is required) and English. (B2 level in english is required) Good analytical, synthesis, innovation and communication skills. Qualities of adaptability and creativity. Teaching skills. Motivation for research activity. Coherent professional project.

- Extensive knowledge of artificial intelligence
- Mastery of neural networks (recurrent and convolutional in particular)
- Mastery of statistics and probabilities, non-convex optimization algorithms, linear algebra
- Knowledge of computation on differentiable computational graphs and high-performance algorithms for AI
- Knowledge of Big Data, and the challenges of heterogeneous, multi-domain and/or missing data
- Knowledge of systems modeling and control, data assimilation (e.g. Kalman filters)
- Proficiency in Python

Applicants should supply the following :
• a detailed resume
• a copy of the identity card or passport
• a covering letter explaining the applicant’s motivation for the position
• detailed exam results
• two references : the name and contact details of at least two people who could be contacted
• to provide an appreciation of the candidate
• Your notes of M1, M2
• level of English equivalent TOEIC
to be sent to recrutement_these@mat.mines-paristech.fr and pierre.kerfriden@minesparis.psl.eu
FundingConvention CIFRE

- MINES ParisTech


A new version of the  COLD SPRAY CLUB website is now online!

A new version of the COLD SPRAY CLUB website is now… The COLD SPRAY CLUB concerns laboratories, technology…
> En savoir +

A MINES ParisTech PhD student awarded at the International Symposium on « High-Temperature Corrosion and Protection of Materials »

Formation A MINES ParisTech PhD student awarded at the… Josiane Nguejio, PhD student at Centre des Matériaux…
> En savoir +

The FEMS Lecturers 2014-2015 include Henry PROUDHON

Recherche The FEMS Lecturers 2014-2015 include Henry PROUDHON Lecturer Series This is a scheme which sponsors selected…
> En savoir +

award for a team of Centre des Matériaux

Recherche award for a team of Centre des Matériaux Nicolas Gueninchault who work in teams M2 and COCAS with Henry…
> En savoir +

Recherche The SF2M award three medals to doctors of the centre des… The medal Réaumur is given by  SF2M, each two…
> En savoir +

+ More articles


+ More events

Plan du site
Mentions légales efil.fr © 2014 MINES ParisTech