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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
Contact
http://www.mat.minesparis.psl.eu/formation/doctorat/propositions-de-sujets-de-these/
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
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