18/06/2018

ALISTORE ERI funded PhD thesis: Battery lifetime prediction by Artificial Intelligence/Machine Learning

This job offer has expired


  • ORGANISATION/COMPANY
    Laboratoire de Réactivité et Chimie des Solides - UMR CNRS 7314; Université de Picardie Jules Verne
  • RESEARCH FIELD
    ChemistryPhysical chemistry
    Computer scienceDatabase management
    Computer scienceProgramming
    MathematicsApplied mathematics
  • RESEARCHER PROFILE
    First Stage Researcher (R1)
  • APPLICATION DEADLINE
    30/11/2018 00:00 - Europe/Brussels
  • LOCATION
    France › Amiens
    Sweden › Goteborg
  • TYPE OF CONTRACT
    Temporary
  • JOB STATUS
    Full-time
  • HOURS PER WEEK
    35
  • OFFER STARTING DATE
    01/10/2018
  • REFERENCE NUMBER
    Funded by ALISTORE European Research Institute

The lithium-ion battery (LIB) technology has reached a very high energy density at beginning-of-life (BOL), but with the significant life-times (≈ 10–15 years) targeted for EVs and stationary applications, the prediction of a chosen end-of-life (EOL) is of uttermost importance – regardless of the exact LIB chemistry. The design of life-time predictors, appropriate health evaluators, and not the least support to strategies to mitigate the ageing, constitute a very challenging task in view of the multiple materials degradation mechanisms of both shelf-life and cycle-life. The electrolyte composition, the active materials’ chemistry, the binders and additives of the electrodes, the fabrication process and the operating conditions – all effect the ageing. As the degradation process in general starts very slowly, the collection of degradation data is a time-consuming and costly task, why accelerated ageing protocols are implemented, but always questioned for representativeness.

This PhD thesis aims at developing and demonstrating a machine learning (ML) based computational platform, ultimately able to predict the battery life-time as function of a wide spectrum of cell properties and operation conditions. In the first stage it will be limited to LIBs, but employing a wide spectrum of chemistries, for which extensive life-time databases already exist. In the second stage one of the most promising next generation battery technologies, the sodium-ion battery (SIB), will be targeted. The student concrete deliverables of this PhD will be a set of ML computer codes predicting life-time, the predicted life-time data itself, as well as a collaborative space in the cloud to share data, results and codes with all the academic and industry club members of the ALISTORE European Research Institute which is funding this project.     

The work will mainly be carried out at the Laboratoire de Réactivité et Chimie des Solides (LRCS) at Université de Picardie Jules Verne in Amiens, France, with regular 3 months stays at the Department of Physics at the Chalmers University of Technology in Göteborg, Sweden (every 9 months).

The candidate should have a background in computational science, applied mathematics, programming and knowledge in chemistry and materials science. Knowledge on artificial intelligence, machine learning, data mining and battery field will be a plus. Excellent English both written and spoken is mandatory. The candidate should be highly dynamic, autonomous, mobile and with great team spirit.

If you are interested, please send your CV, motivation letter and the contact details of 4 referees to Prof. Alejandro A. Franco (alejandro.franco@u-picardie.fr) and Prof. Patrik Johansson (patrik.johansson@chalmers.se).

Additional comments

ALISTORE European Research Institute is a federative research structure coordinated by CNRS, funded though academic member contributions and the membership fees of several that joined its associated Industrial Club. Such funds allow developing hand in hand, integrated and collaborative research oriented towards medium-term transfer to industry. More information of ALISTORE ERI and its members here: http://www.alistore.eu/ 

Required Research Experiences

  • RESEARCH FIELD
    Computer scienceProgramming
  • YEARS OF RESEARCH EXPERIENCE
    1 - 4

Offer Requirements

  • REQUIRED EDUCATION LEVEL
    Computer science: Master Degree or equivalent
    Mathematics: Master Degree or equivalent
    Chemistry: Master Degree or equivalent
    Physics: Master Degree or equivalent
  • REQUIRED LANGUAGES
    ENGLISH: Excellent

Skills/Qualifications

The candidate should have a background in computational science, applied mathematics, programming and knowledge in chemistry and materials science. Knowledge on artificial intelligence, machine learning, data mining and battery field will be a plus. Excellent English both written and spoken is mandatory. The candidate should be highly dynamic, autonomous, mobile and with great team spirit.

Work location(s)
1 position(s) available at
Laboratoire de Réactivité et Chimie des Solides - UMR CNRS 7314; Université de Picardie Jules Verne
France
Amiens
80000
HUB de l'Energie, Rue Baudelocque
1 position(s) available at
Chalmers University of Technology
Sweden
Goteborg

EURAXESS offer ID: 316696