The proposed internship will focus on the study of innovative approaches based on Artificial
Intelligence (AI) methods to estimate the Remaining Useful Lifetime (RUL) of Li-ion batteries used in
Energy Storage Systems and Electric Vehicles.
Main work of this internship will consist of:

• Analyze battery measurements in real operating conditions,
• Manage and clean data (incorrect data, incomplete data, duplicates, etc…),
• Test statistical/machine learning approaches to select the relevant features [1],
• Test different machine learning approaches to diagnose SoH/RUL [1,2,3,4],
• Define and use metrics to qualify / quantify Key Performance Indicators,
• Perform SoH/RUL forecasts according to different usage profiles.
  Moreover, the results obtained by these new methods will be compared to the semi-empirical
  approach currently used by PowerUp.
  Finally, if it makes sense, a coupling of the developed AI method(s) with the current semi-empirical
  approach will be explored.

Few references:
[1] X. Shu, S. Shen, J. Shen, Y. Zhang, G. Li, Z. Chen, and Y. Liu, “State of health prediction of lithium-ion batteries based on
machine learning: Advances and perspectives,” iScience,
vol.24,no.11,p.103265,2021.[Online].Available: https://www.sciencedirect.com/science/article/pii/S2589004221012347
[2] D. Yang, X. Zhang, R. Pan, Y. Wang, and Z. Chen, “A novel gaussian process regression model for state-of-health estimation
of lithium-ion battery using charging curve,” Journal of PowerSources,vol.384,pp.387–
395,2018.[Online].Available: https://www.sciencedirect.com/science/article/pii/S0378775318302398
[3] K. Liu, Y. Li, X. Hu, M. Lucu and W. D. Widanage, “Gaussian Process Regression With Automatic Relevance Determination
Kernel for Calendar Aging Prediction of Lithium-Ion Batteries,” in IEEE Transactions on Industrial Informatics, vol. 16, no. 6,
pp. 3767-3777, June 2020, doi: 10.1109/TII.2019.2941747.
[4] L. Li, Y. Peng, Y. Song and D. Liu, “Lithium-Ion Battery Remaining Useful Life Prognostics Using Data-Driven Deep Learning
Algorithm,” 2018 Prognostics and System Health Management Conference (PHM-Chongqing), 2018, pp. 1094-1100, doi:
10.1109/PHM-Chongqing.2018.00193.

• Data scientist (machine learning, statistical analyses, data preparation, databases,
Python/Matlab, …)
• Energy storage or electrical engineering

Manager + team