Collaboration Project
From the lithium-ion cell to the battery: electrochemical modeling and state estimation
2022/02/01 - 2022/09/01
Electrochemical batteries are ubiquitous in our daily lives, including in our computers or our cell phones. Among
the various technologies available, lithium-ion batteries offer many advantages, particularly in terms of energy
mass, power mass and low self-discharge. In addition, they do not have a memory effect. On the other hand, this
type of batteries requires a management system (BMS) for safety reasons, but also to prevent premature aging.

The BMS plays a key role in the performance and lifespan of the battery, and it is essential to supply the BMS with
accurate data on the current state of the battery. The problem is that little information about battery variables is
directly accessible through measurements, typically the current, the voltage and possibly the temperature. To
access the battery states (state of charge, state of health, functioning state), a mathematical model of the battery
dynamics is usually developed, based on which an observer is designed to estimate the non-measurable internal
variables. Different approaches have been developed for this purpose, including some by CRAN, GREEN and SAFT,
based on local electrochemical models and implementing a nonlinear observer [1,2,3].

It appears that most of these approaches are dedicated to the estimation of the state of a single lithium-ion cell.
However, in practice, batteries are most often composed of several cells associated in series and/or in parallel. For
reasons of implementation and computation time, single-cell approaches cannot be simply duplicated when many
cells are interconnected. It is therefore necessary to develop estimation tools that are easy to implement and
adapted to multi-cell batteries. Few results are available in the literature on this subject, among which [4-6].

[1] P. Blondel, et al., IEEE Transactions on Control Systems Technology 27 (2) (2019) 889-897; doi:
[2] P. Blondel, et al., 20th World Congress of the International Federation of Automatic Control (IFAC 2017) 50 (1)
8127-8132, Toulouse, (2017); doi: 10.1016/j.ifacol.2017.08.1252
[3] E. Planté, et al., Submitted to IEEE Transactions on Control Systems Technology, 2021
[4] Y. Zhen, et al., Journal of Power Sources 383 (2018),
[5] Z. Wang, et al., Applied Energy 294 (2021),
[6] D. Zhang, et al., Submitted to IEEE Transactions on Control Systems Technology, 2021
Control engineering, batteries, modeling, observer, estimation, Lyapunov stability, Matlab-Simulink
This is a Master project for a student in control or electrical engineering. Matlab skills and good knowledge of the
English language are expected.

Feel free to contact Romain Postoyan (romain.postoyan AT for more information.
Control Identification Diagnosis