Collaboration Project
Modeling and estimation of lithium-ion batteries with flat output current voltage
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].

However, most of these approaches are a priori not well suited for lithium-ion cell technologies with flat open
circuit voltage (OCV) versus state of charge curves (quasi-constant OCV curve), and therefore pose a major
problem for the observability of the induced model [4,5]. Batteries with flat OCVs might play an important role in
electric vehicles in the future. To overcome this observability problem and to favor the spreading of batteries with
flat OCVs in a near future, our idea is to temporarily exploit additional measurements, such as the charge state
given by coulometry. This type of measurement becomes inaccurate after a while, so the question is when not to
trust it anymore.

[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] S. Torai, et al., Journal of Power Sources 306 (2016),
[5] M. Berecibar, et al., Energy 103 (2016),
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