Trainee Project
Title:
State of charge and parameters on-line estimation for lithium-ion batteries
Dates:
2023/02/01 - 2023/08/31
Supervisor(s):
Other supervisor(s):
RAEL Stéphane
Description:
Lithium-ion batteries offer many advantages in terms of volumetric capacity and weight. They also avoid the
famous memory effect dear to our first cell phones. On the other hand, this type of batteries requires a
management system for safety reasons, but also to prevent their premature aging.
A better management of lithium-ion batteries will necessarily involve a better knowledge of the internal states
and parameters. In this context, one approach consists in estimating these internal data by using a mathematical
model of the dynamics involved. Thus, the objective of this project is to contribute to the improvement of existing
management systems. The idea is to exploit control engineering techniques to estimate both the state of charge
but also its total capacity and its resistance, data inaccessible by measurement. We will thus have a better
knowledge of its state of aging.
The project will proceed according to the following steps.
Literature review of adaptive estimation techniques for electrical equivalent models.
Construction of adaptive observers to estimate both the state variables and the total capacity/resistance.
Implementation in Matlab-Simulink and comparison of the obtained observers: (i) when the other
parameters are constant; (ii) when they slowly vary with time.
References
M.S. Chong, D. Neić, R. Postoyan, L. Kuhlmann. Parameter and state estimation of nonlinear systems using a
multi-observer under the supervisory framework. IEEE Transactions on Automatic Control, 60(9), 2336-2349,
2015.
Q. Zhang. Adaptive observer for multiple-input-multiple-output (MIMO) linear time-varying systems. IEEE
transactions on automatic control, 47(3), 525-529, 2002.
Y. Zheng, M. Ouyang, X. Han, L. Lu, J. Li. Investigating the error sources of the online state of charge estimation
methods for lithium-ion batteries in electric vehicles. J. of Power Sources, 377, 161-188, 2018.
famous memory effect dear to our first cell phones. On the other hand, this type of batteries requires a
management system for safety reasons, but also to prevent their premature aging.
A better management of lithium-ion batteries will necessarily involve a better knowledge of the internal states
and parameters. In this context, one approach consists in estimating these internal data by using a mathematical
model of the dynamics involved. Thus, the objective of this project is to contribute to the improvement of existing
management systems. The idea is to exploit control engineering techniques to estimate both the state of charge
but also its total capacity and its resistance, data inaccessible by measurement. We will thus have a better
knowledge of its state of aging.
The project will proceed according to the following steps.
Literature review of adaptive estimation techniques for electrical equivalent models.
Construction of adaptive observers to estimate both the state variables and the total capacity/resistance.
Implementation in Matlab-Simulink and comparison of the obtained observers: (i) when the other
parameters are constant; (ii) when they slowly vary with time.
References
M.S. Chong, D. Neić, R. Postoyan, L. Kuhlmann. Parameter and state estimation of nonlinear systems using a
multi-observer under the supervisory framework. IEEE Transactions on Automatic Control, 60(9), 2336-2349,
2015.
Q. Zhang. Adaptive observer for multiple-input-multiple-output (MIMO) linear time-varying systems. IEEE
transactions on automatic control, 47(3), 525-529, 2002.
Y. Zheng, M. Ouyang, X. Han, L. Lu, J. Li. Investigating the error sources of the online state of charge estimation
methods for lithium-ion batteries in electric vehicles. J. of Power Sources, 377, 161-188, 2018.
Keywords:
Control engineering, batteries, modeling, observer, estimation, Lyapunov stability, Matlab-Simulink
Conditions:
This is a M.Sc. project for a student in control or electrical engineering. Matlab skills and good knowledge of the
English language are expected.
English language are expected.
Department(s):
| Control Identification Diagnosis |
Funds:
SAFT
