Open AccessEstimation of lithium-ion battery state-of-charge using an extended kalman filter
Author(s) -
Mouhssine Lagraoui,
A. Nejmi,
Hassan Rayhane,
Abderrahim Taouni
Publication year - 2021
Publication title -
bulletin of electrical engineering and informatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.251
H-Index - 12
ISSN - 2302-9285
DOI - 10.11591/eei.v10i4.3082
Subject(s) - state of charge , extended kalman filter , battery (electricity) , kalman filter , control theory (sociology) , equivalent circuit , lithium ion battery , capacitance , computer science , system identification , battery pack , voltage , electronic engineering , engineering , electrical engineering , data modeling , chemistry , power (physics) , artificial intelligence , physics , control (management) , electrode , quantum mechanics , database
The main goal of a battery management system (BMS) is to estimate parameters descriptive of the battery pack operating conditions in real-time. One of the most critical aspects of BMS systems is estimating the battery's state of charge (SOC). However, in the case of a lithium-ion battery, it is not easy to provide an accurate estimate of the state of charge. In the present paper we propose a mechanism based on an extended kalman filter (EKF) to improve the state-of-charge estimation accuracy on lithium-ion cells. The paper covers the cell modeling and the system parameters identification requirements, the experimental tests, and results analysis. We first established a mathematical model representing the dynamics of a cell. We adopted a model that comprehends terms that describe the dynamic parameters like SOC, open-circuit voltage, transfer resistance, ohmic loss, diffusion capacitance, and resistance. Then, we performed the appropriate battery discharge tests to identify the parameters of the model. Finally, the EKF filter applied to the cell test data has shown high precision in SOC estimation, even in a noisy system.