State of charge estimation using extended kalman filter

In a world where electric mobility is defining our way of living, electric storage is of great importance especially in applications such as electric vehicles. Although battery technologies are diverse, Lithium-ion technology dominates the market due to its high performance. However, in order to kee...

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Bibliographic Details
Published in2019 International Conference on Wireless Technologies, Embedded and Intelligent Systems (WITS) pp. 1 - 6
Main Authors Mazzi, Yahia, Ben Sassi, Hicham, Errahimi, Fatima, Es-Sbai, Najia
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.04.2019
Subjects
Batteries
battery modeling
Covariance matrices
Discharges (electric)
EKF
Estimation
Kalman filters
lithium-ion
Mathematical model
parameters identification
State of charge
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Summary:In a world where electric mobility is defining our way of living, electric storage is of great importance especially in applications such as electric vehicles. Although battery technologies are diverse, Lithium-ion technology dominates the market due to its high performance. However, in order to keep the security of this part, it is essential to use a battery management system (BMS) to ensure safe and optimum operation. As the key function of this system, accurate state of charge (SOC) estimation is crucial. In this paper, we propose an Extended Kalman Filter (EKF) for the state of charge estimation. Firstly, to achieve the best operation of the EKF an accurate model is required; in this work the first-order Thevenin is presented to model the behaviors of the battery. The internal parameters of the selected model are then identified using the least square algorithm. Simulation results of the model alongside the EKF algorithm for SOC estimation of 3.7V/2.6Ah capacity lithium battery are presented, followed by their implementation on electronic card, which consists of a PIC18F4550 microcontroller.
DOI:10.1109/WITS.2019.8723707