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Special Issue on Mathematical Modeling Using Differential Equations and Network Theory
Article

Soc Estimation of the Lithium-Ion Battery Pack using a Sigma Point Kalman Filter Based on a Cell’s Second Order Dynamic Model

1
RIAT Institute, Thai Nguyen University of Technology, 3/2 Street, Tich Luong Ward, Thai Nguyen 250000, Vietnam
2
Electrical Faculty, Thai Nguyen University of Technology; 3/2 Street, Tich Luong Ward, Thai Nguyen 250000, Vietnam
*
Author to whom correspondence should be addressed.
Appl. Sci. 2020, 10(5), 1896; https://doi.org/10.3390/app10051896
Received: 9 February 2020 / Revised: 5 March 2020 / Accepted: 6 March 2020 / Published: 10 March 2020
This paper deals with the state of charge (SoC) estimation of a lithium-ion battery pack (LiBP) connected by some cells in series and parallel. The voltage noise, noise and current bias of current through the LiBP are taken into account in the SoC estimation problem. In order to describe the cell dynamic more accurately, especially for practical applications with charge and discharge amplitude varying suddenly, in this paper we use the second dynamic order model of the cell to estimate the SoC of the LiBP. By applying the sigma point Kalman filter (SPKF), the average SoC of the pack and bias current of current measurement are estimated by first estimator; the second estimator estimates the SoC differences of the cell modules from average SoC of the pack. The SoC of the cell modules are the sum of average SoCs of the pack and the SoC differences. By only using two estimators, the calculation complexity for SoC estimation is more reduced; this is very useful for the LiBP, which has the number of cells connected in a large series. This method was applied for the pack of SAMSUNG ICR18650-22P connected by seven cell modules; the cell modules were connected by nine cells in parallel; the LiBP was charged and discharged with amplitude varying suddenly. The estimated SoC of seven cell modules is smaller than 2% for a temperature operating range typically −5 °C to 45 °C. The comparison of the accuracy of SoC estimation based on the first and the second order dynamic models is made; the result shows that the SoC estimation used the second order dynamic model is more exact. View Full-Text
Keywords: lithium-ion battery; SoC estimation; sigma point Kalman filter; battery pack; second order dynamic model of cell lithium-ion battery; SoC estimation; sigma point Kalman filter; battery pack; second order dynamic model of cell
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MDPI and ACS Style

Nguyen Van, C.; Nguyen Vinh, T. Soc Estimation of the Lithium-Ion Battery Pack using a Sigma Point Kalman Filter Based on a Cell’s Second Order Dynamic Model. Appl. Sci. 2020, 10, 1896. https://doi.org/10.3390/app10051896

AMA Style

Nguyen Van C, Nguyen Vinh T. Soc Estimation of the Lithium-Ion Battery Pack using a Sigma Point Kalman Filter Based on a Cell’s Second Order Dynamic Model. Applied Sciences. 2020; 10(5):1896. https://doi.org/10.3390/app10051896

Chicago/Turabian Style

Nguyen Van, Chi, and Thuy Nguyen Vinh. 2020. "Soc Estimation of the Lithium-Ion Battery Pack using a Sigma Point Kalman Filter Based on a Cell’s Second Order Dynamic Model" Applied Sciences 10, no. 5: 1896. https://doi.org/10.3390/app10051896

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