An improved proportional control forgetting factor recursive least square-Monte Carlo adaptive extended Kalman filtering algorithm for high-precision state-of-charge estimation of lithium-ion batteries

For lithium-ion batteries, the state of charge (SOC) of batteries plays an important role in the battery management system, and the accuracy of the battery model and parameter identification is the basis of SOC estimation. Considering that the system has inevitable steady-state errors and the influe...

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Bibliographic Details
Published in:Journal of solid state electrochemistry Vol. 27; no. 9; pp. 2277 - 2287
Main Authors: Zhu, Chenyu, Wang, Shunli, Yu, Chunmei, Zhou, Heng, Fernandez, Carlos
Format: Journal Article
Language:English
Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.09.2023
Springer Nature B.V
Subjects:
Accuracy
Algorithms
Analytical Chemistry
Batteries
Characterization and Evaluation of Materials
Chemistry
Chemistry and Materials Science
Condensed Matter Physics
Electric charge
Electrochemistry
Energy Storage
Greenhouse gases
Kalman filters
Least squares
Lithium
Lithium-ion batteries
Methods
Noise
Parameter identification
Physical Chemistry
Proportional control
Random noise
Review Paper
State of charge
Adaptive extended Kalman filtering
Lithium-ion battery
Proportional control
Monte Carlo
State of charge
ISSN:1432-8488, 1433-0768
Online Access:Get full text
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Summary:For lithium-ion batteries, the state of charge (SOC) of batteries plays an important role in the battery management system, and the accuracy of the battery model and parameter identification is the basis of SOC estimation. Considering that the system has inevitable steady-state errors and the influence of random noise on SOC estimation results under dynamic conditions, this paper proposed an improved proportional control forgetting factor recursive least square-Monte Carlo adaptive extended Kalman filtering (PCFFRLS-MCAEKF) algorithm for high-precision state-of-charge estimation of lithium-ion batteries. The experimental results show that the proportional control forgetting factor recursive least square algorithm has higher parameter identification accuracy under HPPC and BBDST conditions. Under HPPC working conditions, the root mean square error of PCFFRLS-MCAEKF algorithm is reduced by 1.275%, 0.687%, and 0.549% compared with FFRLS-EKF, PCFFRLS-EKF, and PCFFRLS-AEKF algorithm, and the average absolute error is reduced by 0.71%, 0.537%, and 0.11%. Under BBDST working conditions, the SOC estimation result of PCFFRLS-MCAEKF algorithm is closer to the real SOC, which is consistent with the result obtained under HPPC working conditions. The experimental results show that under HPPC and BBDST working conditions, the PCFFRLS-MCAEKF algorithm can better improve the accuracy and robustness of SOC estimation than FFRLS-EKF, PCFFRLS-EKF, and PCFFRLS-AEKF algorithms.
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ISSN:1432-8488
1433-0768
DOI:10.1007/s10008-023-05514-w