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Parameter Identification and State of Charge Estimation of NMC Cells Based on Improved Ant Lion Optimizer
Joint Authors
Zhao, Xuan
Liu, Xiaodong
Ma, Jian
Zhang, Kai
Zhang, Yixi
Source
Mathematical Problems in Engineering
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-18, 18 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-07-16
Country of Publication
Egypt
No. of Pages
18
Main Subjects
Abstract EN
For lithium battery, which is widely utilized as energy storage system in electric vehicles (EVs), accurate estimating of the battery parameters and state of charge (SOC) has a significant effect on the prediction of energy power, the estimation of remaining mileage, and the extension of usage life.
This paper develops an improved ant lion optimizer (IALO) which introduces the chaotic mapping theory into the initialization and random walk processes to improve the population homogeneity and ergodicity.
After the elite (best) individual is obtained, the individual mutant operator is conducted on the elite individual to further exploit the area around elite and avoid local optimum.
Then the battery model parameters are optimized by IALO algorithm.
As for the SOC estimation, unscented Kalman filter (UKF) is a common algorithm for SOC estimation.
However, a disadvantage of UKF is that the noise information is always unknown, and it is usually tuned manually by “trial-and-error” method which is irregular and time-consuming.
In this paper, noise information is optimized by IALO algorithm.
The singular value decomposition (SVD) which is utilized in the process of unscented transformation to solve the problem of the covariance matrix may lose positive definiteness.
The experiment results verify that the developed IALO algorithm has superior performance of battery model parameters estimation.
After the noise information is optimized by IALO, the UKF can estimate the SOC accurately and the maximum errors rate is less than 1%.
American Psychological Association (APA)
Zhang, Kai& Ma, Jian& Zhao, Xuan& Liu, Xiaodong& Zhang, Yixi. 2019. Parameter Identification and State of Charge Estimation of NMC Cells Based on Improved Ant Lion Optimizer. Mathematical Problems in Engineering،Vol. 2019, no. 2019, pp.1-18.
https://search.emarefa.net/detail/BIM-1195866
Modern Language Association (MLA)
Zhang, Kai…[et al.]. Parameter Identification and State of Charge Estimation of NMC Cells Based on Improved Ant Lion Optimizer. Mathematical Problems in Engineering No. 2019 (2019), pp.1-18.
https://search.emarefa.net/detail/BIM-1195866
American Medical Association (AMA)
Zhang, Kai& Ma, Jian& Zhao, Xuan& Liu, Xiaodong& Zhang, Yixi. Parameter Identification and State of Charge Estimation of NMC Cells Based on Improved Ant Lion Optimizer. Mathematical Problems in Engineering. 2019. Vol. 2019, no. 2019, pp.1-18.
https://search.emarefa.net/detail/BIM-1195866
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1195866