基于蒙特卡洛和 SH-AUKF 算法的锂电池SOC 估计

Chunling Wu; Yanqing Cheng; Xianfeng Xu; Jinhao Meng; Meimei Xie

doi:10.11985/2022.03.008

基于蒙特卡洛和 SH-AUKF 算法的锂电池SOC 估计

Translated title of the contribution: SOC Estimation of Lithium Battery Based on Monte Carlo and SH-AUKF Algorithm

Chunling Wu
, Yanqing Cheng
, Xianfeng Xu
, Jinhao Meng
, Meimei Xie

Research output: Contribution to journal › Article › peer-review

1 Scopus citations

Abstract

Aiming at the problem of low estimation accuracy of SOC of lithium battery, a new adaptive filtering algorithm, SH-AUKF algorithm is proposed by combining Sage-Husa adaptive algorithm with AUKF method. SH-AUKF algorithm can update and modify system noise continuously. UKF, AUKF and SH-AUKF algorithms are used to estimate SOC under DST conditions. The results show that SH-AUKF algorithm has the lowest estimation error and the highest estimation accuracy. Compared with UKF, the estimation accuracy of SH-AUKF algorithm is improved by 45.4%. Compared with AUKF, the estimation accuracy of SH-AUKF algorithm is improved by 14.3%. In order to further reduce the influence of accidental and sudden noise interference on SOC estimation, Monte Carlo sampling method is added in the estimation process. The results show that the error range of SH-AUKF algorithm combined with Monte Carlo method is only ±1×10⁻³, which effectively improves the estimation accuracy.

Translated title of the contribution	SOC Estimation of Lithium Battery Based on Monte Carlo and SH-AUKF Algorithm
Original language	Chinese (Traditional)
Pages (from-to)	66-75
Number of pages	10
Journal	Journal of Electrical Engineering (China)
Volume	17
Issue number	3
DOIs	https://doi.org/10.11985/2022.03.008
State	Published - Sep 2022
Externally published	Yes

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@article{142f028eb53c48098dc27d547b99b847,

title = "基于蒙特卡洛和 SH-AUKF 算法的锂电池SOC 估计",

abstract = "Aiming at the problem of low estimation accuracy of SOC of lithium battery, a new adaptive filtering algorithm, SH-AUKF algorithm is proposed by combining Sage-Husa adaptive algorithm with AUKF method. SH-AUKF algorithm can update and modify system noise continuously. UKF, AUKF and SH-AUKF algorithms are used to estimate SOC under DST conditions. The results show that SH-AUKF algorithm has the lowest estimation error and the highest estimation accuracy. Compared with UKF, the estimation accuracy of SH-AUKF algorithm is improved by 45.4\%. Compared with AUKF, the estimation accuracy of SH-AUKF algorithm is improved by 14.3\%. In order to further reduce the influence of accidental and sudden noise interference on SOC estimation, Monte Carlo sampling method is added in the estimation process. The results show that the error range of SH-AUKF algorithm combined with Monte Carlo method is only ±1×10−3, which effectively improves the estimation accuracy.",

keywords = "Lithium-ion battery, Monte Carlo, Sage-Husa, state of charge",

author = "Chunling Wu and Yanqing Cheng and Xianfeng Xu and Jinhao Meng and Meimei Xie",

year = "2022",

month = sep,

doi = "10.11985/2022.03.008",

language = "繁体中文",

volume = "17",

pages = "66--75",

journal = "Journal of Electrical Engineering (China)",

issn = "2095-9524",

publisher = "Editorial Office of Journal of Electrical Engineering",

number = "3",

}

TY - JOUR

T1 - 基于蒙特卡洛和 SH-AUKF 算法的锂电池SOC 估计

AU - Wu, Chunling

AU - Cheng, Yanqing

AU - Xu, Xianfeng

AU - Meng, Jinhao

AU - Xie, Meimei

PY - 2022/9

Y1 - 2022/9

N2 - Aiming at the problem of low estimation accuracy of SOC of lithium battery, a new adaptive filtering algorithm, SH-AUKF algorithm is proposed by combining Sage-Husa adaptive algorithm with AUKF method. SH-AUKF algorithm can update and modify system noise continuously. UKF, AUKF and SH-AUKF algorithms are used to estimate SOC under DST conditions. The results show that SH-AUKF algorithm has the lowest estimation error and the highest estimation accuracy. Compared with UKF, the estimation accuracy of SH-AUKF algorithm is improved by 45.4%. Compared with AUKF, the estimation accuracy of SH-AUKF algorithm is improved by 14.3%. In order to further reduce the influence of accidental and sudden noise interference on SOC estimation, Monte Carlo sampling method is added in the estimation process. The results show that the error range of SH-AUKF algorithm combined with Monte Carlo method is only ±1×10−3, which effectively improves the estimation accuracy.

AB - Aiming at the problem of low estimation accuracy of SOC of lithium battery, a new adaptive filtering algorithm, SH-AUKF algorithm is proposed by combining Sage-Husa adaptive algorithm with AUKF method. SH-AUKF algorithm can update and modify system noise continuously. UKF, AUKF and SH-AUKF algorithms are used to estimate SOC under DST conditions. The results show that SH-AUKF algorithm has the lowest estimation error and the highest estimation accuracy. Compared with UKF, the estimation accuracy of SH-AUKF algorithm is improved by 45.4%. Compared with AUKF, the estimation accuracy of SH-AUKF algorithm is improved by 14.3%. In order to further reduce the influence of accidental and sudden noise interference on SOC estimation, Monte Carlo sampling method is added in the estimation process. The results show that the error range of SH-AUKF algorithm combined with Monte Carlo method is only ±1×10−3, which effectively improves the estimation accuracy.

KW - Lithium-ion battery

KW - Monte Carlo

KW - Sage-Husa

KW - state of charge

UR - https://www.scopus.com/pages/publications/85173061538

U2 - 10.11985/2022.03.008

DO - 10.11985/2022.03.008

M3 - 文章

AN - SCOPUS:85173061538

SN - 2095-9524

VL - 17

SP - 66

EP - 75

JO - Journal of Electrical Engineering (China)

JF - Journal of Electrical Engineering (China)

IS - 3

ER -

基于蒙特卡洛和 SH-AUKF 算法的锂电池SOC 估计

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