State of Charge Estimation of Lithium-Ion Battery Considering Operating Conditions and Aging Degree

被引：0

作者：

Wu, Jiang ^{[1
,2
]}

Zhang, Yan ^{[1
,2
]}

Liu, Zelong ^{[1
,2
]}

Cheng, Gang ^{[1
,2
]}

Lei, Dong ^{[1
,2
]}

Jiao, Chaoyong ^{[3
]}

机构：

[1] School of Electronics and Information, Xi’an Polytechnic University, Xi’an

[2] Xi’an Key Laboratory of Interconnected Sensing and Intelligent Diagnosis for Electrical Equipment, Xi’an

[3] NARI Technology Co., Ltd., Nanjing

来源：

Shanghai Jiaotong Daxue Xuebao/Journal of Shanghai Jiaotong University | 2024年 / 58卷 / 12期

关键词：

adaptive dual extended Kalman filter (ADEKF); battery aging; complex operating conditions; lithium-ion battery; state of charge(SOC);

D O I：

10.16183/j.cnki.jsjtu.2023.168

中图分类号：

学科分类号：

摘要：

When using the extended Kalman filter (EKF) to estimate the state of charge (SOC) of an electric vehicle power battery, the change of system noise and model parameters may lead to a reduction in estimation accuracy, due to variable operating conditions, battery aging, and other factors. The NCR18650B ternary lithium-ion battery is selected, and the second-order RC model is established with identified parameters. Then, by using EKF as the main body with a fixed measurement noise covariance and adaptively adjusting process noise covariance based on the maximum likelihood estimation criterion, an adaptive extended Kalman filter is built to estimate the SOC of the battery. Simultaneously, a Kalman filter is used to estimate the ohmic resistance in real time. Thus, an adaptive dual extended Kalman filter (ADEKF) algorithm is formed. Finally, algorithm verifications are performed with testing data and public datasets. The ADEKF proposed is used to estimate the SOC of five groups of aged lithium batteries under three operating conditions, which are constant current, dynamic stress test, and Beijing dynamic stress test, and compared with that of EKF and other algorithms. The results show that compared with EKF, the average absolute error of the estimation results of ADEKF for different aged batteries under three operating conditions decreases by 1.868 percentage points, 2.296 percentage points, and 2.534 percentage points, respectively, which proves that ADEKF algorithm can effectively improve the SOC estimation accuracy under multiple operating conditions, battery aging and the combination of the two factors. © 2024 Shanghai Jiaotong University. All rights reserved.

引用

页码：1935 / 1945

页数：10

共 32 条

[1]

LI Jiaqi, XU Xiaoyuan, YAN Zheng, A review of coupled electricity and hydrogen energy system with transportation system under the background of large-scale new energy vehicles access, Journal of Shanghai Jiao Tong University, 56, 3, pp. 253-266, (2022)

[2]

DU C Q, SHAO J B, WU D M, Et al., Research on co-estimation algorithm of SOC and SOH for lithium-ion batteries in electric vehicles, Electronics, 11, 2, (2022)

[3]

SUN Q, ZHANG H, ZHANG J R, Et al., Adaptive unscented Kalman filter with correntropy loss for robust state of charge estimation of lithium-ion battery, Energies, 11, 11, (2018)

[4]

LU Wei, XU Dan, YANG Qingxia, Et al., Fractional model and state-of-charge of lithium battery, Journal of Xi’an Jiaotong University, 51, 7, pp. 124-129, (2017)

[5]

LIU Yi, TAN Guojun, HE Xiaoqun, Optimized battery model based adaptive sigma Kalman filter for state of charge estimation, Transactions of China Electrotechnical Society, 32, 2, pp. 108-118, (2017)

[6]

HUANG Z J, FANG Y S, XU J J., SOC estimation of Li-ION battery based on improved EKF algorithm, International Journal of Automotive Technology, 22, 2, pp. 335-340, (2021)

[7]

JIANG C, WANG S L, WU B, Et al., A state-of-charge estimation method of the power lithium-ion battery in complex conditions based on adaptive square root extended Kalman filter, Energy, 219, (2021)

[8]

LIM K, BASTAWROUS H A, DUONG V H, Et al., Fading Kalman filter-based real-time state of charge estimation in LiFePO4 battery-powered electric vehicles, Applied Energy, 169, pp. 40-48, (2016)

[9]

YANG Fan, HE Jiarui, LU Ming, Et al., SOC estimation of lithium-ion batteries based on BP-UKF algorithm, Energy Storage Science & Technology, 12, 2, pp. 552-559, (2023)

[10]

HE H W, XIONG R, ZHANG X W, Et al., State-of-charge estimation of the lithium-ion battery using an adaptive extended Kalman filter based on an improved thevenin model, IEEE Transactions on Vehicular Technology, 60, 4, pp. 1461-1469, (2011)

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