State of Charge Estimation of LiFeO4 Batteries Based on Time Domain Features of Ultrasonic Waves and Random Forest

被引：0

作者：

Liu S. ^{[1
,2
]}

Yuan L. ^{[1
,2
]}

Zhang C. ^{[1
,2
]}

Jin L. ^{[1
,2
]}

Yang Q. ^{[1
]}

机构：

[1] State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin

[2] Key Laboratory of Electromagnetic Field and Electrical Apparatus Reliability of Hebei Province, Hebei University of Technology, Tianjin

来源：

Diangong Jishu Xuebao/Transactions of China Electrotechnical Society | 2022年 / 37卷 / 22期

关键词：

Lithium iron phosphate battery; random forest; state of charge; time domain features of ultrasonic waves;

D O I：

10.19595/j.cnki.1000-6753.tces.211585

中图分类号：

学科分类号：

摘要：

State of charge (SOC) is an important monitoring parameter in the battery management system. Due to the flat open circuit voltage and SOC curve, SOC of LiFeO4 (LFP) batteries is not sensitive to changes in electrical signals. Therefore, it is difficult to accurately estimate the SOC of LFP batteries. Ultrasonic wave signals can detect changes in the physical properties of electrode materials, and establish a structure-activity relationship to characterize the battery state. In this paper, a SOC estimation method of LFP batteries is proposed based on high-correlation ultrasound features and a low-complexity regression model. Firstly, the consistency and correlation between commonly used ultrasonic features and SOC are analyzed under different conditions such as ultrasonic transmission frequency, current rate, and temperature. Secondly, the time domain ultrasound features of high-correlation are further extended based on the structural features of ultrasound envelope line. After the comparison of data-driven and model-driven methods, an accurate estimation method of SOC is proposed based on random forest model. The experimental results show that the root mean square error and mean absolute error of SOC estimation under different dynamic conditions are lower than 1.9% and 1.6%, respectively, which verifies the reliability and accuracy of this method. © 2022 Chinese Machine Press. All rights reserved.

引用

页码：5872 / 5885

页数：13

共 31 条

[1]

Tian Jinpeng, Xiong Rui, Shen Weixiang, Et al., State-of-charge estimation of LiFePO<sub>4</sub> batteries in electric vehicles: a deep-learning enabled approach, Applied Energy, 291, 3, (2021)

[2]

Wu Chunling, Hu Wenbo, Meng Jinhao, Et al., State of charge estimation of lithium-ion batteries based on maximum correlation-entropy criterion extended Kalman filtering algorithm, Transactions of China Electrotechnical Society, 36, 24, pp. 5165-5175, (2021)

[3]

Aifantis K E, Hackney S A, Kumar R V, (2012)

[4]

Xiong Rui, Wang Ju, Shen Weixiang, Et al., Co-estimation of state of charge and capacity for lithium-ion batteries with multi-stage model fusion method, Engineering, 7, 10, pp. 1469-1482, (2021)

[5]

Chen Xiaopeng, Shen Weixiang, Dai Mingxiang, Et al., Robust adaptive sliding-mode observer using RBF neural network for lithium-ion battery state of charge estimation in electric vehicles, IEEE Transactions on Vehicular Technology, 65, 4, pp. 1936-1947, (2015)

[6]

Chemali E, Kollmeyer P J, Preindl M, Et al., Long short-term memory networks for accurate state-of-charge estimation of Li-ion batteries, IEEE Transactions on Industrial Electronics, 65, 8, pp. 6730-6739, (2018)

[7]

Pang Hui, Guo Long, Wu Longxing, Et al., An improved dual polarization model of Li-ion battery and its state of charge estimation considering ambient temperature, Transactions of China Electrotechnical Society, 36, 10, pp. 2178-2189, (2021)

[8]

Poloni T, Figueroa-Santos M A, Siegel J B, Et al., Integration of non-monotonic cell swelling characteristic for state-of-charge estimation, Annual American Control Conference (ACC), pp. 2306-2311, (2018)

[9]

Popp H, Koller M, Jahn M, Et al., Mechanical methods for state determination of lithium-ion secondary batteries: a review, Journal of Energy Storage, 32, 9, (2020)

[10]

Deng Zhe, Huang Zhenyu, Liu Lei, Et al., Applications of ultrasound technique in characterization of lithium-ion batteries, Energy Storage Science and Technology, 8, 6, pp. 1033-1039, (2019)

← 1 2 3 4 →