Joint SOC-SOH estimation for UUV battery management system

被引：1
|
作者：
Lu D. [1 ,2 ]
Zhou S. [1 ,2 ]
Chen Z. [3 ]
机构：
[1] Shanghai Marine Electronic Equipment Research Institute, Shanghai
[2] Science and Technology on Underwater Acoustics Antagonizing Laboratory, Shanghai
[3] State Key Laboratory of Ocean Engineering, Collaborative Innovation Center for Advanced Ship and Deep-Sea Exploration, Shanghai Jiao Tong University, Shanghai
来源：
Zhejiang Daxue Xuebao (Gongxue Ban)/Journal of Zhejiang University (Engineering Science) | 2024年 / 58卷 / 05期
关键词：
extended Kalman filter (EKF); lithium-ion battery; SOC-SOH joint estimation; support vector regression (SVR); unmanned underwater vehicle (UUV);
D O I：
10.3785/j.issn.1008-973X.2024.05.021
中图分类号：
学科分类号：
摘要：
A joint state of charge (SOC)-state of health (SOH) method of estimation was proposed in order to improve the state estimation accuracy of unmanned underwater vehicle (UUV) battery management system. A test bench was constructed, and four groups of lithium-ion batteries were used for charging and discharging test under the whole life cycle. Data under different attenuation degrees were obtained. Four-dimensional factors were designed by theoretical derivation and experimental analysis, and a SOH estimation model based on improved support vector regression (SVR) was established. The coupling relationship between battery states was explored. A SOC estimation model based on extended Kalman filter (EKF) was established and the forgetting factor recursive least squares (RLS) algorithm was used to update the model parameters. The SOC estimation results were corrected by SOH. The method was validated through different testing conditions experiment. Results show that the four-dimensional characterization factor and battery capacity have good correlation. The accuracy of SOH estimation model is high, and the accuracy of SOC estimation model is improved by joint modification. The proposed joint estimation method has high universality and reliability, and can be used as an effective state estimation algorithm for embedded battery management system. © 2024 Zhejiang University. All rights reserved.
引用
收藏
页码：1080 / 1090
页数：10
相关论文
共 22 条
  • [1] DU X, HU X, HU J, Et al., An adaptive interactive multi-model navigation method based on UUV, Ocean Engineering, 267, (2023)
  • [2] YAN Zheping, WANG Lu, Research status and progress of UUV underwater localization [J], Journal of Harbin Engineering University, 38, 7, pp. 989-1000, (2017)
  • [3] DAI Hua, ZHANG Bin, XU Yanzhe, Current status and development trend of power batteries for UUV [J], Ship Science and Technology, 42, 23, pp. 155-158, (2020)
  • [4] JING Youze, LIU Zhiwei, Current situation and development trend of power battery for UUV [J], Power Technology, 43, 6, pp. 1073-1076, (2019)
  • [5] HUANG D, CHEN Z, ZHENG C, Et al., A model-based state-of-charge estimation method for series-connected lithium-ion battery pack considering fast-varying cell temperature [J], Energy, 185, C, pp. 847-861, (2019)
  • [6] ZHAO Jingying, HU Jin, ZHANG Xuehui, Et al., Joint estimation of the SOC-SOH based on lithium battery model and fractional order theory [J], Transactions of China Electrotechnical Society, 38, 17, pp. 4551-4563, (2023)
  • [7] LEI Kebing, CHEN Ziqiang, Estimation of SOC of battery based on improved multi-innovation extended Kalman filter [J], Journal of Zhejiang University: Engineering Science, 55, 10, pp. 1978-1985, (2021)
  • [8] DUAN Linchao, ZHANG Xugang, ZHANG Hua, Et al., SOC estimation of lithium-ion battery based on second-order approximate extended Kalman filter [J], China Mechanical Engineering, 34, 15, pp. 1797-1804, (2023)
  • [9] WANG Y, CHENG Y, XIONG Y, Et al., Estimation of battery open-circuit voltage and state of charge based on dynamic matrix control-extended Kalman filter algorithm, Journal of Energy Storage, 52, A, (2022)
  • [10] DANG L, HUANG Y, ZHANG Y, Et al., Multi-kernel correntropy based extended Kalman filtering for state-of-charge estimation [J], ISA Transactions, 129, B, pp. 271-283, (2022)
123