Exploiting the Electrochemical Impedance Spectroscopy Frequency Profiles for State-of-Health Predication of Lithium-Ion Battery

被引:2
|
作者
Al-Hiyali, Mohammed Isam [1 ,2 ]
Kannan, Ramani [1 ]
Alharthi, Yahya Z. [3 ]
Shutari, Hussein [4 ]
机构
[1] Univ Teknol PETRONAS, Dept Elect & Elect Engn, Seri Iskandar 32610, Perak, Malaysia
[2] AL Mansour Univ Coll, Med Instruments Technol Engn Dept, Baghdad 10068, Iraq
[3] Univ Hafr Albatin, Coll Engn, Dept Elect Engn, Hafar al Batin 39524, Saudi Arabia
[4] Univ Sains Malaysia, Sch Elect & Elect Engn, Nibong Tebal 14300, Penang, Malaysia
关键词
Lithium-ion batteries; battery management systems; soh predication; wavelet-machine learning; EIS-Frequency profiles;
D O I
10.1149/1945-7111/ad7b7a
中图分类号
O646 [电化学、电解、磁化学];
学科分类号
081704 ;
摘要
Battery Management Systems (BMS) are essential for optimizing battery performance and extending lifespan through continuous monitoring and decision-making via control sensors. The State of Health (SOH) is one of the BMS metrics that provides valuable information on battery health and degradation. However, one of the main challenges in the BMS domain development is finding accurate and effective algorithms for battery SOH prediction, especially for electric vehicles and grid-connected energy storage systems. This study introduces a new SOH prediction method using wavelet-convolutional neural regression networks (CNRN) algorithms. The methodology involves extracting detailed frequency profiles from Electrochemical Impedance Spectroscopy (EIS) data, which are processed through wavelet transformation to capture both time and frequency domain features. These transformed profiles are then input into the CNRN model for SOH prediction. The results demonstrate improved SOH prediction accuracy with EIS frequency profiles, evidenced by a reduction in root mean square error (RMSE) compared to the standard EIS profile. This improvement is due to the fact that the wavelet-CNRN algorithm efficiently captures both the time and frequency features of the battery impedance. Moreover, the performance of the proposed algorithm demonstrated robustness in early end-of-life (EOL) prediction, thereby enhancing the reliability and safety of BMS functions.
引用
收藏
页数:8
相关论文
共 50 条
  • [41] Summary of Health-State Estimation of Lithium-Ion Batteries Based on Electrochemical Impedance Spectroscopy
    Sun, Xinwei
    Zhang, Yang
    Zhang, Yongcheng
    Wang, Licheng
    Wang, Kai
    ENERGIES, 2023, 16 (15)
  • [42] An Accurate State of Health Estimation for Retired Lithium-ion Batteries Based on Electrochemical Impedance Spectroscopy
    Liu, Xuefeng
    Li, Yichao
    Gu, Pingwei
    Zhang, Ying
    Duan, Bin
    Zhang, Chenghui
    2022 41ST CHINESE CONTROL CONFERENCE (CCC), 2022, : 5253 - 5257
  • [43] Joint Estimation of State-of-Health and State-of-Charge for Lithium-Ion Battery Based on Electrochemical Model Optimized by Neural Network
    Sun, Xiaodong
    Chen, Qi
    Zheng, Linfeng
    Yang, Jufeng
    IEEE Journal of Emerging and Selected Topics in Industrial Electronics, 2023, 4 (01): : 168 - 177
  • [44] State-of-health (SOH) evaluation on lithium-ion battery by simulating the voltage relaxation curves
    Qian, Kun
    Huang, Binhua
    Ran, Aihua
    He, Yan-Bing
    Li, Baohua
    Kang, Feiyu
    ELECTROCHIMICA ACTA, 2019, 303 : 183 - 191
  • [45] Aging performance characterization and state-of-health assessment of retired lithium-ion battery modules
    Zhang, Qichao
    Li, Xinzhou
    Du, Zhichao
    Liao, Qiangqiang
    JOURNAL OF ENERGY STORAGE, 2021, 40
  • [46] A New Hybrid Neural Network Method for State-of-Health Estimation of Lithium-Ion Battery
    Bao, Zhengyi
    Jiang, Jiahao
    Zhu, Chunxiang
    Gao, Mingyu
    ENERGIES, 2022, 15 (12)
  • [47] State-of-Health Estimation of Lithium-Ion Battery Based on Interval Capacity for Electric Buses
    Ye, Baolin
    Zhang, Zhaosheng
    Wang, Shuai
    Ma, Yucheng
    IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION, 2025, 11 (02): : 6096 - 6106
  • [48] Semi-supervised deep learning for lithium-ion battery state-of-health estimation using dynamic discharge profiles
    Xiang, Yue
    Fan, Wenjun
    Zhu, Jiangong
    Wei, Xuezhe
    Dai, Haifeng
    CELL REPORTS PHYSICAL SCIENCE, 2024, 5 (01):
  • [49] Comparison-Transfer Learning Based State-of-Health Estimation for Lithium-Ion Battery
    Liu, Wei
    Gao, Songchen
    Yan, Wendi
    JOURNAL OF ELECTROCHEMICAL ENERGY CONVERSION AND STORAGE, 2024, 21 (04)
  • [50] A neural network based state-of-health estimation of lithium-ion battery in electric vehicles
    Yang, Duo
    Wang, Yujie
    Pan, Rui
    Chen, Ruiyang
    Chen, Zonghai
    8TH INTERNATIONAL CONFERENCE ON APPLIED ENERGY (ICAE2016), 2017, 105 : 2059 - 2064