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
  • [31] Machine learning predictions of lithium-ion battery state-of-health for eVTOL applications
    Granado, Lerys
    Ben-Marzouk, Mohamed
    Saenz, Eduard Solano
    Boukal, Yassine
    Juge, Sylvain
    JOURNAL OF POWER SOURCES, 2022, 548
  • [32] State-of-health estimation of lithium-ion battery based on feature transfer learning
    Li, Penghua
    Cheng, Yi
    Shan, KangHeng
    Fang, Yifan
    2022 34TH CHINESE CONTROL AND DECISION CONFERENCE, CCDC, 2022, : 889 - 894
  • [33] Lithium-ion battery state-of-health estimation in electric vehicle using optimized partial charging voltage profiles
    Meng, Jinhao
    Cai, Lei
    Stroe, Daniel-Ioan
    Luo, Guangzhao
    Sui, Xin
    Teodorescu, Remus
    ENERGY, 2019, 185 : 1054 - 1062
  • [34] Design of a fast measuring system for electrochemical impedance spectroscopy of lithium-ion battery
    Lyu, Chao
    Wu, Qi
    Zhao, Di
    Wei, Gang
    Ge, Yaming
    Li, Ming
    Zhu, Meng
    PROCEEDINGS OF THE 2021 IEEE 16TH CONFERENCE ON INDUSTRIAL ELECTRONICS AND APPLICATIONS (ICIEA 2021), 2021, : 1820 - 1825
  • [35] Review of electrochemical impedance spectroscopy methods for lithium-ion battery diagnostics and their limitations
    Novakova, Katerina
    Papez, Vaclav
    Sadil, Jindrich
    Knap, Vaclav
    MONATSHEFTE FUR CHEMIE, 2024, 155 (3-4): : 227 - 232
  • [36] Review of electrochemical impedance spectroscopy methods for lithium-ion battery diagnostics and their limitations
    Kateřina Nováková
    Václav Papež
    Jindřich Sadil
    Václav Knap
    Monatshefte für Chemie - Chemical Monthly, 2024, 155 : 227 - 232
  • [37] Interacting Multiple Model for Lithium-Ion Battery State of Charge Estimation Based on the Electrochemical Impedance Spectroscopy
    Huang, Ce
    Wu, Haibin
    Li, Zhi
    Li, Ran
    Sun, Hui
    ELECTRONICS, 2023, 12 (04)
  • [38] Insights Into Lithium-Ion Battery Cell Temperature and State of Charge Using Dynamic Electrochemical Impedance Spectroscopy
    Knott, L. M.
    Long, E.
    Garner, C. P.
    Fly, A.
    Reid, B.
    Atkins, A.
    INTERNATIONAL JOURNAL OF ENERGY RESEARCH, 2024, 2024
  • [39] Health Indicators Identification of Lithium-Ion Battery From Electrochemical Impedance Spectroscopy Using Geometric Analysis
    Zhou, Zhongkai
    Li, Yan
    Wang, Qing-Guo
    Yu, Jinpeng
    IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 2023, 72
  • [40] Lithium-ion battery health estimate based on electrochemical impedance spectroscopy and CNN-BiLSTM-Attention
    Xing, Qingkai
    Sun, Xinwei
    Fu, Yaping
    Wang, Kai
    IONICS, 2025, 31 (02) : 1389 - 1403