State-of-Health Diagnosis of Lithium-Ion Batteries Using Nonlinear Frequency Response Analysis

被引:22
作者
Harting, Nina [1 ,2 ]
Wolff, Nicolas [1 ,2 ]
Roeder, Fridolin [1 ,2 ]
Krewer, Ulrike [1 ,2 ]
机构
[1] TU Braunschweig, Inst Energy & Proc Syst Engn, Braunschweig, Germany
[2] TU Braunschweig, Battery LabFactory Braunschweig, Braunschweig, Germany
关键词
ELECTROCHEMICAL IMPEDANCE SPECTROSCOPY; AGING MECHANISMS; DEGRADATION; CHARGE; PERFORMANCE; MANAGEMENT; QUANTIFY; VOLTAGE;
D O I
10.1149/2.1031902jes
中图分类号
O646 [电化学、电解、磁化学];
学科分类号
081704 ;
摘要
Estimation of the State-of-Health (SOH) of Lithium-ion Batteries (LIBs) is commonly conducted using in-situ measurement methods, such as Incremental Capacity Analysis (ICA) and Differential Voltage Analysis (DVA) as well as impedance based techniques. In this study, we present an alternative method for SOH estimation: The nonlinear dynamic measurement method Nonlinear Frequency Response Analysis (NFRA) is shown to be able to estimate capacity fade of LIBs due to loss of active material. Capacity loss correlates with the quotient of the root mean square of the second and the third harmonic response for different excitation amplitudes in the frequency range sensitive to electrochemical reactions at approximately 1 Hz. The results of the experimental cycle-aging study are validated and further analyzed by using a reaction model containing Butler-Volmer kinetics with a dynamic charge balance of the electrode. Simulations show that the NFR quotient and capacity fade due to loss of specific surface area correlate exactly. We identify the NFR quotient as a reliable, easily measurable parameter for the diagnosis of the SOH of LIBs. Therefore, this study reveals a novel approach for SOH estimation of LIBs based on dynamic analysis with NFRA. (C) The Author(s) 2019. Published by ECS.
引用
收藏
页码:A277 / A285
页数:9
相关论文
共 50 条
  • [1] State-of-Health Identification of Lithium-Ion Batteries Based on Nonlinear Frequency Response Analysis: First Steps with Machine Learning
    Harting, Nina
    Schenkendorf, Rene
    Wolff, Nicolas
    Krewer, Ulrike
    APPLIED SCIENCES-BASEL, 2018, 8 (05):
  • [2] Nonlinear Frequency Response Analysis (NFRA) of Lithium-Ion Batteries
    Harting, Nina
    Wolff, Nicolas
    Roeder, Fridolin
    Krewer, Ulrike
    ELECTROCHIMICA ACTA, 2017, 248 : 133 - 139
  • [3] Identification of Lithium Plating in Lithium-Ion Batteries using Nonlinear Frequency Response Analysis (NFRA)
    Harting, Nina
    Wolff, Nicolas
    Krewer, Ulrike
    ELECTROCHIMICA ACTA, 2018, 281 : 378 - 385
  • [4] Perspective on State-of-Health Determination in Lithium-Ion Batteries
    Dubarry, Matthieu
    Baure, George
    Ansean, David
    JOURNAL OF ELECTROCHEMICAL ENERGY CONVERSION AND STORAGE, 2020, 17 (04)
  • [5] Exploration of Imbalanced Regression in state-of-health estimation of Lithium-ion batteries
    Zhao, Zhibin
    Liu, Bingchen
    Wang, Fujin
    Zheng, Shiyu
    Yu, Qiuyu
    Zhai, Zhi
    Chen, Xuefeng
    JOURNAL OF ENERGY STORAGE, 2025, 105
  • [6] State-of-Health Estimation for Lithium-Ion Batteries in Hybrid Electric Vehicles-A Review
    Zhang, Jianyu
    Li, Kang
    ENERGIES, 2024, 17 (22)
  • [7] Degradation mechanism analysis and State-of-Health estimation for lithium-ion batteries based on distribution of relaxation times
    Zhang, Qi
    Wang, Dafang
    Schaltz, Erik
    Stroe, Daniel-Ioan
    Gismero, Alejandro
    Yang, Bowen
    JOURNAL OF ENERGY STORAGE, 2022, 55
  • [8] State-of-Health Estimation for Lithium-Ion Batteries Using Domain Adversarial Transfer Learning
    Ye, Zhuang
    Yu, Jianbo
    IEEE TRANSACTIONS ON POWER ELECTRONICS, 2022, 37 (03) : 3528 - 3543
  • [9] State-of-Health Estimation of Lithium-Ion Batteries Using Incremental Capacity Analysis Based on Voltage-Capacity Model
    He, Jiangtao
    Wei, Zhongbao
    Bian, Xiaolei
    Yan, Fengjun
    IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION, 2020, 6 (02) : 417 - 426
  • [10] Feature parameter extraction and intelligent estimation of the State-of-Health of lithium-ion batteries
    Deng, Yuanwang
    Ying, Hejie
    Jiaqiang, E.
    Zhu, Hao
    Wei, Kexiang
    Chen, Jingwei
    Zhang, Feng
    Liao, Gaoliang
    ENERGY, 2019, 176 : 91 - 102