Determination of the Distribution of Relaxation Times by Means of Pulse Evaluation for Offline and Online Diagnosis of Lithium-Ion Batteries
Source abstract
Abstract
The distribution of relaxation times (DRT) analysis of impedance spectra is a proven method to determine the number of occurring polarization processes in lithium-ion batteries (LIBs), their polarization contributions and characteristic time constants. Direct measurement of a spectrum by means of electrochemical impedance spectroscopy (EIS), however, suffers from a high expenditure of time for low-frequency impedances and a lack of general availability in most online applications. In this study, a method is presented to derive the DRT by evaluating the relaxation voltage after a current pulse. The method was experimentally validated using both EIS and the proposed pulse evaluation to determine the DRT of automotive pouch-cells and an aging study was carried out. The DRT derived from time domain data provided improved resolution of processes with large time constants and therefore enabled changes in low-frequency impedance and the correlated degradation mechanisms to be identified. One of the polarization contributions identified could be determined as an indicator for the potential risk of plating. The novel, general approach for batteries was tested with a sampling rate of 10 Hz and only requires relaxation periods. Therefore, the method is applicable in battery management systems and contributes to improving the reliability and safety of LIBs.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Advanced Battery Technologies Research · Advancements in Battery Materials · Advanced Battery Materials and Technologies
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Electrical Energy Storage Technology, Departement of Energy and Automation Technology, Faculty IV, Secr. EMH 2, Technische Universität Berlin, Einsteinufer 11, D-10587 Berlin, Germany
Funding
Bundesministerium für Bildung und Forschung
References (38)
Show source-provided references
1. A Comparison between Electrochemical Impedance Spectroscopy and Incremental Capacity-Differential Voltage as Li-ion Diagnostic Techniques to Identify and Quantify the Effects of Degradation Modes within Battery Management Systems10.1016/j.jpowsour.2017.03.042 ↗
2. Critical review of non-invasive diagnosis techniques for quantification of degradation modes in lithium-ion batteries10.1016/j.rser.2019.03.060 ↗
3. Voltage relaxation and impedance spectroscopy as in-operando methods for the detection of lithium plating on graphitic anodes in commercial lithium-ion cells10.1016/j.jpowsour.2015.11.044 ↗
4. Investigation of the Solid Electrolyte Interphase Formation at Graphite Anodes in Lithium-Ion Batteries with Electrochemical Impedance Spectroscopy10.1016/j.electacta.2017.01.128 ↗
5. Non-invasive yet separate investigation of anode/cathode degradation of lithium-ion batteries (nickel–cobalt–manganese vs. graphite) due to accelerated aging10.1016/j.jpowsour.2019.227369 ↗
6. Impedance change and capacity fade of lithium nickel manganese cobalt oxide-based batteries during calendar aging10.1016/j.jpowsour.2017.03.090 ↗
7. Characterization of high-power lithium-ion batteries by electrochemical impedance spectroscopy. I. Experimental investigation10.1016/j.jpowsour.2010.12.102 ↗
8. Characterizing aging effects of lithium ion batteries by impedance spectroscopy10.1016/j.electacta.2005.02.148 ↗
9. Fundamentals of battery dynamics10.1016/j.jpowsour.2005.10.041 ↗
10. Comparison of methods for estimating continuous distributions of relaxation times10.1063/1.2188053 ↗
11. Optimal Regularization in Distribution of Relaxation Times applied to Electrochemical Impedance Spectroscopy: Ridge and Lasso Regression Methods - A Theoretical and Experimental Study10.1016/j.electacta.2014.09.058 ↗
12. Influence of the Discretization Methods on the Distribution of Relaxation Times Deconvolution: Implementing Radial Basis Functions with DRTtools10.1016/j.electacta.2015.09.097 ↗
13. Analysis and Application of Distribution of Relaxation Times in Solid State Ionics10.1016/j.ssi.2016.10.009 ↗
14. Danzer, M.A. (2019). Generalized Distribution of Relaxation Times Analysis for the Characterization of Impedance Spectra. Batteries, 5.10.3390/batteries5030053 ↗
15. Hahn, M., Schindler, S., Triebs, L.C., and Danzer, M.A. (2019). Optimized Process Parameters for a Reproducible Distribution of Relaxation Times Analysis of Electrochemical Systems. Batteries, 5.10.3390/batteries5020043 ↗
16. Evaluation of electrochemical impedance spectra by the distribution of relaxation times10.2109/jcersj2.16267 ↗
17. Investigation of physico-chemical processes in lithium-ion batteries by deconvolution of electrochemical impedance spectra10.1016/j.jpowsour.2017.07.006 ↗
18. Time-domain fitting of battery electrochemical impedance models10.1016/j.jpowsour.2015.04.099 ↗
19. New approach for the calculation of impedance spectra out of time domain data10.1016/j.electacta.2011.07.096 ↗
20. Novel method for the on-line estimation of low-frequency impedance of lithium-ion batteries10.1016/j.est.2020.101818 ↗
21. Adaptive modeling in the frequency and time domain of high-power lithium titanate oxide cells in battery management systems10.1016/j.est.2020.101966 ↗
22. A review of fractional-order techniques applied to lithium-ion batteries, lead-acid batteries, and supercapacitors10.1016/j.jpowsour.2018.04.033 ↗
23. The distribution of relaxation times as basis for generalized time-domain models for Li-ion batteries10.1016/j.jpowsour.2012.07.100 ↗
24. Investigation of lithium-ion battery degradation mechanisms by combining differential voltage analysis and alternating current impedance10.1016/j.jpowsour.2019.227575 ↗
25. Time-Domain Parameter Extraction Method for Thévenin-Equivalent Circuit Battery Models10.1109/TEC.2014.2318205 ↗
26. Xiaosong, H., Fengchun, S., Yuan, Z., and Huei, P. (July, January 29). Online estimation of an electric vehicle Lithium-Ion battery using recursive least squares with forgetting. Proceedings of the 2011 American Control Conference, San Francisco, CA, USA.
27. Goldammer, M.S.E., and Kowal, J. (December, January 18). Investigation of degradation mechanisms in lithium-ion batteries by incremental open-circuit-voltage characterization and impedance spectra. Proceedings of the 2020 IEEE Vehicle Power and Propulsion Conference (VPPC), Gijon, Spain.10.1109/VPPC49601.2020.9330948 ↗
28. Tikhonov, A.N., Goncharsky, A.V., and Hazewinkel, M. (2010). Numerical Methods for the Solution of Ill-Posed Problems, Springer.
29. Schindler, S. (2018). Diskrete Elektrochemische Modellierung und Experimentelle Identifikation von Lithium-Ionen-Zellen Basierend auf Halbzellpotentialen, Universität Bayreuth.
30. A study of the open circuit voltage characterization technique and hysteresis assessment of lithium-ion cells10.1016/j.jpowsour.2015.06.140 ↗
31. Gantenbein, S., Schönleber, M., Weiss, M., and Ivers-Tiffée, E. (2019). Capacity Fade in Lithium-Ion Batteries and Cyclic Aging over Various State-of-Charge Ranges. Sustainability, 11.10.3390/su11236697 ↗
32. Long-term equalization effects in Li-ion batteries due to local state of charge inhomogeneities and their impact on impedance measurements10.1016/j.electacta.2015.10.108 ↗
33. Separation of predominant processes in electrochemical impedance spectra of lithium-ion batteries with nickel-manganese-cobalt cathodes10.1016/j.jpowsour.2019.03.068 ↗
34. Modeling graphite anodes with serial and transmission line models10.1016/j.jpowsour.2015.02.038 ↗
35. Durability and Reliability of EV Batteries under Electric Utility Grid Operations: Path Dependence of Battery Degradation10.1149/2.0421805jes ↗
36. Operando lithium plating quantification and early detection of a commercial LiFePO4 cell cycled under dynamic driving schedule10.1016/j.jpowsour.2017.04.072 ↗
37. A novel mechanistic modeling framework for analysis of electrode balancing and degradation modes in commercial lithium-ion cells10.1016/j.jpowsour.2017.01.026 ↗
38. Identification and machine learning prediction of knee-point and knee-onset in capacity degradation curves of lithium-ion cells10.1016/j.egyai.2020.100006 ↗