Analysis of Aging and Degradation in Lithium Batteries Using Distribution of Relaxation Time
In this paper, the deconvolution of Electrochemical Impedance Spectroscopy (EIS) data into the Distribution of Relaxation Times (DRTs) is employed to provide a detailed examination of degradation mechanisms in lithium-ion batteries. Using an nth RC model with Gaussian functions, this study achieves...
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MDPI AG
2025-01-01
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| Online Access: | https://www.mdpi.com/2313-0105/11/1/34 |
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| author | Muhammad Sohaib Abdul Shakoor Akram Woojin Choi |
| author_facet | Muhammad Sohaib Abdul Shakoor Akram Woojin Choi |
| author_sort | Muhammad Sohaib |
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| description | In this paper, the deconvolution of Electrochemical Impedance Spectroscopy (EIS) data into the Distribution of Relaxation Times (DRTs) is employed to provide a detailed examination of degradation mechanisms in lithium-ion batteries. Using an nth RC model with Gaussian functions, this study achieves enhanced separation of overlapping electrochemical processes where Gaussian functions yield smoother transitions and clearer peak identification than conventional piecewise linear functions. The advantages of employing Tikhonov Regularization (TR) with Gaussian functions over Maximum Entropy (ME) and FFT methods are highlighted as this approach provides superior noise resilience, unbiased analysis, and enhanced resolution of critical features. This approach is applied to LIB cell data to identify characteristic peaks of the DRT plot and evaluate their correlation with battery degradation. By observing how these peaks evolve through cycles of battery aging, insights into specific aging mechanisms and performance decline are obtained. This study combines experimental measurements with DRT peak analysis to characterize the impedance distribution within LIBs which enables accelerated detection of degradation pathways and enhances the predictive accuracy for battery life and reliability. This analysis contributes to a refined understanding of LIB degradation behavior, supporting the development of advanced battery management systems designed to improve safety, optimize battery performance, and extend the operational lifespan of LIBs for various applications. |
| format | Article |
| id | doaj-art-2552c39576f64484b298d5f2d414d753 |
| institution | Kabale University |
| issn | 2313-0105 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
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| series | Batteries |
| spelling | doaj-art-2552c39576f64484b298d5f2d414d7532025-01-24T13:22:29ZengMDPI AGBatteries2313-01052025-01-011113410.3390/batteries11010034Analysis of Aging and Degradation in Lithium Batteries Using Distribution of Relaxation TimeMuhammad Sohaib0Abdul Shakoor Akram1Woojin Choi2School of Electrical Engineering, Soongsil University, Seoul 06978, Republic of KoreaSchool of Electrical Engineering, Soongsil University, Seoul 06978, Republic of KoreaSchool of Electrical Engineering, Soongsil University, Seoul 06978, Republic of KoreaIn this paper, the deconvolution of Electrochemical Impedance Spectroscopy (EIS) data into the Distribution of Relaxation Times (DRTs) is employed to provide a detailed examination of degradation mechanisms in lithium-ion batteries. Using an nth RC model with Gaussian functions, this study achieves enhanced separation of overlapping electrochemical processes where Gaussian functions yield smoother transitions and clearer peak identification than conventional piecewise linear functions. The advantages of employing Tikhonov Regularization (TR) with Gaussian functions over Maximum Entropy (ME) and FFT methods are highlighted as this approach provides superior noise resilience, unbiased analysis, and enhanced resolution of critical features. This approach is applied to LIB cell data to identify characteristic peaks of the DRT plot and evaluate their correlation with battery degradation. By observing how these peaks evolve through cycles of battery aging, insights into specific aging mechanisms and performance decline are obtained. This study combines experimental measurements with DRT peak analysis to characterize the impedance distribution within LIBs which enables accelerated detection of degradation pathways and enhances the predictive accuracy for battery life and reliability. This analysis contributes to a refined understanding of LIB degradation behavior, supporting the development of advanced battery management systems designed to improve safety, optimize battery performance, and extend the operational lifespan of LIBs for various applications.https://www.mdpi.com/2313-0105/11/1/34distribution of relaxation timeelectrochemical impedance spectroscopydegradation mechanismsolid electrolyte interfaceloss of active materialloss of lithium ion |
| spellingShingle | Muhammad Sohaib Abdul Shakoor Akram Woojin Choi Analysis of Aging and Degradation in Lithium Batteries Using Distribution of Relaxation Time Batteries distribution of relaxation time electrochemical impedance spectroscopy degradation mechanism solid electrolyte interface loss of active material loss of lithium ion |
| title | Analysis of Aging and Degradation in Lithium Batteries Using Distribution of Relaxation Time |
| title_full | Analysis of Aging and Degradation in Lithium Batteries Using Distribution of Relaxation Time |
| title_fullStr | Analysis of Aging and Degradation in Lithium Batteries Using Distribution of Relaxation Time |
| title_full_unstemmed | Analysis of Aging and Degradation in Lithium Batteries Using Distribution of Relaxation Time |
| title_short | Analysis of Aging and Degradation in Lithium Batteries Using Distribution of Relaxation Time |
| title_sort | analysis of aging and degradation in lithium batteries using distribution of relaxation time |
| topic | distribution of relaxation time electrochemical impedance spectroscopy degradation mechanism solid electrolyte interface loss of active material loss of lithium ion |
| url | https://www.mdpi.com/2313-0105/11/1/34 |
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