Authors :
Ayush Sharma
Volume/Issue :
Volume 11 - 2026, Issue 8 - August
Google Scholar :
https://tinyurl.com/348w5p3y
DOI :
https://doi.org/10.38124/ijisrt/26aug1527
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working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and
ResearchGate.
Abstract :
Introduction:
Retired 18650 lithium-ion cells may retain useful capacity. However, second-life reuse requires low-cost methods which
characterise electrical behaviour that are degradation-relevant without solely relying on slow capacity testing. This study
evaluates whether a sub-₹3,000 pseudo-random binary sequence (PRBS) impedance front-end can extract repeatable
broadband features under controlled conditions. I hypothesise that PRBS features will demonstrate measurable associations
with degradation-relevant cell properties within the experimentally validated bandwidth.
Methods:
36 retired 18650 cells will be tested at controlled state of charge, temperature and rest. The repeated capacity cycles,
DCIR, self-discharge screening, and PRBS impedance features will be collected on the same cells and fixture. Repeatability
and continuous feature-property associations will be examined in the primary analysis. The secondary analysis will analyse
the grade classification versus the best case DCIR classifications using Leave-One out Cross-Validation (LOOCV).
Keywords :
Second-Life Batteries; 18650 Lithium-Ion Batteries; Electrochemical Impedance Spectroscopy; Pseudo-Random Binary Sequence; DC Internal Resistance; Battery Grading
References :
- A. N. Patel et al., "Lithium-ion battery second life: pathways, challenges and outlook," Frontiers in Chemistry, 2024.
- E. Braco et al., "Fast capacity and internal resistance estimation method for second-life batteries from electric vehicles," Applied Energy, vol. 329, 120235, 2023.
- M. Faraji-Niri, M. Rashid, J. Sansom, M. Sheikh, D. Widanage, and J. Marco, "Accelerated state of health estimation of second-life lithium-ion batteries via electrochemical impedance spectroscopy tests and machine-learning techniques,"
- J. Sihvo, N. Hallemans, A. H. Tan, D. A. Howey, S. R. Duncan, and T. Roinila, "Pseudo-random sequences for low-cost operando impedance measurements of Li-ion batteries," IEEE Transactions on Transportation Electrification, vol. 12, no. 3, pp. 4484-4496, 2026, doi: 10.1109/TTE.2026.3659482.
- J. Cohen, "A coefficient of agreement for nominal scales," Educational and Psychological Measurement, vol. 20, no. 1, pp. 37-46, 1960.
- Q. McNemar, "Note on the sampling error of the difference between correlated proportions or percentages," Psychometrika, vol. 12, pp. 153-157, 1947.
- J. Sihvo, D.-I. Stroe, T. Messo, and T. Roinila, "Fast approach for battery impedance identification using pseudo-random sequence signals," IEEE Transactions on Power Electronics, vol. 35, no. 3, pp. 2548-2557, 2020.
Introduction:
Retired 18650 lithium-ion cells may retain useful capacity. However, second-life reuse requires low-cost methods which
characterise electrical behaviour that are degradation-relevant without solely relying on slow capacity testing. This study
evaluates whether a sub-₹3,000 pseudo-random binary sequence (PRBS) impedance front-end can extract repeatable
broadband features under controlled conditions. I hypothesise that PRBS features will demonstrate measurable associations
with degradation-relevant cell properties within the experimentally validated bandwidth.
Methods:
36 retired 18650 cells will be tested at controlled state of charge, temperature and rest. The repeated capacity cycles,
DCIR, self-discharge screening, and PRBS impedance features will be collected on the same cells and fixture. Repeatability
and continuous feature-property associations will be examined in the primary analysis. The secondary analysis will analyse
the grade classification versus the best case DCIR classifications using Leave-One out Cross-Validation (LOOCV).
Keywords :
Second-Life Batteries; 18650 Lithium-Ion Batteries; Electrochemical Impedance Spectroscopy; Pseudo-Random Binary Sequence; DC Internal Resistance; Battery Grading
