Study on the Impact of Voltage Characteristics, Discharge Time Lag, and Electrolyte Density Measured Based on Temperature-Corrected Hydrometer Method for the Reliability of 110 VDC Protection Batteries

Authors

DOI:

https://doi.org/10.26905/jeemecs.v9i2.16582

Keywords:

Battery reliability, Battery efficiency, Discharge time, Electrolyte density, Battery diagnostics

Abstract

The substation battery serves as an emergency power source during interruptions of the Alternating Current (AC) supply to the rectifier, thereby maintaining the uninterrupted operation of protection and control equipment. Considering the critical role of batteries in maintaining substation system reliability, routine diagnostic testing is required to evaluate their suitability as Direct Current (DC) power sources. This study investigates the performance and reliability of 110 VDC protection batteries installed in a high-voltage substation through comprehensive diagnostic evaluations. The diagnostic procedures applied include terminal voltage measurement, electrolyte density measurement, and battery capacity testing, which are commonly used methods to assess battery condition in substation applications. Battery efficiency and estimated discharge time while supplying protection loads were selected as the primary performance indicators for evaluating battery suitability as a DC backup source. The test results indicate that battery 1 achieved an efficiency of 36.74%, while battery 2 exhibited an efficiency of 35.08%. Furthermore, the estimated discharge duration for both batteries was approximately 53 minutes when supplying the protection load. These values are significantly below the recommended operational standards, which require a minimum efficiency of 60% and a discharge duration of at least 3 hours. Based on these findings, the batteries are classified as unreliable and unsuitable for sustained DC supply during blackout conditions. The results highlight the importance of periodic battery diagnostics and timely maintenance to ensure reliable DC power systems in substations.

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Author Biographies

Jamaaluddin, Muhammadiyah University of Sidoarjo

Department of Electrical Engineering

Miftachul Ulum, Trunojoyo Madura University

Department of Electrical Engineering

References

[1] L. Apa, L. D’Alvia, Z. Del Prete, and E. Rizzuto, “A characterization of the uncertainties associated with an automated system for the study of lithium-ion cells: A case-study of a domestic grid 24-h scenario,” IEEE Transactions on Instrumentation and Measurement, vol. 73, pp. 1–11, 2024, doi: 10.1109/TIM.2024.3476559.

[2] M. Rouholamini et al., “A review of modeling, management, and applications of grid-connected Li-ion battery storage systems,” IEEE Transactions on Smart Grid, vol. 13, no. 6, pp. 4505–4524, 2022, doi: 10.1109/TSG.2022.3188598.

[3] S. Wang, K. Ou, W. Zhang, and Y.-X. Wang, “A state-of-charge and state-of-health joint estimation method of lithium-ion battery based on temperature-dependent extended Kalman filter and deep learning,” IEEE Transactions on Industrial Electronics, vol. 72, no. 1, pp. 570–579, 2025, doi: 10.1109/TIE.2024.3409912.

[4] R. R. Kumar, C. Bharatiraja, K. Udhayakumar, S. Devakirubakaran, K. S. Sekar, and L. Mihet-Popa, “Advances in batteries, battery modeling, battery management system, battery thermal management, SOC, SOH, and charge/discharge characteristics in EV applications,” IEEE Access, vol. 11, pp. 105761–105809, 2023, doi: 10.1109/ACCESS.2023.3318121.

[5] S. Karimi, M. Zadeh, and J. A. Suul, “Operation-based reliability assessment of shore-to-ship charging systems including on-shore batteries,” IEEE Transactions on Industry Applications, vol. 59, no. 4, pp. 4752–4763, 2023, doi: 10.1109/TIA.2023.3258419.

[6] I. Alvarez-Monteserin and M. Á. Sanz-Bobi, “An online fade capacity estimation of lithium-ion battery using a new health indicator based only on a short period of the charging voltage profile,” IEEE Access, vol. 10, pp. 11138–11146, 2022, doi: 10.1109/ACCESS.2022.3143107.

[7] L. Timilsina, P. R. Badr, P. H. Hoang, G. Ozkan, B. Papari, and C. S. Edrington, “Battery degradation in electric and hybrid electric vehicles: A survey study,” IEEE Access, vol. 11, pp. 42431–42462, 2023, doi: 10.1109/ACCESS.2023.3271287.

[8] J. Omakor, M. S. Miah, and H. Chaoui, “Battery reliability assessment in electric vehicles: A state-of-the-art,” IEEE Access, vol. 12, pp. 77903–77931, 2024, doi: 10.1109/ACCESS.2024.3406424.

[9] S. A. Hasib et al., “A comprehensive review of available battery datasets, RUL prediction approaches, and advanced battery management,” IEEE Access, vol. 9, pp. 86166–86193, 2021, doi: 10.1109/ACCESS.2021.3089032.

[10] L. Cheng, Y. Wan, Y. Zhou, and D. W. Gao, “Operational reliability modeling and assessment of battery energy storage based on lithium-ion battery lifetime degradation,” Journal of Modern Power Systems and Clean Energy, vol. 10, no. 6, pp. 1738–1749, 2022, doi: 10.35833/MPCE.2021.000197.

[11] P. Mangaiyarkarasi, R. Jayaganthan, and A. V. Thayyil, “Development of an enhanced self-correcting equivalent circuit model for state estimation of lithium-ion batteries in electric vehicle applications,” IEEE Access, vol. 13, pp. 197616–197633, 2025, doi: 10.1109/ACCESS.2025.3634374.

[12] M. Gholami, S. A. Mousavi, and S. M. Muyeen, “Enhanced microgrid reliability through optimal battery energy storage system type and sizing,” IEEE Access, vol. 11, pp. 62733–62743, 2023, doi: 10.1109/ACCESS.2023.3288427.

[13] C.-Y. Tang and J.-T. Lin, “Online autonomous specific gravity measurement strategy for lead-acid batteries,” IEEE Sensors Journal, vol. 20, no. 4, pp. 1980–1987, 2020, doi: 10.1109/JSEN.2019.2948778.

[14] S. Li, C. Ye, Y. Ding, Y. Song, and M. Bao, “Reliability assessment of renewable power systems considering thermally-induced incidents of large-scale battery energy storage,” IEEE Transactions on Power Systems, vol. 38, no. 4, pp. 3924–3938, 2023, doi: 10.1109/TPWRS.2022.3200952.

Published

2026-08-31

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