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2025 Vol.17, Issue 4 Preview Page
31 December 2025. pp. 92-97
Abstract
References
1

World Economic Forum, 2025, “Homepage,” https://www.weforum.org.

2

Ministry of Land, Infrastructure and Transport Statistic System, 2025, “Statistics Portal,” https://stat.molit.go.kr.

3

Bilfinger, P., et al., 2025, “Why We Need a Standardized State of Health Definition for Electric Vehicle Battery Packs - A Proposal for Energy- and Capacity-Based Metrics,” arXiv.org.

10.1038/s44406-025-00010-8
4

Hsu, Y. C., Wang, P., and W. F. Wu, 2023, “State of Health Prediction and Reliability Analysis of Li-ion Battery,” IEOM Society International, pp. 1376~1377.

10.46254/AN13.20230409
5

Santolaya, M. E., Pous, A. M., Casals, L. C., Corchero, C., and Eichman, J., 2024, “Quantifying the Impact of Battery Degradation in Electric Vehicle Driving through Key Performance Indicators,” Batteries, Vol. 10, No. 4, pp. 8199~8212.

10.3390/batteries10030103
6

Park, J. S., Kim, D. G., Kim, Y. D., and Han, C. P., 2022, “A Study on the Benefits Estimation Derived from Vehicle Inspection Scheme,” Transactions of KSAE, Vol. 30, No. 11, pp. 909~916.

10.7467/KSAE.2022.30.11.909
7

Doose, S., Hahn, A., Fischer, S., Müller, J., Haselrieder, W., and Kwade, A. 2023, “Comparison of the consequences of state of charge and state of health on the thermal runaway behavior of lithium ion batteries,” Journal of Energy Storage, Vol. 62, pp. 106837~106837.

10.1016/j.est.2023.106837
8

Joshi, T., Leon, C., Snyder, M., Sankuratri, A., and Raza, M., 2024, “Analyzing Internal Resistance in Lithium Nickel Cobalt Oxide Vehicle Batteries for Enhanced SOC and SOH Prediction,” pp. 1113~1118.

10.1109/SoutheastCon52093.2024.10500138
9

Madani, S. S., Ziebert, C., Vahdatkhah, P., and Sadrnezhaad, S. K., 2024, Recent Progress of Deep Learning Methods for Health Monitoring of Lithium-Ion Batteries, Vol. 10, Iss: 6, pp. 204~204.

10.3390/batteries10060204
10

Alamin, K. S. S., Chen, Y., Macii, E., Poncino, M., and Vinco, S., 2025, “Advancing Electric Vehicle Battery Management: A Data-Driven Digital Twin Approach for Real-Time Monitoring and Performance Enhancement,” IEEE Transactions on Vehicular Technology, pp. 1~15.

10.1109/TVT.2025.3565907
11

Park, S. J., Kang, B. J., Kim, W., Kang, M., Lim, C., and Hong, Y., 2024, “Electric Vehicle Battery Lifespan Diagnosis Technology Based on Resistance Trends Under Real-World Operating Conditions,” Vol. MA2024-02, Issue 3, pp. 390~390.

10.1149/MA2024-023390mtgabs
12

Zhang, Z., Wang, S., Lin, N., Wang, Z., and Liu, P., 2023, “State of Health Estimation of Lithium-Ion Batteries in Electric Vehicles Based on Regional Capacity,” Vol. 15, Issue 3, pp. 2052~2052.

10.3390/su15032052
13

Xiao Y, Shi X, Li X, Duan Y, Li X, Zhang J, et al., 2024, “Machine Learning Applied to Lithium‐Ion Battery State Estimation for Electric Vehicles: Method Theoretical, Technological Status, and Future Development. Energy storage,” Energy storage.

10.1002/est2.70080
Information
  • Publisher :Korean Auto-vehicle Safety Association
  • Publisher(Ko) :한국자동차모빌리티안전학회
  • Journal Title :Journal of Auto-vehicle Safety Association
  • Journal Title(Ko) :자동차안전학회지
  • Volume : 17
  • No :4
  • Pages :92-97
  • Received Date : 2025-11-10
  • Accepted Date : 2025-11-17