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Study of Battery State-of-charge Estimation with kNN Machine Learning Method
Abstract
Electric vehicles have high demand due to their ecofriendly nature. From this point of view, lithium batteries have gained high attention in recent days due to their high efficiency and long life time. Hence, it is of the utmost importance to evaluate the battery characteristics, such as the state of charge (SOC), depth of discharge (DOD), and remaining life of a battery to ensure battery safety. These parameters were derived in order to estimate the battery life time before degradation. This estimation is very much required in making a decision about battery usage in the future. In this study, the SOC of a lithium polymer battery was evaluated in a real-time experiment. Charging and discharging cycles were done, and we obtained the voltage, current, and time data from the experimental result. This experimental data trained machine learning methods such as the kNN (k Nearest Neighbor) method to estimate the SOC more precisely. After training the model, a test was done. The proposed estimator was calibrated by experimental data. The results are satisfactory with accuracy of 98% and mean absolute error (MAE) as low as 0.74[%].
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K-nearest neighbors has demonstrated notable accuracy and adaptability in SOC estimation for lithium-ion batteries.
“KNN has demonstrated notable accuracy and adaptability in SOC estimation for LIBs.”
From Advancements in the estimation of the state of charge of lithium-ion battery: a comprehensive review of traditional and deep learning approaches (2025) · cites this paper as [14]
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El-sayed, E. I., Elsayed, S. K., & Mohammad, A. (2024) · Sustainability · doi:10.3390/su16219301 · Cited by 40
Zhang, X., Zhang, L., Wu, J., Bai, W., Dai, H., Lin, H., Zhang, F., & Yang, Y. (2025) · Journal of Electroanalytical Chemistry · doi:10.1016/j.jelechem.2025.119093 · Cited by 37
Wu, Y., Bai, D., Zhang, K., Li, Y., & Yang, F. (2025) · Journal of Materials Informatics · doi:10.20517/jmi.2024.84 · Cited by 20
Touzout, W., Chikh, M. A. A., Gougam, F., Afia, A., Bouchikh, M. E.-A., & Tedjani, M. R. (2025) · Electrical Engineering · doi:10.1007/s00202-025-02997-w · Cited by 6
Design of Backpropagation Neural Network for Aging Estimation of Electric Battery
Shin, K. J. (2023) · Sensors and Materials · doi:10.18494/sam4181 · Cited by 3
Self-citationDevelopment of an Energy Consumption Minimization Strategy for a Series Hybrid Vehicle
Göl, M. F., Baba, A. F., & Hartavi, A. E. (2025) · World Electric Vehicle Journal · doi:10.3390/wevj16070383 · Cited by 1
Kuloglu, A., Avsar, R., Tetik, T., & Konar, M. (2026) · Engineering Science and Technology an International Journal · doi:10.1016/j.jestch.2026.102322
Khedhiri, M., Slama, R., Redondo-Iglesias, E., & Trigui, R. (2026) · Batteries · doi:10.3390/batteries12060185
“To overcome these limitations, machine learning (ML) techniques have been introduced to learn the relationship between measurable signals such as voltage, current, and temperature and the SoC directly from data [14].”
Metadata
- Authors
- Talluri, T., Chung, H. T., & Shin, K.
- Venue
- IEIE Transactions on Smart Processing & Computing
- Published
- 2021-12-31
- Volume
- 10
- Issue
- 6
- Pages
- 496-504
- ISSN
- 2287-5255 (ISSN-L)
- OpenAlex
- W4206158236
- Cited by
- 8
- References
- 0
- Citations by year
- 2026: 2 · 2025: 4 · 2024: 1 · 2023: 1
- FWCI
- 0.16
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEIE Transactions on Smart Processing and Computing (publishedVersion)
- Topics
- Physical Sciences › Engineering › Automotive Engineering › Battery Modeling and Management
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advancements in Battery Materials
- Physical Sciences › Engineering › Automotive Engineering › Electric and Hybrid Vehicle Technologies
- Keywords
- state of charge estimation, lithium polymer batteries, k-nearest neighbors, electric vehicles, depth of discharge, battery degradation, charging and discharging cycles, battery safety
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