A method for determining the operability (SOH) and residual resource (rul) of an electric vehicle traction battery based on a neural network approach
Abstract
There is an objective problem of determining the current operability (SOH) and residual resource (RUL) of an electric vehicle traction battery, which is significantly influenced by external factors: ambient temperature, terrain, road surface assessment, charging technology, driver qualifications. This article discusses methods for determining the residual resource (RUL) and current operability (SOH) of an electric vehicle traction battery. A method for determining the operability (SOH) and residual resource (RUL) of an electric vehicle traction battery based on a neural network approach, taking into account external factors, is proposed.
About the Authors
Y. N. Katsuba
Empress Catherine II Saint Petersburg Mining University
Russian Federation
Russian Federation
Candidate of Technical Sciences, Associate Professor
M. E. Kochegarov
Empress Catherine II Saint Petersburg Mining University
Russian Federation
Russian Federation
Postgraduate Student
References
1. Сегмент SUV захватил лидерство в парке электрокаров и гибридов [Электронный ресурс] // Автостат: сайт. – URL: https://www.autostat.ru/infographics/58503/ (дата обращения: 15.04.2025)
2. В России зарегистрировано 90 тысяч электрокаров и гибридов [Электронный ресурс] // Автостат: сайт. – URL: https://www.autostat.ru/infographics/58435/ (дата обращения: 15.04.2025)
3. Продажи электромобилей в России [Электронный ресурс] // Автостат: сайт. – URL: https://www.autostat.ru/infographics/57785/ (дата обращения: 15.04.2025)
4. Sadegh Kouhestani, H., Yi, X., Qi, G., Liu, X., Wang, R., Gao, Y., Yu, X., & Liu, L. (2022). Prognosis and Health Management (PHM) of Solid-State Batteries: Perspectives, Challenges, and Opportunities. Energies, 15(18), 6599. https://doi.org/10.3390/en15186599
5. Zhao, J., Zhu, Y., Zhang, B., Liu, M., Wang, J., Liu, C., & Hao, X. (2023). Review of State Estimation and Remaining Useful Life Prediction Methods for Lithium–Ion Batteries. Sustainability, 15(6), 5014. https://doi.org/10.3390/su15065014
6. Akbar, K., Zou, Y., Awais, Q., Baig, M. J. A., & Jamil, M. (2022). A Machine Learning-Based Robust State of Health (SOH) Prediction Model for Electric Vehicle Batteries. Electronics, 11(8), 1216. https://doi.org/10.3390/electronics11081216
7. Huang, S.-C., Tseng, K.-H., Liang, J.-W., Chang, C.-L., & Pecht, M. G. (2017). An Online SOC and SOH Estimation Model for Lithium-Ion Batteries. Energies, 10(4), 512. https://doi.org/10.3390/en10040512
8. X. Cui and T. Hu, "State of Health Diagnosis and Remaining Useful Life Prediction for Lithium-ion Battery Based on Data Model Fusion Method," in IEEE Access, vol. 8, pp. 207298-207307, 2020, https://doi.org/10.1109/ACCESS.2020.3038182
9. Ming-Feng Ge, Yiben Liu, Xingxing Jiang, Jie Liu, A review on state of health estimations and remaining useful life prognostics of lithium-ion batteries, Measurement, 174, 2021, 109057, ISSN 0263-2241, https://doi.org/10.1016/j.measurement.2021.109057
10. Kocsis Szürke, S., Sütheö, G., Apagyi, A., Lakatos, I., & Fischer, S. (2022). Cell Fault Identification and Localization Procedure for Lithium-Ion Battery System of Electric Vehicles Based on Real Measurement Data. Algorithms, 15(12), 467. https://doi.org/10.3390/a15120467
11. Natthida Sukkam, Thossaporn Onsree, Nakorn Tippayawong; Overview of machine learning applications to battery thermal management systems in electric vehicles. AIP Conf. Proc. 17 November 2022; 2681 (1): 020004. https://doi.org/10.1063/5.0115829
Review
For citations:
Katsuba Y.N., Kochegarov M.E. A method for determining the operability (SOH) and residual resource (rul) of an electric vehicle traction battery based on a neural network approach. Social-economic and technical systems: research, design and optimization. 2025;(3):52-59. (In Russ.)
Views:
21
Email this article 