ARCHIVES
Original Article
Battery Performance Monitoring and Control Using Machine Learning
Gaana H1
Harsha K2
Nandan Kumar M S3
Vishwas N Gowda4
Punyashekar5
1 Assistant professor, Department of Electrical and Electronics Engineering, PES College of Engineering, Mandya, Karnataka, India. 2 3 4 5 Student, Department of Electrical and Electronics Engineering, PES College of Engineering, Mandya, Karnataka, India.
Published Online: May-June 2026
Pages: 207-216
Cite this article
↗ https://www.doi.org/10.59256/ijire.20260703020
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10.1016/j.energy.2024.132048.
15. J. Du, C. Zhang, S. Li, L. Zhang, and W. Zhang, “Aging abnormality detection of lithium-ion batteries combining feature engineering
and deep learning,” Energy, vol. 297, Art. no. 131276, 2024, doi: 10.1016/j.energy.2024.131276.
16. S. Rout, S. K. Samal, D. J. Gelmecha, and S. Mishra, “Estimation of state of health for lithium-ion batteries using advanced data-driven
techniques,” Scientific Reports, vol. 15, Art. no. 30438, 2025, doi: 10.1038/s41598-025-93775-y.
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18. A. Lanubile et al., “Domain knowledge-guided machine learning framework for state of health estimation in Lithium-ion batteries,”
Communications Engineering, vol. 3, Art. no. 168, 2024, doi: 10.1038/s44172-024- 00304-2.
19. C. Armenta-Deu, “Online determination of state of charge in lithium-ion batteries: influence of the state of health,” Frontiers in Energy
Research, vol. 13, Art. no. 1573972, 2025, doi: 10.3389/fenrg.2025.1573972.
20. M. Li et al., “State of Health Estimation and Battery Management: A Review of Health Indicators, Models and Machine Learning,”
Materials, vol. 18, no. 1, Art. no. 145, 2025, doi: 10.3390/ma18010145.
Dataset considerations, algorithmic approaches, and future trends,” Journal of Energy Storage, vol. 101, Art. no. 113827, 2024, doi:
10.1016/j.est.2024.113827.
2. Y. Shang, S. Wang, N. Tang, Y. Fu, and K. Wang, “Research progress in fault detection of battery systems: A review,” Journal of
Energy Storage, vol. 98, Art. no. 113079, 2024, doi: 10.1016/j.est.2024.113079.
3. M. Jiang et al., “Advances in battery state estimation of battery management system in electric vehicles,” Journal of Power Sources,
vol. 612, Art. no. 234781, 2024, doi: 10.1016/j.jpowsour.2024.234781.
4. O. Ali et al., “Comprehensive review of battery management systems for electric vehicles: Thermal management, c h a r g i n g
s t r a t e g i e s , and emergingtechnologies,” Journal of Power Sources, vol. 658, Art. no. 238269, 2025, doi:
10.1016/j.jpowsour.2025.238269.
5. G. Krishna, R. Singh, A. Gehlot, V. A. Shaik, B. Twala, and N. Priyadarshi, “IoT-based real-time analysis of battery management system
with long range communication and FLoRa,” Results in Engineering, vol. 23, Art. no. 102770, 2024, doi:
6. 10.1016/j.rineng.2024.102770.
7. A. Gozuoglu, “IoT-enhanced battery management system for real-time SoC and SoH monitoring using STM32-based programmable
electronic load,” Internet of Things, vol. 30, Art. no. 101509, 2025, doi: 10.1016/j.iot.2025.101509.
8. M. G. M. Abdolrasol et al., “Advanced data-driven fault diagnosis in lithium-ion battery management systems for electric vehicles:
Progress, challenges, and future perspectives,” eTransportation, vol. 22, Art. no. 100374, 2024, doi: 10.1016/j.etran.2024.100374.
9. N. Gnanasekar et al., “Battery fault diagnosis methods for electric vehicle lithium-ion batteries: Correlating codes and battery
management system,” Process Safety and Environmental Protection, vol. 196, Art. no. 106919, 2025, doi:
10.1016/j.psep.2025.106919.
10. H. Li, H. Shaukat, R. Zhu, M. B. Kaleem, and Y. Wu, “Fault Detection of Li-Ion Batteries in Electric Vehicles: A Comprehensive
Review,” Sustainability, vol. 17, no. 14, Art. no. 6322, 2025, doi: 10.3390/su17146322.
11. J. Zhang and K. Li, “State-of-Health Estimation for Lithium-Ion Batteries in Hybrid Electric Vehicles—A Review,” Energies, vol. 17,
no. 22, Art. no. 5753, 2024, doi: 10.3390/en17225753.
12. K. Tang et al., “The State of Health Estimation of Lithium-Ion Batteries: A Review of Health Indicators, Estimation Methods,
Development Trends, and Challenges,” World Electric Vehicle Journal, vol. 16, no. 8, Art. no. 429, 2025, doi: 10.3390/wevj16080429.
13. Tripp-Barba, J. A. Aguilar-Calderón, L. Urquiza-Aguiar, A. Zaldívar-Colado, and A. Ramírez-Noriega, “A Systematic Mapping Study
on State Estimation Techniques for Lithium-Ion Batteries in Electric Vehicles,” World Electric Vehicle Journal, vol. 16, no.
2, Art. no. 57, 2025, doi: 10.3390/wevj16020057. Y. Yan et al., “Fault diagnosis of lithium-ion battery sensors based on multi-method
fusion,” Journal of Energy Storage, vol. 85, Art. no. 110969, 2024, doi: 10.1016/j.est.2024.110969.
14. D. Liu, S. Wang, Y. Fan, C. Fernandez, and F. Blaabjerg, “An optimized multi-segment long short-term memory network strategy for
power lithium-ion battery state of charge estimation adaptive wide temperatures,” Energy, vol. 304, Art. no. 132048, 2024, doi:
10.1016/j.energy.2024.132048.
15. J. Du, C. Zhang, S. Li, L. Zhang, and W. Zhang, “Aging abnormality detection of lithium-ion batteries combining feature engineering
and deep learning,” Energy, vol. 297, Art. no. 131276, 2024, doi: 10.1016/j.energy.2024.131276.
16. S. Rout, S. K. Samal, D. J. Gelmecha, and S. Mishra, “Estimation of state of health for lithium-ion batteries using advanced data-driven
techniques,” Scientific Reports, vol. 15, Art. no. 30438, 2025, doi: 10.1038/s41598-025-93775-y.
17. H. Liu et al., “Multi-modal framework for battery state of health evaluation using open-source electric vehicle data,” Nature
Communications, vol. 16, Art. no. 1137, 2025, doi: 10.1038/s41467-025-56485-7.
18. A. Lanubile et al., “Domain knowledge-guided machine learning framework for state of health estimation in Lithium-ion batteries,”
Communications Engineering, vol. 3, Art. no. 168, 2024, doi: 10.1038/s44172-024- 00304-2.
19. C. Armenta-Deu, “Online determination of state of charge in lithium-ion batteries: influence of the state of health,” Frontiers in Energy
Research, vol. 13, Art. no. 1573972, 2025, doi: 10.3389/fenrg.2025.1573972.
20. M. Li et al., “State of Health Estimation and Battery Management: A Review of Health Indicators, Models and Machine Learning,”
Materials, vol. 18, no. 1, Art. no. 145, 2025, doi: 10.3390/ma18010145.
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