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Review Article

AI-Enhanced Battery Monitoring and Control in Standalone Solar Power Systems

Badavath Gandhi1 Bhattu Divya Teja Sri2 Maddela Sai Kumar3 Budha Vijay Kumar4 Ch. Rajasri5

1 2 3 4 UG Scholar, Department of Electrical and Electronics Engineering, Holy Mary Institute of Technology and Science, Hyderabad, Telangana, India. 5 Assistant Professor, Department of Electrical and Electronics Engineering, Holy Mary Institute of Technology and Science, Hyderabad, Telangana, India.

Published Online: January-April 2026

Pages: 18-20

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Abstract

Battery systems are critical to the reliability and performance of standalone solar power installations. Inefficient battery monitoring and control can lead to premature battery failure, energy losses, and increased operational costs. This article explores the integration of artificial intelligence (AI) for enhanced battery monitoring and control in standalone solar systems, emphasizing improved accuracy, predictive maintenance, and optimized energy utilization. By reviewing recent research, we highlight the evolution of battery management strategies and the transformative impact of machine learning and intelligent control frameworks. The study outlines practical implementations, discusses current challenges, and proposes directions for future research.

References

  • 1. Zhang, Y., Lin, X., & Wang, J. (2023). Machine learning for Battery State of Charge estimation: A review. Renewable Energy Journal,

  • 15(2), 101–119.

  • 2. Kumar, A., & Singh, P. (2022). Predictive maintenance of solar battery systems using AI algorithms. International Journal on Energy

  • Systems, 27(4), 385–402.

  • 3. Li, D., & Chen, H. (2021). Reinforcement learning in energy storage control for standalone photovoltaic systems. IEEE Transactions on

  • Sustainable Energy, 12(1), 25–34.

  • 4. Mahmood, F., & Malik, K. (2020). Hybrid fuzzy-AI based controllers for battery management. Journal of Intelligent Systems, 34(9), 1137–

  • 1155.

  • 5. Patel, R., & Sharma, V. (2023). Digital twin applications in renewable energy storage systems. Energy Informatics Review, 8(1), 45–63.

  • 6. Santos, J., & Pereira, L. (2021). IoT enabled battery monitoring for decentralized solar installations. Sensors and Systems Journal, 19(3),

  • 248–267.

  • 7. Lee, S., & Kim, J. (2022). AI-based thermal management in lithium-ion batteries for solar applications. Journal of Thermal Engineering,

  • 16(7), 890–906.

  • 8. Cheng, Z., & Wu, Q. (2020). Solar irradiance forecasting using deep learning for energy storage optimization. Renewable Forecasting Letters,

  • 11(5), 76–92.

  • 9. Roy, M., & Das, R. (2023). Fault diagnosis in solar battery systems using classification algorithms. Journal of Power and Energy

  • Engineering, 10(2), 55–69.

  • 10. Oliveira, T., & Silva, M. (2021). Adaptive load management for enhanced battery life in off-grid systems. Energy and Buildings, 224, 110–

  • 124.

  • 11. Nguyen, H., & Tran, T. (2022). Neural network approaches for state of health monitoring in photovoltaic battery systems. Energy Reports,

  • 8, 1450–1462.

  • 12. Gupta, S., & Shah, R. (2020). A comparison of AI models for standalone solar energy optimization. International Journal of Renewable

  • Energy Research, 10(1), 54–63.

  • 13. Fernández, P., & García, L. (2023). Data-driven analytics for battery health prediction. Sustainable Energy Analytics, 5(1), 23–47.

  • 14. Hussein, A., & Youssef, M. (2021). IoT and AI integration for decentralized energy storage systems. Journal of Distributed Energy Systems,

  • 7(4), 97–115.

  • 15. Dutta, K., & Pal, S. (2022). Intelligent control strategies for off-grid solar storage: A review. Journal of Clean Energy Technologies, 10(3),

  • 232–249.

Citations

Badavath Gandhi, Bhattu Divya Teja Sri, Maddela Sai Kumar, Budha Vijay Kumar, Ch. Rajasri, “AI-Enhanced Battery Monitoring and Control in Standalone Solar Power Systems”, Indian Journal of Electrical and Electronics Engineering, Volume 03, Issue 01, January-April 2026, PP: 18-20.

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Licensing

© 2026 The Author(s). Published by Fifth Dimension Research Publication.

This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0/ ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent.