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Year 2026 · Volume 3 · Issue 1

Review Article

Intelligent Battery Management System for Off-Grid Solar Applications

Banothu Nageshwar1 Sagishetty Charan Raju2 Yenkarla Naveen Kumar3 Tejavath Tarun4 T. Rajeswari5
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: 01-04

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Abstract

Off-grid solar power systems play a significant role in delivering electricity to remote and rural areas where conventional grid infrastructure is unavailable. Battery energy storage systems are essential components of such installations, as they ensure an uninterrupted power supply during non-sunlight hours. However, improper battery usage can result in efficiency losses, safety hazards, and reduced lifespan. An Intelligent Battery Management System (IBMS) addresses these challenges by monitoring and controlling critical battery parameters, including voltage, current, temperature, state of charge, and state of health. By employing intelligent algorithms and real-time control strategies, IBMS enhances battery performance, improves safety, and extends operational life. This article presents a comprehensive study of intelligent battery management systems for off-grid solar applications, covering system architecture, advantages, applications, and future scope.

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References

  • 1. Linden, D., & Reddy, T. B., Handbook of Batteries, McGraw-Hill, 2011.

  • 2. Chen, M., & Rincon-Mora, G. A., IEEE Transactions on Energy Conversion, 2006.

  • 3. Rahimi-Eichi, H. et al., IEEE Industrial Electronics Magazine, 2013.

  • 4. Divya, K. C., & Østergaard, J., Electric Power Systems Research, 2009.

  • 5. Tremblay, O., & Dessaint, L. A., World Electric Vehicle Journal, 2009.

  • 6. J. Zhang et al., “Battery management systems for large-scale energy storage,” Renewable Energy, 2021.

  • 7. M. Chen and G. A. Rincon-Mora, “Accurate electrical battery model,” IEEE Trans. Energy Conversion, 2006.

  • 8. H. He et al., “State-of-charge estimation of lithium-ion batteries,” Applied Energy, 2011.

  • 9. A. Barré et al., “A review on lithium-ion battery ageing mechanisms,” Journal of Power Sources, 2013.

  • 10. X. Hu et al., “Advanced battery management systems,” IEEE Trans. Industrial Electronics, 2019.

  • 11. B. S. Bhangu et al., “State-of-charge estimation using Kalman filters,” IEEE Trans. Vehicular Technology, 2005.

  • 12. Y. Wang et al., “Battery management for solar microgrids,” Energy Procedia, 2017.

  • 13. S. Sepasi et al., “State of health estimation,” IEEE Trans. Power Electronics, 2015.

Citations

Banothu Nageshwar¹, Sagishetty Charan Raju², Yenkarla Naveen Kumar³, Tejavath Tarun⁴, T. Rajeswari⁵, “Intelligent Battery Management System for Off-Grid Solar Applications”, Indian Journal of Electrical and Electronics Engineering, Volume 03, Issue 01, January-April 2026, PP: 01-04.

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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.

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