- Home
- Archives
- 2026
- Vol. 3 Issue 1
- Article
AI-Enhanced Battery Monitoring and Control in Standalone Solar Power Systems
Authors
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.
Article Information
- DOI
- No Doi
- Published In
- Year 2026 · Volume 3 · Issue 1
- Published Online
- January-April 2026
- Pages
- 18-20
- Article Type
- Review Article
- Check for updates
-
Abstract
View PDFBattery 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.
Related Articles in this Issue
Share Article
*Instagram doesn't support direct link sharing from web. Copy the link and share it in your Instagram story or post.