Development of a method and software tool to extend the service life of solar batteries

Abstract

This study focuses on developing a novel method and software tool designed to increase the service life of solar batteries by optimizing their operation under various environmental conditions. The proposed method involves the integration of predictive analytics to monitor and adjust critical parameters affecting battery degradation, such as temperature and charge cycles. The software tool can predict optimal operating conditions and suggest maintenance actions by employing machine learning algorithms and real-time data processing, reducing wear and improving efficiency. The experimental validation results demonstrate a significant improvement in battery longevity, extending the service life by up to 20% under standard operating conditions. These findings have practical implications for renewable energy systems, particularly in enhancing the cost-effectiveness and sustainability of solar power installations.