Lead Acid Battery Optimization and Fault Prediction using IoT

Lead-acid batteries play a crucial role in various applications, including renewable energy storage, automotive systems, and uninterruptible power supplies. However, their efficiency and lifespan are significantly affected by operational conditions and timely fault detection. This research presents an IoT-based optimization and fault prediction system for lead-acid batteries using the VL53L optical sensor. The proposed system continuously monitors critical battery parameters, including electrolyte levels and temperature, using the VL53L sensor and other auxiliary sensors. The collected data is transmitted to an IoT platform for real-time analysis, fault detection, and predictive maintenance. Machine learning algorithms are implemented to identify patterns and predict potential failures, enabling proactive maintenance and improving battery performance. The system enhances battery reliability, reduces downtime, and optimizes overall efficiency. Experimental results demonstrate the effectiveness of the proposed approach in extending battery life and minimizing unexpected failures. This work contributes to sustainable energy management by integrating IoT and predictive analytics for lead-acid battery monitoring.