Predictive Maintenance of VRLA Batteries in BTS Systems

Aug 1, 2025

This undergraduate project was developed in collaboration with Nian Electronic Company to implement predictive maintenance for Valve-Regulated Lead-Acid (VRLA) batteries in BTS systems. The project implements the methodology from the research paper “Predictive Maintenance of VRLA Batteries in UPS towards Reliable Data Centers”using a Gradient Boosted Decision Trees (GDBT) model.

The system predicts VRLA battery degradation in advance for the company’s BTS systems, helping prevent unexpected failures and optimize maintenance scheduling. Key features include:

  • Battery Health Monitoring: Real-time assessment of battery condition using performance metrics
  • Deterioration Prediction: Early warning system for battery replacement needs
  • Cost Optimization: Reduced downtime and extended battery lifespan through intelligent maintenance

The implementation processes battery voltage, internal resistance, temperature data, and historical maintenance records to provide accurate predictions for critical infrastructure applications.

Machine Learning VRLA Batteries Classification