Predictive Maintenance Optimization for Electric Vehicle Charging Infrastructure Using Bayesian Hyperparameter Optimization and Real-Time Sensor Fusion

**Abstract:** This paper presents a novel methodology for predicting and preventing failures in electric vehicle (EV) charging infrastructure through a real-time predictive maintenance (RPM) system. Utilizing a Bayesian Hyperparameter Optimization (BHPO) framework coupled with sensor fusion from a network of EV charging stations, we develop a robust predictive model capable of anticipating component failures up to 72 hours in advance. The system demonstrably improves uptime, reduces maintenance costs, and enhances the overall reliability of EV charging networks, addressing a critical bottleneck for widespread EV adoption. The core innovation lies in dynamically adapting model hyperparameters based on real-time failure patterns and operating conditions, surpassing the performance limitations of traditional static maintenance schedules and rule-based systems.