Digital Twin-Based Predictive Maintenance for EV Charging Networks

Abstract

Electric vehicle charging networks are becoming large, connected infrastructures where charger failures, component degradation, and service interruptions affect reliability and user access. Digital twins have shown value in synchronized monitoring, virtual state tracking, and intelligent asset supervision, but predictive maintenance architectures for charging networks remain limited. This matters because conventional maintenance based on periodic inspection or reactive repair often misses early deterioration under variable electrical, thermal, and communication stress. This article presents a digital twin architecture for predictive maintenance of EV charging networks by integrating physical-virtual synchronization, subsystem health indexing, degradation analysis, remaining useful life estimation, and maintenance priority scheduling. The framework identifies charger faults before critical failure and supports timely intervention while preserving service continuity. Results show strong fault prediction and useful life estimation, along with reduced response time, lower downtime, and improved network availability. The study shows that digital twin architectures can shift charger maintenance from reactive service to predictive, network-aware reliability management.