Intelligent Predictive Maintenance for Urban Energy and Transportation Systems: A Hybrid AI Approach

Abstract

Today's complex urban energy and transportation systems demand new maintenance solutions to keep them running properly. This study develops an AI-driven predictive maintenance solution for electrical substations and HEV batteries using data from the Internet of Things sensors. Our framework uses machine-learning methods such as Bi-LSTM, GRU, and GBT models to spot system weaknesses with higher accuracy. Based on test results Bi-LSTM proved better than other models by achieving a 91% F1 score alongside 4.3% Mean Absolute Error across predictions and anomaly detection. According to the results, the proposed framework lowered maintenance costs by half and proved better than traditional and recent methods. The proposed system combines insights from power substations and develops edge-cloud technologies to better use EV batteries. Real-world systems data validate those reductions in downtime happen together with better system reliability. This system now works in cities, tracks vehicle fleets, and supports smart city construction. The predictive system framework delivers exceptional energy and mobility management while remaining affordable and expandable for future urban infrastructure solutions.