De-Risking Solar-Powered EV Charging Infrastructure Projects Through AI Enabled Adaptive Project Management Framework: A Monte Carlo Simulation Approach

Abstract

The rapid global transition toward sustainable transportation requires the mass deployment of Solar-Powered Electric Vehicle Charging Infrastructure (SPEVCI). However, the integration of localized photovoltaic arrays, high-capacity battery storage, and high-voltage charging terminals transforms these installations into highly complex socio-technical systems. Traditional, deterministic project management methodologies—such as the Critical Path Method (CPM)—rely on static planning and consistently fail to manage the dynamic supply chain volatility, weather delays, and cross-disciplinary dependencies inherent to SPEVCI deployment. This paper proposes a novel, hybridized methodology: the Dynamic Lifecycle and AI-BIM Integration (DLAI) framework. By synthesizing Predictive Agile-AI (PA-AI), AI-Enhanced Systems Engineering (AESE), and Digital Twin spatial computing, the DLAI framework operates as a continuous, intelligent data ecosystem. It orchestrates spatial simulation, predictive execution routing, and agile telemetry through an IoT-enabled centralized feedback loop.