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

Author

Listed:
  • Matthew Busayo Olanrewaju

  • Hazzan Adedapo Aderinko

  • Cornelius Chukwukjekwu Okafor

  • Emmanuel Ayodeji Afolabi

  • Christopher Nwabuwa

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.

Suggested Citation

  • Matthew Busayo Olanrewaju & Hazzan Adedapo Aderinko & Cornelius Chukwukjekwu Okafor & Emmanuel Ayodeji Afolabi & Christopher Nwabuwa, 2026. "De-Risking Solar-Powered EV Charging Infrastructure Projects Through AI Enabled Adaptive Project Management Framework: A Monte Carlo Simulation Approach," International Journal of Innovative Science and Research Technology (IJISRT), IJISRT Publication, vol. 11(09), pages 341-358, September.
    • Handle: RePEc:cvr:ijisrt:2026:09:ijisrt26sep365
    DOI: https://doi.org/10.38124/ijisrt/26sep365
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