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


Authors : Matthew Busayo Olanrewaju; Hazzan Adedapo Aderinko; Cornelius Chukwukjekwu Okafor; Emmanuel Ayodeji Afolabi; Christopher Nwabuwa

Volume/Issue : Volume 11 - 2026, Issue 9 - September

Google Scholar : https://tinyurl.com/3mbwnnnp

DOI : https://doi.org/10.38124/ijisrt/26sep365

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ResearchGate

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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.

Keywords : SPEVCI; Project Management; Charging Infrastructure; AI.

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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.

Keywords : SPEVCI; Project Management; Charging Infrastructure; AI.

Paper Submission Last Date
30 - September - 2026

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