AI-driven Optimization of Urban EV Charging Infrastructure: A Structured Review and Conceptual Framework for Climate-Smart Regions
This presentation was delivered at the Smart Cities in Smart Regions Conference 2026 in Lahti, Finland. It examines how artificial intelligence can support urban EV charging infrastructure planning by translating predictive and optimization models into practical infrastructure decisions. The presentation introduces a framework linking data, AI and analytics, system constraints, planning decisions, and outcomes, with governance and risk thresholds as cross-cutting elements. A Finnish real-world EV fast-charging case study illustrates risk-aware grid connection planning using charging data and Monte Carlo simulation. The presentation further discusses how AI can extend static risk estimates toward more context-aware, scalable, and adaptive decision support for cities, distribution system operators, charging operators, and EV users.
Authors
- Hesam Vahib
Institutions
- Lappeenranta-Lahti University of Technology (FI)
Publication Details
- Journal
- Zenodo (CERN European Organization for Nuclear Research)
- Published
- 2026-09-24
- DOI
- https://doi.org/10.5281/zenodo.22944343
- Primary Topic
- Electric Vehicles and Infrastructure
- Type
- article
- Field-Weighted Citation Impact
- 0.00