From Simulation to Sustainable Urban Mobility: A Purpose-Driven Framework for Advanced EV Charging Technology Transfer
Shangqing Wang, Syed Irtaza Haider, Shiwei Shen, Faezeh Motazedian, Rico Radeke, Laura del Rio Carazo, Frank H. P. Fitzek
Abstract
The integration of electric vehicles (EVs) into urban mobility systems offers both opportunities and challenges for cities pursuing sustainability and energy efficiency. While simulation-based approaches have demonstrated the technical feasibility of advanced charging strategies, such as smart charging, bidirectional charging, and Vehicle-to-Grid (V2G), their real-world impact is often constrained by policy, business models, and stakeholder engagement. This paper introduces a purpose-driven technology transfer (PDTT) framework that systematically connects simulation insights with the needs of urban stakeholders. Leveraging a modular simulation architecture based on Simulation of Urban Mobility (SUMO), we evaluate the operational and sustainability impacts of advanced EV charging in complex urban scenarios, assessing indicators such as energy efficiency and CO2 emissions. The PDTT framework guides the translation of simulation results into actionable pathways for deployment, policy design, and market innovation, supporting co-creation with city planners, utilities, and communities. Our findings highlight the value of simulation as a diagnostic and engagement tool, and demonstrate how a multidimensional, stakeholder-centered approach can accelerate the sustainable adoption of advanced EV charging technologies in smart city contexts.
Create a lesson
Related papers
Unmitigated? How are the world's largest companies responding to AI risks?
Alexander K. Saeri, Jess Graham, Michael Noetel et al.
From Guidelines to Practice: Defining Requirements for AI Red-Teaming Tools
Gia Phong Tran, Thai Duong Huynh, Randolph Loh et al.
Harder Than the Task: From Technical to Organizational Difficulty in AI Benchmarking
Victor Barres, Aden Barton, Nicolai J. Foss et al.
Reducing SNAP Payment Errors at Lower Cost With AI: A Simulation Study
Alex Chohlas-Wood
LLMs and the Organization of Scientific Work
Stefano Bianchini, Georgiana-Ioana Coroama
Defining Autonomous Weapon Systems: A schematic framework for refined purpose-driven definitions
Björn Lundgren, Nathan G. Wood