Modelagem digital como ferramenta de apoio à eficiência energética e roteirização para veículos elétricos inteligentes

Abstract

The growing demand for sustainable solutions in urban mobility has driven the development of Intelligent Electric Vehicles (IEVs) and strategies aimed at optimizing energy consumption. However, factors such as limited range, scarcity of charging stations, and variability in traffic conditions still pose significant obstacles to the widespread adoption of these vehicles. In this context, this work investigates the application of Digital Twins (DTs) as a runtime decisionsupport tool for dynamic route planning and energy monitoring of intelligent eletric vehicles (IEVs). Initially, a bibliometric analysis was conducted using the Web of Science database, with the aim of assessing the relevance of the topic in the global scenario and gaining a better understanding of potential joint applications. As a result, the research identified 4.209 documents, of which 206 include the term digital twin as a keyword. The construction of the cooccurrence map revealed five main thematic clusters, highlighting the terms electric vehicles, energy efficiency, autonomous vehicles, and digital twin as central to the analyzed corpus. In the experimental stage, a simulation-based application was developed and integrated with SUMO, coupled to the TraCI (Traffic Control Interface) interface and programmed in Python. In this setup, the digital twin logic continuously monitors operational variables such as speed, acceleration, and battery state-of-charge. At each simulation step, decisions such as route recalculation and redirection to charging points are made dynamically based on road network conditions and the available energy. The results obtained demonstrate the feasibility of using DTs to mitigate "range anxiety", improve real-time energy efficiency, and foster greater acceptance of electric vehicles in urban mobility.