A Digital Twin of Charging Stations for Fleets of Electric Vehicles

Abstract

The increasing concern over the environmental impact of fossil fuels and associated CO2 emissions created a growing interest on the use of electric vehicles (EVs) and green energy utilization. In this context, the widespread adoption of EVs should be accompanied by the introduction of generation from renewable energy sources (RES). That insertion, at the distribution level, presents challenges that result from their intermittent nature, requiring demand-response measures that can be addressed by adjusting the charging processes to match the available power. In the framework of EVs renting companies, it is essential to have an efficient charging management that allows achieving high levels of self-consumption and self-sufficiency, lower operational costs and lower payback periods for the investments made. The utilization of digital twins (DTs) can be key to achieve those goals, providing accurate simulations and predictions. Their use in the context of EV charging can offer valuable insights into optimizing charging scheduling and predicting energy demands, taking into consideration distinct scenarios. This paper presents the work done to implement DTs of a set of charging stations (CSs) and EVs, which allow the modeling and improved management of the charging processes of EV fleets, for a set of CSs, integrating RES. In this charging context, experimental results using the DT were applied considering a predicted mobility. The applied scenarios supported an effective and optimized managing performance, reaching low paybacks and high self-sufficiency values. The obtained results show that this method is a viable and cost-effective solution for companies renting EVs.

Development and Implementation of a Smart Charging System for Electric Vehicles based on the ISO 15118 Standard

“…Through this charge scheduling, and combined with load management algorithms, the charging can be further optimised. Furthermore, future developments aim to integrate the two main modules (EVpi and EVSEpi) in a physical system (that integrates renewable energy sources and local storage), the digital twin of which was introduced in [18]. Additionally, there are plans for implementation…”

Development and Implementation of a Smart Charging System for Electric Vehicles Based on the ISO 15118 Standard

“…Future developments should consider implementing power-line communication over the control pilot line. Also, although the battery model implemented in this work effectively emulates real charging behavior, incorporating more complex models, such as the one used in [30], could enhance the system's realism even further.…”

“…Future developments will focus on addressing the transition from controlled emulation to real-world implementation. Key areas for further research and development include the following: (1) scalability: extending the system to a more comprehensive framework that allows for testing V2G communication dynamics in larger and more complex scenarios, which includes scaling up the number of EVs and EVSEs to evaluate system performance under higher loads and more varied conditions; (2) physical system integration: integrating the EVpi and EVSEpi modules into a physical system that includes RES and batteries, building upon the digital twin introduced in previous research [30], which will involve testing the system's interoperability with actual hardware and ensuring that it meets real-world performance standards; and (3) renewable energy communities: applying the system within the context of renewable energy communities and extending it to also consider electric boats, as explored in previous research on Culatra Island [31,32], which involves assessing the system's effectiveness in supporting sustainable energy practices and community-based energy management.…”

A Digital Twin Framework for Simulating Distributed Energy Resources in Distribution Grids

“…DT technology is crucial in addressing challenges faced by EVs. For instance, the literature highlights the effectiveness of DTs [58] in EV propulsion units [59], accurately estimating SoC [60] and analyzing heavy-duty vehicle aerodynamics and powertrain configurations [61], accurately modelling vehicle behavior during driving cycles [62], optimizing charging schedules and forecasting energy demand under various scenarios [63]. Moreover, the DT is significant in addressing various challenges in the EV, including health monitoring, predictive maintenance, and optimization of energy consumption [64] and charging behavior [65].…”