Publications
A Study of Charge Point Infrastructure Policies on EV Driver Satisfaction
Abstract: This paper presents a simulation approach to assess the impact of changes to the charge point infrastructure and policies on Electric Vehicle (EV) user satisfaction, combining both market drivers with the practicalities of EV usage. An agent-based model (ABM) approach is developed where a large number of EVs, that represent the user population, drive within a region of interest. By simulating the driver’s response to their charging experience, the model allows large scale trends to emerge from the population to guide infrastructure policies as the number of EVs increases beyond the initial early adopter market.
The model incorporates a Monte Carlo approach to generate EV and driver agent instances with distinct characteristics, including battery size, vehicle type, driving style, sensitivity to range. The driver model is constructed to respond to events that may increase range anxiety, e.g. increasing the likelihood of charging as the driver becomes more anxious.
A charge point infrastructure and EV population scenario is simulated, including a queuing system for charge stations. The impact on EV driver satisfaction of new policies, including the number of charge points, power rating of charge points, pricing models, green energy providers and numbers of EVs is simulated. The driver satisfaction is assessed by combining a number of metrics, e.g. range anxiety metric, time spent in queues through to access to the desired brand or green energy.
As the number of EVs increase, the policies need to focus on the efficient use of existing charge points to maintain customer satisfaction. The study uses the results to consider the balance between the minimum requirements and value enhancing requirements for customer satisfaction.
Authors: Fussey, P., Akin-Onigbinde, A., and Skarvelis-Kazakos, S.
Enhancing EV user experience in eCharge4Drivers project: the case of the metropolitan city of Bari
Abstract: The Electric Vehicles (EVs) are progressively diffusing around the world aiming at substituting the internal combustion engine (ICE) vehicles in the next years. To make it reliable and close the market of ICE vehicles by 2035 according to the 2050 decarbonization objectives, in Europe, several initiatives and research and innovation programme are ongoing. In the context of the H2020 EU eCharge4Drivers project1, in a usercentric perspective, this paper presents enhanced services to improve the charging and traveling experience of EV end users. The case study of the Metropolitan City of Bari (Italy) is considered in which the pilot demonstrations are based on the use of enhanced and interoperable services such as booking, route planning and new incentives schemes. The preliminary results of the pilot tests are described as well as the a-priori user’s perception of the proposed solutions using the Kano model analysis.
Authors: Michele Roccotelli, Daniele Pagano, Gaetano Volpe, & Maria Pia Fanti.
The barriers to widespread adoption of vehicle-to-grid: A comprehensive review
Abstract: The remarkable growth of electric vehicles (EV) popularity in recent years has significantly pushed the EV charging demand, potentially causing significant pressure on the electricity grid. The Vehicle-to-Grid (V2G) technology, allowing for bidirectional energy flow between the EV battery and the grid, is able to mitigate this pressure, by unlocking the energy flexibility, inherently available in the EV battery. However, despite its potential, V2G technology has not yet reached the mass market due to a number of barriers hampering its adoption. The current study has identified and analyzed 23 barriers potentially hampering the adoption of V2G technology, including technical, business, and EV user-related challenges. The results of the performed analysis define the risk of non-adoption of V2G technology associated with every barrier, along with the interrelations between the barriers. These research findings allow to understand the relative importance of the barriers, structuring and facilitating the necessary efforts of the involved stakeholders toward the adoption of V2G technology.
Authors: Andrei Goncearuc, Cedric De Cauwer, Nikolaos Sapountzoglou, Gilles Van Kriekinge, Dominik Huber, Maarten Messagie, Thierry Coosemans
K-Protection of Global Secret in Discrete Event Systems Using Supervisor Control
Abstract: This work addresses the security problem of protecting secrets in the framework of discrete event systems that are modeled by deterministic finite automata. We characterize a global secret that composes of one or multiple states, in which each state is assigned to a security level. A state is said to be protected if any event sequence from the initial state for reaching it contains the amount of protected events equal to or greater than the required security level. In addition, we assume that the protected event labels must be recovered within a bounded of consecutive protected events (called as K -protection). Our objective is to design a K -protection event policy such that the protected secret state pieces satisfy a predefined protection threshold. To this end, we first construct a security automaton that integrates the system state information and its current security level, and a K -protection automaton that lists all the possible protections of event sequences. Then by using the supervisor control theory technique, the valid protecting policy to enforce the security requirement is obtained. Finally, examples are used to illustrate the proposed protection method.
Authors: R. Liu, W. Duan, A. M. Mangini and M. P. Fanti
Electrical Vehicle Smart Charging Using the Open Charge Point Interface (OCPI) Protocol
Abstract: This paper proposes a new approach to the design of smart charging systems. It aims to separate the role of the Smart Charging Service Provider (SCSP) from the role of the Charge Point Operator (CPO) to provide real flexibility and efficiency of mass deployment. As interoperability is required for this purpose, the challenge is to use standard equipment and protocols in the design of the smart charging Energy Management System (EMS). The use of an Open Charge Point Interface (OCPI) is crucial for an interface between the EMS and the Charge Point Operator. The smart charging EMS developed has been implemented and successfully tested with two CPOs, with different use cases: (1) EV charging infrastructure at office buildings, and (2) EV charging infrastructure installed at a public car park facility.
Authors: Guillemin, S.; Choulet, R.; Guyot, G.; Hing, S.
Review on Integrated On-Board Charger-Traction Systems: V2G Topologies, Control Approaches, Standards and Power Density State-of-the-Art for Electric Vehicle
Abstract: This article reviews the different topologies compatible with V2G feature and control approaches of integrated onboard charger (iOBC) systems for battery electric vehicles (BEVs). The integrated topologies are presented, analyzed, and compared in terms of component count, switching frequency, total harmonic distortion (THD), charging and traction efficiencies, controllability, reliability and multifunctionality. This paper also analyzes different control approaches for charging and traction modes. Moreover, the performance indices such as setting time, rise time, overshoot, etc., are summarized for charging and traction operations. Additionally, the feasibility of a Level 3 charging (AC fast charging with 400 Vac) of up to 44 kW iOBC is discussed in terms of converter efficiencies with different switching frequencies and switch technologies such as SiC and GaN. Finally, this paper explores the power density trends of different commercial integrated charging systems. The power density trend analysis could certainly help researchers and solution engineers in the automotive industry to select the suitable converter topology to achieve the projected power density.
Authors: S. Jaman, S. Chakraborty, D-D.Tran, T. Geury, M. El Baghdadi & O. Hegazy, O
Development and Validation of an Integrated EV Charging Station With Grid Interfacing Inverter for Residential Application
Abstract: This research paper proposes a novel grid-connected modular inverter for an integrated bidirectional charging station for residential applications. The system is designed to support the electrical grid by providing buffering services and enhancing the stability of the grid. The proposed system consists of a modular bidirectional inverter which can work as an EV charger. The system can operate in different modes such as charging and discharging the EV battery, storing excess energy from the grid during low-demand periods, and supplying energy back to the grid during high-demand periods. A low-level control strategy based on the droop control technique and feedforward decoupling is developed to manage the power flow between the EV battery, household load and the grid. The performance of the proposed system is evaluated through simulation studies in MATLAB/Simulink software. The results show the system’s ability to support the grid during peak demand and boost renewable energy integration. It also acts as a backup power supply during outages, increasing reliability. The experimental validation in a laboratory environment confirms an 87% charging efficiency with 35% SoC, drawing 6.3 kW from the grid. Returning power to the grid achieves a V2G efficiency of around 94%, delivering 8.6 kW AC output and 8.77 kW battery input at a 55% SoC.
Authors: S. Jaman, M. Abdel-Monem, T. Geury and O. Hegazy
Cloud-Based Optimization of a Battery Model Parameter Identification Algorithm for Battery State-of-Health Estimation in Electric Vehicles
Abstract: Connectivity and cloud computing are key elements in the future of electric mobility. They allow manufacturers to provide advanced fleet management and predictive diagnostic services. In particular, cloud computing dramatically enhances data availability and enables the use of more complex and accurate state estimation algorithms for electric vehicle lithium-ion batteries. A tuning procedure for a moving window least squares algorithm to estimate the parameters of a 2-RC equivalent circuit battery model is presented in this paper. The tuning procedure uses real data collected from a test vehicle and uploaded to the Stellantis-CRF cloud. The tuned algorithm was applied to eight months of road tests and showed very small estimation errors. The errors are comparable to other literature data, even when the literature results were obtained in laboratory tests. The estimated model parameters are tracked through time and seem accurate enough to show the first signs of battery aging.
Authors: Di Rienzo R, Nicodemo N, Roncella R, Saletti R, Vennettilli N, Asaro S, Tola R, Baronti F.
Modeling and Validation of Grid-to-Vehicle (G2V) and Vehicle-to-Grid (V2G) Operation with Combo CCS Type 2 Connector for Medium Duty Electric Vehicle Applications
Abstract: In this research work, a modeling and control technique for a two-stage DC charger module is described with relevant dynamic equations and transfer functions. The simulation study and real-time testing of charging (G2V) and discharging (V2G) operation for a medium-duty electric vehicle with Combo CCS type 2 connector port is also performed. It is investigated that the power transfer efficiency for V2G operation using the Combo CCS Type 2 connector reaches approximately 94% for 4.8kW and 83% for 3kW. It is also found that higher power charging and V2G operations shows lower Total Harmonic Distortion (THD). For instance, during a power transfer of 5.1kW back to the grid at 60% State of Charge (SoC), the current THD is observed to be 3.12%. In contrast, the THD during charging mode is measured at 2.42%. These results highlight the improved quality of power during high-power charging and V2G operations, indicating reduced harmonic content and enhanced grid stability.
Authors: S. Jaman, O.H. Garcia, M. A-Monem, T. Geury and O. Hegazy
Electric Vehicles Routing Simulation and Optimization under Smart Charging Strategies
Abstract: This work deals with the problem of optimizing the routing of electric vehicles (EVs) for logistics operations. An optimization approach solves the Electric Vehicle Routing Problem and is formulated as an Integer Linear Programming problem. The objective is to minimize the charging/discharging cost considering the shortest path for each EV that must deliver freight to a group of customers. Moreover, to validate the performance of the proposed optimization method, we adopt SUMO software to model and simulate the EVRP problem solution. A real case study in the Apulia region (Italy) is considered to demonstrate the method’s efficacy. Additionally, different traffic scenarios are simulated in the SUMO environment, and the results show the impact of traffic on the traveling times.
Authors: Wasim A. Ali, María A. del Cacho Estil-les, Agostino M. Mangini, Michele Roccotelli, Maria Pia Fanti
A Trip Planner Tool for Electric Vehicles in Long Distance Journeys
Abstract: In the era of the transition towards electric vehicles (EVs), new services and tools are needed in order to facilitate the use of such vehicles. In this paper, a new tool is designed to optimally plan the long distance trips with an EV. The trip planner tool is realized by using MATLAB software. It implements an algorithm that, based on the EV battery model and on the charging stations information available on the route from departure to destination, determines the best itinerary in terms of travel time and cost, minimizing the charge stops. The prototype of the trip planner tool is demonstrated by a real case study.
Authors: Michele Roccotelli, Maria Pia Fanti, Agostino Marcello Mangini
Incremental profitability evaluation of V2G-enabled aFRR services for semi-public EVSE infrastructure: a case study in Belgium
Abstract: The current paper defines a framework for the introduction of automated frequency restoration reserve (aFRR) services enabled by vehicle-to-grid (V2G) technology into the business model of an entity owning and operating a network of semi-public EVSE (Electric Vehicle Supply Equipment). It assesses the profitability of this introduction by performing a case-study based on the real-life EV charging data from the EVSE network located in a hospital parking. From the results of the study, it is clearly visible that the introduction of V2G-enabled aFRR services has a significant positive incremental profitability, heavily dependent, however, on the plug-in ratio of EVSE network, determined by the EV user behavior.
Authors: Goncearuc, A., Sapountzoglou, N., De Cauwer, C., Coosemans, T., Messagie, M., Crispeels, T.
Real-World Implementation of Smart Charging: Challenges and Lessons Learned
Abstract: This paper aims to provide an overview of the charging tariffs and e-mobility schemes adopted in different EU cities based on a survey conducted within the EU project eCharge4Drivers. The outcomes of the survey are presented and analysed in order to extract a generalised tariffication formula which allows any eMSPs or CPOs to explore different options to overcome the issues that might be affecting their current CP management strategy.
Authors: Van Kriekinge, G., De Cauwer, C., Callebaut, L., Coosemans, T., & Messagie, M. (2023).
An overview on charging tariff schemes and incentives: the eCharge4Drivers project
Abstract This paper aims to provide an overview of the charging tariffs and e-mobility schemes adopted in different EU cities based on a survey conducted within the EU project eCharge4Drivers. The outcomes of the survey are presented and analysed in order to extract a generalised tariffication formula which allows any eMSPs or CPOs to explore different options to overcome the issues that might be affecting their current CP management strategy.
Authors: Evangelos Karfopoulos, Jaume Roca, Jaume Mata, Angel Lopez, Villy Portouli, Angelos Amditis
Electric Vehicles Routing Including Smart-Charging Method and Energy Constraints
Abstract This paper presents an optimization-based approach for the Electric Vehicle Routing Problem considering Smart-Charging methods. The objective, based on the application of the model, is to obtain the shortest route for each of the electric vehicles that have to deliver freight to a set of customers minimizing the charging/discharging cost. Based on the Smart-Charging method, in which vehicles can charge/discharge energy from/to the grid, the power grid limits, and balancing needs are considered. In this way, both the charging points and the energy districts are prevented from exceeding the maximum allowed energy peak. A real case study in the Apulia Italian region (Italy) shows the effectiveness of the proposed optimization model.
Authors: María A. del Cacho Estil-les, Maria Pia Fanti, Agostino Marcello Mangini, Michele Roccotelli
Small-Signal Average Switch Modeling and Dual-Loop Control of Bidirectional Integrated Converter for G2V and V2G Applications in Battery EVs
Abstract This paper proposes a small signal modeling approach of a bidirectional integrated converter configuration, which utilizes the traction inverter, motor windings and interleaved DC/DC converter for charging applications. This modeling technique represents the power electronic converters (AC/DC and DC/DC) as a transfer function that facilitates both Grid-to-Vehicle (G2V) and Vehicle-to-Grid (V2G) operations. During the G2V mode, the traction inverter converts into an AC/DC converter, while during the V2G mode, it operates as a DC/AC inverter, and the 3-phase interleaved DC/DC converter operates in buck and boost modes for the G2V and V2G operations, respectively. Moreover, the controller design based on the plant transfer functions is also a focal point of this paper. The inner current and outer voltage loop controllers of the DC/DC converter are designed based on the k-factor approach. For DC- link voltage control of the AC/DC converter, a dual loop control approach is adopted. Finally, the performance of both control systems has been validated via performance comparison between switch- based model and small-signal average system model in MATLAB/Simulink®.
Authors: Shahid Jaman, Sajib Chakraborty, Mohammed El Baghdadi, Thomas Geury, Omar Hegazy
Digital Twin in Intelligent Transportation Systems
Abstract This study reviews the research works published in the last five years on Digital Twin (DT) technology for intelligent transportation systems, focusing on the use of DT in electromobility and autonomous vehicles. The review is carried out systematically, considering specific domains within intelligent transportation in which DT technology is applied in combination with Internet of Thing and 5G technologies. In addition, the paper discusses the current issues in electric vehicle services, such as tracking, monitoring, battery management systems, and connectivity, and how they can be addressed effectively through DT approaches.
Authors: Wasim Ali, Michele Roccotelli, Maria Pia Fanti
Locating and Sizing Electric Vehicle Chargers Considering Multiple Technologies
Abstract In order to foster electric vehicle (EV) adoption rates, the availability of a pervasive and efficient charging network is a crucial requirement. In this paper, we provide a decision support tool for helping policymakers to locate and size EV charging stations. We consider a multi-year planning horizon, taking into account different charging technologies and different time periods (day and night). Accounting for these features, we propose an optimization model that minimizes total investment costs while ensuring a predetermined adequate level of demand coverage. In particular, the setup of charging stations is optimized every year, allowing for an increase in the number of chargers installed at charging stations set up in previous years. We have developed a tailored heuristic algorithm for the resulting problem. We validated our algorithm using case study instances based on the village of Gardone Val Trompia (Italy), the city of Barcelona (Spain), and the country of Luxembourg. Despite the variability in the sizes of the considered instances, our algorithm consistently provided high-quality results in short computational times, when compared to a commercial MILP solver. Produced solutions achieved optimality gaps within 7.5% in less than 90 s, often achieving computational times of less than 5 s.
Authors: Tommaso Schettini, Mauro dell’Amico, Francesca Fumero, Ola Jabali, Federico Malucelli
An Enhanced Path Planner for Electric Vehicles Considering User-Defined Time Windows and Preferences
Abstract A number of decision support tools facilitating the use of Electric Vehicles (EVs) have been recently developed. Due to the EVs’ limited autonomy, routing and path planning are the main challenges treated in such tools. Specifically, determining at which Charging Stations (CSs) to stop, and how much the EV should charge at them is complex. This complexity is further compounded by the fact that charging times depend on the CS technology, the EV characteristics, and follow a nonlinear function. Considering these factors, we propose a path-planning methodology for EVs with user preferences, where charging is performed at public CSs. To achieve this, we introduce the Electric Vehicle Shortest Path Problem with time windows and user preferences (EVSPPWP) and propose an efficient heuristic algorithm for it. Given an origin and a destination, the algorithm prioritizes CSs close to Points of Interest (POIs) that match user inputted preferences, and user-defined time windows are considered for activities such as lunch and spending the night at hotels. The algorithm produces flexible solutions by considering clusters of charging points (CPs) as separate CSs. Furthermore, the algorithm yields resilient paths by ensuring that recommended paths have a minimum number of CSs in their vicinity. The main contributions of our methodology are the following: modeling user-defined time windows, including user-defined weights for different POI categories, creating CSs based on clusters of CPs with sufficient proximity, using resilient paths, and proposing an efficient algorithm for solving the EVSPPWP. To facilitate the use of our methodology, the algorithm was integrated into a web interface. We demonstrate the use of the web interface, giving usage examples and comparing different settings.
Authors: Maximiliano Cubillos, Mauro Dell’Amico, Ola Jabali, Federico Malucelli, Emanuele Tresoldi
Development of Smart Charging Scheduling and Power Management Strategy of a PV-ESS based Scalable EV Charging Station
Abstract This paper describes smart power management and charging scheduling strategy for a multiple port electric vehicle (EV) charging station, connected to battery storage systems and renewable energy sources. The charging station can charge different types of EVs, like electric scooters and passenger cars at different power levels. The energy management optimizes the usage of the power sources to fulfill the charging demand by analyzing the overall load demand, the electricity tariff, and the information provided by the EV user. The station’s control system is set up to satisfy the charging demand primarily with a solar photovoltaic (PV) array and an energy storage system (ESS) as a battery. In the case of PV power generation and battery power shortage, it draws power from the grid to fulfill the charging demand. Additionally, a charging scheduling strategy is described in this paper in which the charging current setpoint for each charger is estimated by an optimization process in the local charger controller. The overall system management and charging scheduling are simulated and verified in the MATLAB/Simulink environment.
Authors: Shahid Jamana, Boud Verbruggea, Assel Zhaksylyka, Thomas Geurya, Mohamed El Baghdadia, Omar Hegazya
Enhanced booking services for electric mobility
Authors: Maria Pia Fanti, Michele Roccotelli, Alessandro Rinaldi, Daniele Pagano, Raol Buqi, Stefano Persi, Montserrat Anglès, Evangelos Karfopoulos, Angelos Amditis
Smart charging solutions for electric mobility
Abstract Transport electrification is an accelerating reality which should be complemented by the respective charging technologies and services for its successful deployment. While the electric vehicle (EV) market is now fully developed, users still have concerns about key areas of the electric vehicle charging infrastructure. The transition from conventional vehicles to electric ones dictates the design and development of user-centric charging solutions aiming to facilitate the accessibility to as well as the usability of the charging network and improve the user’s charging experience. This paper aims to highlight the critical issues of the electric mobility sector and analyse smart charging approaches supporting the large-scale implementation of electric cars.
Authors: Alessandro Rinaldi, Maria Pia Fanti, Michele Roccotelli, Evangelos Karfopoulos, Angelos Amditis, Villy Portouli
Development and Validation of V2G Technology for Electric Vehicle Chargers Using Combo CCS Type 2 Connector Standards
Abstract Vehicle-to-Grid (V2G) technology is viewed as a viable solution to offer auxiliary power system services. Currently, V2G operation is only possible through DC chargers using the CHAdeMO connector with the necessary communication protocol. However, in Europe, for high-power DC charging (>50 kW), the Combined Charging Service (CCS) Type 2 is preferred over CHAdeMO. Therefore, this work presents the development of a V2G testing system with a Combo CCSType 2 charger including communication via the ISO 15118-2 protocol. The BOSCH passenger car with a 400 V battery pack is used to test and validate the technical feasibility of V2G charging via a Combo CCS Type 2 connector standard. The V2G feature is characterized in terms of efficiency, signal delay, response proportionality, magnitude accuracy and noise precision. A data driven V2G charger simulation model based on the real-time data is also developed in MATLAB/Simulink. The performance under various operating settings is presented in the outcomes, emphasizing the need for appropriate hardware calibration, and understanding while delivering standard-compliant grid control services using V2G technology. Finally, the results of the simulation model are compared with the real hardware results in terms of error, noise level and data magnitude accuracy.
Authors: Shahid Jaman, Boud Verbrugge, Oscar Hernandez Garcia, Mohamed Abdel-Monem, Blum Oliver, Thomas Geury, Omar Hegazy