Today’s AC railway feeding systems are composed by several conductors with various connections to each other. The return current is shared between rails, ground wires connected to rails, ground; current flows to ground are determined by rail-to-ground distributed conductance. This paper gives formulas to deal with all these complexities, both for power quality and power frequency studies, integrating and combining previous studies which cover only part of them. Especially new in this paper is a technique to include in the computation currents dispersed into soil through ballast. It also illustrates a way to implement easily all the proposed formulas using a specific, open and flexible simulation language, the Modelica language. An application of the proposed formulas and related simplifications is illustrated.

The increasing integration of renewable energy sources and electric vehicles is reshaping distribution networks, calling for advanced control strategies to maintain power system quality, stability and resilience, intended to capacity to maintain stability after severe perturbations. This paper investigates the application of grid-forming (GFM) controls, of two types: droop and virtual synchronous machine, within high-power EV charging stations (HPCS) connected to medium voltage distribution grids. Through detailed simulation of five representative case studies, our results show that GFM converters can sustain isolated grids, support black-start procedures, and provide voltage/frequency regulation. Notably, compared to traditional grid-following inverters, GFM strategies enable HPCS to enhance grid resilience to faults (short circuits considered) and also recovery after blackouts. All models are released as open-source Modelica code, enabling reproducibility and further research. These findings highlight the potential of leveraging charging infrastructure as a flexible, grid-supportive resource in future energy systems. The considered control actions may require bi-directional exchange of power between vehicles and the grid, usually called V2G (vehicle-to-grid) control. However, interesting opportunities also exist in the context of V1G (Smart Charging), where power flow is unidirectional but still controllable. They can in principle used also to interface grid-connected independent battery energy storage systems, but using charging battery-electric vehicles for this purpose has the advantage of exploiting infrastructure already in place (the charging station), adding grid services to it, to enhance its economic competitiveness. The importance of using future charging battery electric vehicles to support grid services is confirmed by the fact that this study has been funded by a research project devoted to this.

Lithium batteries for energy storage systems are a prominent solution for both stationary and mobile applications. Electro-thermal modelling of the cell is a useful tool for monitoring voltage and temperature in order to predict battery behaviour especially in cases of critical operative conditions. This paper provides a modelling approach focusing on the calibration of parameters of an electro-thermal model for large prismatic LFP lithium cells. The designed model is tuned by means of experimental tests that identify a set of parameters that are function of a cell’s state-of-charge and temperature. The model outputs are voltage, cell surface, and internal temperature profiles, which are validated against experimental data referring to realistic working conditions, even providing an intense level of thermal stress. The model accuracy is marked by a voltage mean average error lower than 1% and a mean cell surface temperature deviation lower than 1 K.

Batteries are critical components of battery electric vehicles (BEVs) and yet their expected lifetime is hard to estimate. Various determinants play a significant role, among which cell temperatures and usage profile are notably the most predominant, but their impact is complex to estimate. The variability of the vehicle usage patterns makes it difficult to estimate typical stresses on the battery, and cell temperatures are often unknown conversely to the ambient temperature, that is usually used in degradation models. For these reasons, in this study we present a methodology for estimating the expected battery lifetime in electric vehicles (EVs) through a comprehensive powertrain, electrical and thermal model of the vehicle and its battery, including a battery degradation model. Supported by experimental measures, the whole model, including the battery cooling system, is calibrated with data obtained from the onboard Controller Area Network (CAN) of a BEV. The case study to represent typical traffic conditions on Italian routes highlights that the approach is robust, expected battery lifetimes are suitable for typical vehicle usage, and that appropriate battery thermal modelling can reduce errors in the lifetime assessment of beyond 40-50%. The study emphasizes the importance of active thermal management in preserving battery health, and the methodology provides a foundation for further multi-disciplinary modeling of EV battery aging.

As new models of electric vehicles are put on the market, with larger batteries and higher charging rates, there are growing concerns about how the charging infrastructure should be upgraded and modernized to cope with the challenges they pose. Governmental directives and rules dictate minimal requirements for the future charging infrastructure, but would these be enough to handle massive numbers of electric vehicles? In this manuscript we describe an electric mobility simulator that can be used to mimic highway vehicular flows and evaluate the queues at charging stations under different penetration levels of electric vehicles. Based on actual vehicular flows from the most important Italian motorway, we find that non-homogeneous queues at charging stations can be predicted, and the infrastructure planned by the EU rules may not be able to accommodate a penetration level of more than 3% of electric vehicles in the highway without giving rise to unacceptably high waiting times at charging stations. Also, we note that smart assignments of electric vehicles at charging stations may significantly improve waiting times, opening the discussion on the need for allocation policies and guidelines. Extensive Monte Carlo simulations on an accurate reconstruction of the Italian case study support the discussion and our findings.