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This paper is focused on the design criteria of the power conversion systems operating within ultra-fast charging stations for electric vehicles. The proposed architecture is based on a DC bus, which features the integration of renewable energy sources and buffered storage systems, performing the new concept of smart grid system. Simulations of the power converters and storage systems, working as power devices of recharging station architecture, are implemented in the Matlab-Simulink environment with models of each subcomponent provided by the Sim-Power-System tool. The reported simulations are mainly devoted to verify the design criteria of the architecture scheme and the control strategies of the power fluxes related to power converters. The advantages and convenience in terms of power quality and requirementsfrom the main grid are shown, also during the EV fastcharging operations. Finally, the resulted recharging times are evaluated as comparable to the fuelling times generally taken by traditional oil based vehicles.

As the largest renewable electricity source, hydropower represents an alternative to fossil fuels to achieve a low-carbon future. However, increasing evidence suggests that hydropower reservoirs are an important source of biogenic greenhouse gases (GHGs), albeit with large uncertainties. Combining spatially resolved assessments of GHG fluxes and hydroelectric capacity databases, we assessed that global GHG emissions from reservoirs is 0.38 Pg CO2 eq.yr−1, accounting for 1.0% of global anthropogenic emissions. The median carbon intensity for hydropower is ∼63.0 kg CO2eq. MWh−1, which is lower than that for fossil fuels, but higher than that for other renewable energy sources. High carbon intensity is mostly linked to shallow (water storage depth <20 m) and eutrophic reservoirs. Furthermore, we found that the reservoir carbon intensity (CI) value would be markedly increased to 131.5 kg CO2eq. MWh−1 when considering the dams under construction and planning. A low-carbon future will benefit from optimal dam planning and management measures, i.e., applying sludge removal treatments, thereby reducing the proportion of shallow reservoirs and anthropogenic pollution.

The development of Lithium-ion batteries for electric vehicles have recently become the subject of large research efforts due to some remarkable advantages over other kinds of batteries, such as: high energy density, high power density, long cycle life and long calendar life. This paper is aimed to develop an ageing model suitable to estimate the battery lifetime, the residual capacity and reliability margins under different driving cycles, taking also into account the battery calendar ageing

This paper presents an overview of issues and technologies related to the proper design of charging infrastructures for road electric vehicles. The analysis is carried out taking into account that the recharging stations of electric vehicles might be integrated in smart grids, which interconnect the main grid with distributed power plants, different kinds of renewable energy sources, stationary electrical storage systems and electric loads. The study is introduced by an analysis of the main characteristics concerning different kinds of storage systems to be used for stationary and on-board applications. Then, different charging devices, modes and architectures are presented and described showing their characteristics and potentialities. DC and AC configurations of charging stations are compared in terms of the issues related to their impact on the main grid and the design of their main components. Specific attention was devoted also to the ultra-fast DC architecture, which appears a possible solution to positively affect a wide spread of plug-in hybrid and full electric road vehicles.

Abstract Fuel Cell Vehicles (FCVs) has been introduced to the market around the world in recent years. As the largest automobile market of the world, China is also one of the potential FCVs market. However, a series of factors and barriers influence the willingness of China's customers to accept FCVs. By using Fishbone Diagram, field survey and workshop discussions, this paper proposes a group of factors that may affect customers' preferences on FCVs. Furthermore, Fuzzy AHP and Pareto Analysis are employed to prioritize these factors, and identify the critical ones. The results indicate that fuel availability, vehicle performance, and economic costs are the most important dimensions in affecting customers' attitude towards FCVs. More specifically, vehicle reliability and safety, purchasing cost, industry development, vehicle model and space contribute the most significance in customers' purchase decision. According to the results, some policy implications are proposed from the prospective of improving and demonstrating vehicle performance, government leading facility construction and operation, and costs reductions.

Environmental climate change has encouraged countries across the world to develop policies aimed to the reduction in energy consumption and greenhouse gas emissions. The introduction of Zero-Emission Vehicles based on electrical powertrains, could reduce the emission of environmental pollutants, the noise levels and could increase the liveability of urban areas. Although in recent years research on batteries has brought several benefits to electric vehicle performance, key barriers to their adoption are still high cost, reduced autonomy, long charging times and the leak of a suitable network of charging stations. Substantial improvements in electric vehicles performance are expected with the development of new Li-ion batteries, thanks to some notable advantages over other types of batteries, such as: high energy density, high power density, long cycle life and long calendar life. This paper is aimed to present a reliability assessment procedure based on an ageing model able to estimate from datasheet information the lifetime of Lithium-ion batteries for electric vehicles, the residual capacity and reliability margins under different driving cycles, taking also into account the battery calendar ageing.

Abstract The modern revolutionary changes in power delivery systems with the advent of smart and flexible grids require systems interoperability, seamless integration of technologies and functionalities. This scenario is paving the way to new prospective of energy clusters that call for new methodologies for the dynamic energy management of distributed energy resources. This paper proposes a new management scheme of energy clusters (e.g., smart building, energy community or virtual power plant) that is based on a data driven decision-maker that daily deploys the activities of the day ahead, providing an optimized scheduling which will be the base for the operations for the next day. The new aggregator relies on some innovative features. It leverages an optimization process based on the elastic net regularization that proves to be an effective support for finding the best scheduling of the distributed resources according to specific key performance indicators, that presently is the unbalance. The decision process works on predicated data obtained through a recently assessed short term forecasting based on long short-term memory neural networks properly adapted to distributed environments. The method allows to extract insight from data and run what-if scenarios to define the best scheduling of resources. The technique uses a set of feasible rules to optimally aggregate the distributed resources and demand-side management programs. We tested the proposed aggregator on a real energy cluster making use of real measured data over two years period. The results show the effectiveness of the proposed approach that is able to predict any sort of unit and manage any sort of program. The significance of this work is to approach the energy management as an optimization and decision problem in a more robust way of reasoning, by employing forecasting/optimization over all the quantities in the community, resulting in a thoroughly complex and efficient method.

this study aims, for the Italian case, to: a) estimate the national passenger and freight road travel demand with respect to three different time periods: 2005 (reference year for EU “fit for 55” target); 2019 (last consolidated pre-covid year); 2022 (base-line year); b) define possible road transport scenarios to 2030 following the Avoid-Shift-Improve (ASI) approach; c) estimate the GHG and energy consumption inventory related to national road transport for the 2005, 2019; 2022 and 2030 scenarios.