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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

Climate change is driving compositional shifts in ecological communities directly by affecting species and indirectly through changes in species interactions. For example, competitive hierarchies can be inversed when competitive dominants are more susceptible to climate change. The brown seaweed Fucus vesiculosus is a foundation species in the Baltic Sea, experiencing novel interactions with the invasive red seaweed Gracilaria vermiculophylla, which is known for its high tolerance to environmental stress. We investigated the direct and interactive effects of warming and co-occurrence of the two algal species on their performance, by applying four climate change-relevant temperature scenarios: 1) cooling ) 2 °C below ambient - representing past conditions), 2) ambient summer temperature (18 °C), 3) IPCC RCP2.6 warming scenario (1 °C above ambient), and 4) RCP8.5 warming (3 °C above ambient) for 30 days and two compositional levels (mono and co-cultured algae) in a fully-crossed design. The RCP8.5 warming scenario increased photosynthesis, respiration, and nutrients' uptake rates of mono- and co-cultured G. vermiculophylla while growth was reduced. An increase in photosynthesis and essential nutrients' uptake and, at the same time, a growth reduction might result from increasing stress and energy demand of G. vermiculophylla under warming. In contrast, the growth of mono-cultured F. vesiculosus significantly increased in the highest warming treatment (+3 °C). The cooling treatment (-2 °C) exerted a slight negative effect only on co-cultured F. vesiculosus photosynthesis, compared to the ambient treatment. Interestingly, at ambient and warming (RCP2.6 and RCP8.5 scenarios) treatments, both F. vesiculosus and G. vermiculophylla appear to benefit from the presence of each other. Our results suggest that short exposure of F. vesiculosus to moderate or severe global warming scenarios may not directly affect or even slightly enhance its performance, while G. vermiculophylla net performance (growth) could be directly hampered by warming.

The advantages of real-time dataset with artificial intelligence-based machine learning algorithms can be utilized for improving energy management in Electric Vehicles (E-Vehicle). Here the machine learning algorithms such as classification, regression, reinforcement, and clustering are incorporated to identify the suitable profile pattern. Electric vehicle modelling can be developed from profile, outline, and formulation for energy efficiency can be done from vehicle state, specifications from acceleration and deceleration with necessary constraints. Each vehicle energy management stages are realized as kinetic energy, mechanical energy, and electrical energy for minimizing the losses within a boundary except proving the constraints. With a problem formulated in each stage for classifying the input profile, it is found that regression analysis matches the energy optimization, reinforcement technique fixes the boundary at every profile input and clustering of data ensures formation of data set. The proposed scheme is validated with MATLAB/Simulink environment and the results shows that the new methods improved the energy efficiency in every stage at the level of 10 to 30%. Also, among the energy efficient techniques, classification and regression schemes adds an error loss of 0.2 to 0.3%, the reinforcement method includes the data handling error of 0.3 to 0.4% and finally, the clustering method increases the error loss in the level of 0.4 to 0.5%.

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.