• Energy Research
• Restricted close
• Embargo close
• 7. Clean energy close
• 11. Sustainability close
• 12. Responsible consumption close
• Transport Research close

Abstract The kinetics of methane hydrate decomposition was studied using a semibatch stirred-tank reactor. The decomposition was accomplished by reducing the pressure on a hydrate slurry in water to a value below the three-phase equilibrium pressure at the reactor temperature. The data were obtained at temperatures from 274 to 283 K and pressures from 0.17 to 6.97 MPa. The stirring rates were high enough to eliminate mass-transfer effects. Analysis of the data indicated that the decomposition rate was proportional to the particle surface area and to the difference in the fugacity of methane at the equilibrium pressure and the decomposition pressure. The proportionality constant showed an Arrhenius temperature dependence. An estimate of the hydrate particle diameters in the experiments permitted the development of an intrinsic model for the kinetics of hydrate decomposition.

Electric vehicle seems to go well together with the growing societal trend of becoming more self-supplying with renewable electricity produced in the household. However, aligning household electricity production and electric vehicle charging have received little attention in HCI although both areas have been pursued separately for a number of years. In this paper, we present findings from a qualitative study that explore the potential of aligning electric vehicle charging with times where renewable electricity is being produced in the household. We present an empirical qualitative study of 5 households (19 persons) that own electric vehicles and also produce their own renewable electricity. Our findings, described in five themes, reveal that aligning charging and electricity production can be a challenge and tension exist for aligning consumption such as motivation, roles, mobility patterns, and electricity producing technology. We further discuss our findings and possible directions for future HCI research in the field.

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

Abstract In a context where electric mobility is gaining increasing importance as a more sustainable solution for urban environments, this work presents an analysis of electric mobility feasibility and adequacy based on private users' naturalistic driving data. Several scenarios were tested to evaluate different charging event opportunities and their impacts on electric mobility feasibility. In more detail, scenario 1 considered that vehicles would recharge whenever they are stopped for 2, 4 or 6 h, either on weekdays or weekend days; scenario 2 tested the hypothesis of recharging only during the night period; and scenario 3 assumed that vehicles would recharge during the day on weekdays. Furthermore, the potential energy impacts of electric mobility at a city level, by applying a driver and street level approach, were also estimated. Results revealed that electric mobility is highly feasible for weekday urban trips, while weekend trips due to their higher average distance are less suitable to be performed by EVs. Scenario 1, due to its higher recharging opportunities was found to be the best-case scenario. In this case, the percentage of eligible trips was found to be equal to or higher than 94% and 88% on weekdays and weekend days, respectively. Results showed also the lower electric mobility feasibility if considering only daytime charging, on weekdays (scenario 3). However, if considering night charging (scenario 2), the electric mobility eligibility was found to improve significantly. When considering a street level analysis, the potential reduction in energy consumption ranges in average from −60 to −70%, enabling the visualization of higher EV potential, with increasing potential for reducing energy consumption for increasing road grades. Concluding, since electric mobility is particularly suited for urban driving and most households detain 2 or more vehicles, there is a high potential to replace at least one ICEV by an EV. In this case, people may adapt their driving behavior, using the EV for their day-to-day urban driving while the ICEV would be used for longer trips. Nonetheless, the capacity to recharge during night plays a significant role on trips eligibility. Therefore, the availability of home-charge set-ups or a much higher deployment of public charging stations at residential locations are required in order to incentivize drivers to shift towards electric mobility.

The aim of this publication is a comparison of a wide selection of range extender technologies. Thereby a large amount of criteria are looked into. This assures that the comparison is feasible for a diversity of performance specifications. Hence an information level is achieved, that allows for a justified choice of technology according to the desired range extended electric vehicle concept.

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%.