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This paper presents a tradeoff analysis in terms of accuracy and computational cost between different architectures of artificial neural networks for the State of Charge (SOC) estimation of lithium batteries in hybrid and electric vehicles. The considered layouts are partly selected from the literature on SOC estimation, and partly are novel proposals that have been demonstrated to be effective in executing estimation tasks in other engineering fields. One of the architectures, the Nonlinear Autoregressive Neural Network with Exogenous Input (NARX), is presented with an unconventional layout that exploits a preliminary routine, which allows setting of the feedback initial value to avoid estimation divergence. The presented solutions are compared in terms of estimation accuracy, duration of the training process, robustness to the noise in the current measurement, and to the inaccuracy on the initial estimation. Moreover, the algorithms are implemented on an electronic control unit in serial communication with a computer, which emulates a real vehicle, so as to compare their computational costs. The proposed unconventional NARX architecture outperforms the other solutions. The battery pack that is used to design and test the networks is a 20 kW pack for a mild hybrid electric vehicle, whilst the adopted training, validation and test datasets are obtained from the driving cycles of a real car and from standard profiles.

As a critical indictor in the Battery Management System (BMS), State of Charge (SOC) is closely related to the reliable and safe operation of lithium-ion (Li-ion) batteries. Model-based methods are an effective solution for accurate and robust SOC estimation, the performance of which heavily relies on the battery model. This paper mainly focuses on battery modeling methods, which have the potential to be used in a model-based SOC estimation structure. Battery modeling methods are classified into four categories on the basis of their theoretical foundations, and their expressions and features are detailed. Furthermore, the four battery modeling methods are compared in terms of their pros and cons. Future research directions are also presented. In addition, after optimizing the parameters of the battery models by a Genetic Algorithm (GA), four typical battery models including a combined model, two RC Equivalent Circuit Model (ECM), a Single Particle Model (SPM), and a Support Vector Machine (SVM) battery model are compared in terms of their accuracy and execution time.

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.

Abstract An experimental and theoretical investigation is being performed to evaluate exhaust emissions and fuel consumption of Heavy Duty Vehicles (HDVs) circulating in urban areas and involved in commercial shipping activities. The study is focused on the city of Genoa, whose urban road network is influenced by highway connections and shipping activities, as seven motorway exits and more than twenty accesses to port area are located within the urban area. In a first step, the HDV flows crossing highway exits, urban zones and port areas were evaluated, as well as the relevant vehicle classes. The typical urban trips linking highway exits to port gates and the HDV mission profiles within the port area were then defined, whose validation was performed through an experimental campaign for HDV instantaneous speed measurements on urban trips and in port zones. The availability of speed patterns enabled the application of Passenger Car and Heavy Duty Emission Model (PHEM) for the estimation of fuel consumption and emission factors for selected HDV classes. The main results of the different investigation steps are presented and discussed in the paper, outlining the specific activities of HDVs in port area and the relevant emissive behaviour.

Human pressures on marine ecosystems significantly increased during last decades. Among the intense anthropic activities, industrial fisheries have caused the alteration of habitats, the reduction of biodiversity and the main fish stocks. The aim of this research, carried out in the Adriatic Sea, was to test a repeatable Marine Spatial Planning framework aimed at enhancing fisheries sustainability through the application of Decision Support Tools and the composition of a catalog of possible measures. The use of these tools proved very useful to identify possible criticalities and facilitate an effective exchange with fisheries stakeholders, local authorities, and fishermen, whose involvement was an indispensable step in the process. Tool-based analyses allowed to assess the spatial footprint of a range of anthropogenic pressures from human activities (e.g., fisheries, maritime traffic, and aquaculture). Within this multi-pressure scenario, special attention was paid to fishing-related disturbances and potential conflicts across different fishing métier and with other sectors. Specifically, results highlighted the spatial features of the major fishing pressures (e.g., abrasion from trawling) affecting essential fish habitats, marine mammals and turtles in the study area. A portfolio of possible management measures is identified for the study area. It provides clear evidence that, in order to mitigate emerging conflicts and cumulative impacts, it is necessary to combine and integrate different types of measures: spatial measures modulated over time, monitoring and control, actions to fill knowledge gaps, concertation—involvement—co-management actions, improvement of governance systems, actions to support innovation in the sector, etc. Given the complex set of measures discussed, this work can provide a useful contribution to the management of fisheries both at local and regional level, fostering the transition to sustainable fisheries.

In this paper, an optimization model is defined for the design of a smart energy infrastructure integrating different technologies to satisfy the electrical demand of a given site. The considered smart energy infrastructure includes a photovoltaic plant, electrical storage systems, electric vehicles (EVs), and charging stations. The objective function of the optimization model considers the costs related to the installation and maintenance of the considered technologies, as well as the costs associated with the energy exchanges with the external grid. A very extensive numerical analysis is reported in the paper, referred to a test case in a real site in Liguria Region, in the north of Italy. Many scenarios are analyzed and discussed, with specific attention to evaluate the role of electric mobility within a smart energy infrastructure and a focus on EVs acting as mobile storage systems.

AbstractThis paper argues that a focus on values, trust and context is vital to build a fuller understanding of public perceptions of carbon dioxide capture and storage (CCS). Empirical data from interviews conducted in the UK and Italy as part of the EU FP7-funded ECO2 project is presented to illustrate how publics and stakeholders often evaluate the geological storage of carbon dioxide in terms of its relation to their broader world views, rather than purely in terms of the perceived techno-scientific risks of the technology.