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Proton-exchange membrane (PEM) fuel cells are increasingly used in the automotive sector. A crucial point for estimating the performance of such systems is open-circuit voltage (OCV) losses, among which the most influential are mixed potential, hydrogen crossover, and internal short circuits. These losses are often overlooked in the modeling of such electrochemical cells, leading to an inaccurate estimation of the real voltage that is calculated starting from the Nernst Equation. An innovative method is presented to estimate the losses based on the division of the membrane into two domains: solid and aqueous. The influence of the macro-parameters (temperature, pressure, and RH) was analyzed for each phenomenon and was linked to the membrane water content. For low levels of PEM hydration, internal short circuits were of the same order of magnitude as hydrogen crossover. The OCV model accuracy was assessed on a commercial stack, used on a vehicle prototype competing in the Shell Eco-Marathon challenge. The data of interest were obtained through laboratory tests and subsequent disassembly of the stack. A PEM thickness of 127 μm was measured corresponding to Nafion 115. For further validation, the model results were compared with data in the literature.

In thermodynamics, the useful work in any process can be evaluated by using the exergy quantity. The analyses of irreversibility are fundamental in the engineering design and in the productive processes’ development in order to obtain the economic growth. Recently, the use has been improved also in the thermodynamic analysis of the socio-economic context. Consequently, the exergy lost is linked to the energy cost required to maintain the productive processes themselves. The fundamental role of the fluxes and the interaction between systems and their environment is highlighted. The equivalent wasted primary resource value for the work-hour is proposed as an indicator to support the economic considerations on the biofuel production by using biomass and bacteria. The equivalent wasted primary resource value for the work-hour is proposed as an indicator to support the economic considerations of the biofuel production by using biomass and bacteria. Moreover, the technological considerations can be developed by using the exergy inefficiency. Consequently, bacteria use can be compared with other means of biofuel production, taking into account both the technologies and the economic considerations. Cyanobacteria results as the better organism for biofuel production.

A novel maximum power point tracking (MPPT) technique based on mutual coordination of two photovoltaic (PV) modules/arrays has been proposed for distributed PV (DPV) systems. The proposed technique works in two stages. Under non-mismatch conditions between PV modules/arrays, superior performance stage 1 is active, which rectifies the issues inherited by the perturb and observe (P&O) MPPT. In this stage, the technique revolves around the perturb and observe (P&O) algorithm containing an intelligent mechanism of leader and follower between two arrays. In shading conditions, stage 2 is on, and it works like conventional P&O. Graphical analysis of the proposed technique has been presented under different weather conditions. Simulations of different algorithms have been performed in Matlab/Simulink. Simulation results of the proposed technique compliment the graphical analysis and show a superior performance and a fast response as compared to others, thus increasing the efficiency of distributed PV systems.

Maximum power-point tracking (MPPT) is applied to enable effective operation of photovoltaic (PV) systems under different external conditions. MPPT is based on a control system that aims at maintaining the PV system operation in the most effective conditions of maximum power output. This paper demonstrates the effective application of a novel adaptive control approach developed to be used in the field of power electronics. The application to MPPT is developed by using a non-inverted Buck-Boost converter applied to the PV system. The novel control methodology is based on the application of the Lyapunov stability concepts. The strength of this novel control technique is confirmed by the accurate comparison among the results obtained by using the proposed solution and some controllers proposed in the literature.

The electric vehicle (EV) market is growing rapidly due to the necessity of shifting from fossil fuel-based mobility to a more sustainable one. Smart charging paradigms (such as vehicle-to-grid (V2G), vehicle-to-building (V2B), and vehicle-to-home (V2H)) are currently under development, and the existing implementations already enable a bidirectional energy flow between the vehicles and the other systems (grid, buildings, or home appliances, respectively). With regard to grid connection, the increasingly higher penetration of electric vehicles must be carefully analyzed in terms of negative impacts on the power quality; and hence, the effects of electric vehicle charging stations (EVCSs) must be considered. In this work, the interactions of multiple electric vehicle charging stations have been studied through laboratory experiments. Two identical bidirectional DC chargers, with a rated power of 11 kW each, have been supplied by the same voltage source, and the summation phenomenon of the current harmonics of the two chargers (which leads to an amplification of their values) has been analyzed. The experiment consisted of 100 trials, which considered four different combinations of power set-points in order to identify the distribution of values and to find suitable indicators for understanding the trend of the harmonic interaction. By studying the statistical distribution of the Harmonic Summation Index, defined in the paper, the impact of the harmonic distortion caused by the simultaneous charging of multiple electric vehicles has been explored. Based on this study, it can be concluded that the harmonic contributions of the electric vehicle charging stations tend to add up with increasing degrees of similarity of the power set-points, while they tend to cancel out the more the power set-points differ among the chargers.

Battery electric vehicles (BEVs) face challenges like their limited all-electric range, the discrepancy between promised and actual energy efficiency, and battery health degradation, despite their environmental benefits. This article proposes an optimal adaptive cruise control (OACC) framework by leveraging ideal vehicle-to-vehicle communication to address these challenges. In a connected vehicle environment, where it is assumed that the Ego vehicle’s vehicle control unit (VCU) accurately knows the speed and position of the Leading vehicle, the VCU can optimally plan the acceleration trajectory for a short-term future time window through a model predictive control (MPC) framework tailored to BEVs. The primary objective of the OACC is to reduce the energy consumption and battery state-of-health degradation of a BEV. The Chevrolet Spark 2015 is chosen as the BEV platform used to validate the effectiveness of the proposed OACC. Simulations conducted under urban and highway driving conditions, as well as under communication delay and infused noise, resulted in up to a 3.7% reduction in energy consumption and a 9.7% reduction in battery state-of-health (SOH) degradation, demonstrating the effectiveness and robustness of the proposed OACC.

The share of industry in final global energy consumption was more than 30% in 2020, of which, the hard-to-abate sectors accounted for almost 60% of total final consumption in industry. Similarly, in Europe, industry accounts for around 25% of final energy consumption. In order to reduce the impact of industry in energy consumption and greenhouse gas emissions, Europe has set many policies that support and regulate the sector, including pricing carbon emissions in a cap-and-trade scheme called the European Emission Trading Scheme (EU ETS). According to the EU ETS, in 2021 the verified emissions of all stationary installations were around 1.3 billion tons of carbon dioxide equivalent emissions. In 2021, the total allocated allowances amounted to around 1 billion tons of carbon dioxide equivalent emissions, half of which were freely allocated. After reviewing the existing modeling approaches for industrial clusters and the available datasets, and assessing the energy consumption and carbon dioxide emissions at plant level using a geographical information system approach (GIS), a taxonomy for industrial cluster decarbonization was introduced. This taxonomy shows that describing industry as sets of clustered installations rather than based on the conventional sectoral economic classification provides more insights into energy transition. First, the cluster description provides a more accurate techno-economic assessment based on a finer characterization of economies of scale compared to traditional energy systems models. Second, the industrial clustering approach may more realistically show the feasibility, in addition to the costs and benefits from coupling industry with transport (e.g., industrial fleets and logistics) or buildings (e.g., city scale), due to a more detailed representation of the energy sources and sinks.

Because of the increase of the data traffic demand, wireless access networks, through which users access telecommunication services, have expanded, in terms of size and of capability and, consequently, in terms of power consumption. Therefore, costs to buy the necessary power for the supply of base stations of those networks is becoming very high, impacting the communication cost. In this study, strategies to reduce the amount of money spent for the purchase of the energy consumed by the base stations are proposed for a network powered by solar panels, energy batteries and the power grid. First, the variability of the energy prices is exploited. It provides a cost reduction of up to 30%, when energy is bought in advance. If a part of the base stations is deactivated when the energy price is higher than a given threshold, a compromise between the energy cost and the user coverage drop is needed. In the simulated scenario, the necessary energy cost can be reduced by more than 40%, preserving the user coverage by greater than 94%. Second, the network is introduced to the energy market: it buys and sells energy from/to the traditional power grid. Finally, costs are reduced by the reduction of power consumption of the network, achieved by using microcell base stations. In the considered scenario, up to a 31% cost reduction is obtained, without the deterioration of the quality of service, but a huge Capex expenditure is required.