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Solid oxide fuel cells (SOFC) could facilitate the green energy transition as they can produce high-temperature heat and electricity while emitting only water when supplied with hydrogen. Additionally, when operated with natural gas, these systems demonstrate higher thermoelectric efficiency compared to traditional microturbines or alternative engines. Within this context, although digitalisation has facilitated the acquisition of extensive data for precise modelling and optimal management of fuel cells, there remains a significant gap in developing digital twins that effectively achieve these objectives in real-world applications. Existing research predominantly focuses on the use of machine learning algorithms to predict the degradation of fuel cell components and to optimally design and theoretically operate these systems. In light of this, the presented study focuses on developing digital twin-oriented models that predict the efficiency of a commercial gas-fed solid oxide fuel cell under various operational conditions. This study uses data gathered from an experimental setup, which was employed to train various machine learning models, including artificial neural networks, random forests, and gradient boosting regressors. Preliminary findings demonstrate that the random forest model excels, achieving an R2 score exceeding 0.98 and a mean squared error of 0.14 in estimating electric efficiency. These outcomes could validate the potential of machine learning algorithms to support fuel cell integration into energy management systems capable of improving efficiency, pushing the transition towards sustainable energy solutions. [ABSTRACT FROM AUTHOR]

Optimizing the hydrogen value chain is essential to ensure hydrogen market uptake in replacing traditional fossil fuel energy and to achieve energy system decarbonization in the next years. The design of new plants and infrastructures will be the first step. However, wrong decisions would result in temporal, economic losses and, in the worst case, failures. Because huge investments are expected, decision makers have to be assisted for its success. Because no tools are available for the optimum design and geographical location of power to gas (P2G) and power to hydrogen (P2H) plants, the geographic information system (GIS) and mathematical optimization approaches were combined into a new tool developed by CNR-ITAE and the University of Bologna in the SuperP2G project, aiming to support the interested stakeholders in the investigation and selection of the optimum size, location, and operations of P2H and P2G industrial plants while minimizing the levelized cost of hydrogen (LCOH). In the present study, the tool has been applied to hydrogen mobility, specifically to investigate the conversion of the existing refuelling stations on Italian highways to hydrogen refuelling stations (HRSs). Middle-term (2030) and long-term (2050) scenarios were investigated. In 2030, a potential demand of between 7000 and 10,000 tons/year was estimated in Italy, increasing to between 32,600 and 72,500 tons/year in 2050. The optimum P2H plant configuration to supply the HRS was calculated in different scenarios. Despite the optimization, even if the levelized cost of hydrogen (LCOH) reduces from 7.0–7.5 €/kg in 2030 to 5.6–6.2 €/kg in 2050, the results demonstrate that the replacement of the traditional fuels, i.e., gasoline, diesel, and liquefied petroleum gases (LPGs), will be disadvantaged without incentives or any other economic supporting schemes. [ABSTRACT FROM AUTHOR]

The need for innovative heating and cooling systems to decarbonize the building sector is widely recognized. It is especially important to increase the share of renewables at building level by maximizing self-consumption and reducing the primary energy demand. Accordingly, in the present paper, the results on a wide experimental campaign on a hybrid system are discussed. The system included a sorption module working as the topping cycle in a cascade configuration with a DC-driven vapor compression heat pump. A three-fluids heat exchanger with a phase change material (PCM), i.e., RT4 with nominal melting temperature of 4 °C, was installed on the evaporator side of the heat pump, for simultaneous operation as thermal storage and heat pumping purposes. The heat pump was connected to a DC-bus that included PV connection and electricity storage (batteries). Results showed that the energy efficiency of the heat pump in cascade operation was double compared to compression-only configuration and that, when simultaneously charging and discharging the latent storage in cascade configuration, no penalization in terms of efficiency compared to the compression-only configuration was measured. The self-sufficiency of the system was evaluated for three reference weeks in summer conditions of Athens climate and it was found that up to 100% of the electricity needed to drive the system could be self-produced for a modest cooling demand and up to 67% for the warmer conditions with high cooling demand. [ABSTRACT FROM AUTHOR]

In this paper, a new approach to modeling the hysteresis phenomenon of the open circuit voltage (OCV) of lithium-ion batteries and estimating the battery state of charge (SoC) is presented. A characterization procedure is proposed to identify the battery model parameters, in particular, those related to the hysteresis phenomenon and the transition between charging and discharging conditions. A linearization method is used to obtain a suitable trade-off between the model accuracy and a low computational cost, in order to allow the implementation of SoC estimation on common hardware platforms. The proposed characterization procedure and the model effectiveness for SoC estimation are experimentally verified using a real grid-connected storage system. A mixed algorithm is adopted for SoC estimation, which takes into account both the traditional Coulomb counting method and the developed model. The experimental comparison with the traditional approach and the obtained results show the feasibility of the proposed approach for accurate SoC estimation, even in the presence of low-accuracy measurement transducers. [ABSTRACT FROM AUTHOR]