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Efficiency of energy resource use is a key factor for a sustainable energy future. By matching the exergy levels of supply and demand, Energy Quality Management (EQM) of building energy supply systems may achieve more efficient use of energy resources. Beyond this, environmental impact of energy supply systems is another essential issue. This work addresses the influence of EQM on CO 2 emissions in the operation optimization of a Distributed Energy System (DES). A multi-objective linear programming problem is formulated to reduce energy costs and increase the overall exergy efficiency. Total CO 2 emissions are evaluated for the optimized operation strategies of the DES. The operators of the DES can choose the operation strategy from the Pareto front, based on their priorities and also aware of effects on CO 2 emissions. Results demonstrate more efficient use of energy resources and reduction in CO 2 emissions through EQM, as compared with conventional energy supply systems.

Efficiency of energy resource use is a key factor for a sustainable energy future. By matching the exergy levels of supply and demand, Energy Quality Management (EQM) of building energy supply systems may achieve more efficient use of energy resources. Beyond this, environmental impact of energy supply systems is another essential issue. This work addresses the influence of EQM on CO 2 emissions in the operation optimization of a Distributed Energy System (DES). A multi-objective linear programming problem is formulated to reduce energy costs and increase the overall exergy efficiency. Total CO 2 emissions are evaluated for the optimized operation strategies of the DES. The operators of the DES can choose the operation strategy from the Pareto front, based on their priorities and also aware of effects on CO 2 emissions. Results demonstrate more efficient use of energy resources and reduction in CO 2 emissions through EQM, as compared with conventional energy supply systems.

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.

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.

Exterior insulation finishing systems (EIFSs) can efficiently promote energy efficiency of buildings. In this study, an EIFS with high thermal efficiency is presented to improve the insulation behavior of building enclosure. Based on heat transfer analysis results, energy simulations of buildings with fire spread prevention structures were performed. Results revealed that heat flow through the wall increased by 10.3 % when using a metal rail to fix the insulation; in contrast, using non-combustible phenolic foam reduces heat flow by 37.4 %, satisfying the requirement for fire spread prevention structures. Additionally, the energy consumption decreased by 8.8 % when both mineral wool and phenolic foam were applied. Fire spread prevention structures are essential to improve the fire safety performance of buildings. This external insulation system efficiently promote energy saving in building; additionally, leveraging a phase change material to improve the thermal storage performance of the building can reduce energy consumption by up to 11.9 %.

Exterior insulation finishing systems (EIFSs) can efficiently promote energy efficiency of buildings. In this study, an EIFS with high thermal efficiency is presented to improve the insulation behavior of building enclosure. Based on heat transfer analysis results, energy simulations of buildings with fire spread prevention structures were performed. Results revealed that heat flow through the wall increased by 10.3 % when using a metal rail to fix the insulation; in contrast, using non-combustible phenolic foam reduces heat flow by 37.4 %, satisfying the requirement for fire spread prevention structures. Additionally, the energy consumption decreased by 8.8 % when both mineral wool and phenolic foam were applied. Fire spread prevention structures are essential to improve the fire safety performance of buildings. This external insulation system efficiently promote energy saving in building; additionally, leveraging a phase change material to improve the thermal storage performance of the building can reduce energy consumption by up to 11.9 %.