This work analyzes a real-life dataset of charging records to understand charging behaviors at public charging stations. Data analytics were carried out to understand the statistical distribution of EV charging time, peak charging period, charging duration, seasonal and yearly effects, etc. The study was carried out with charger-centric data of a public car park in England, containing more than 12,000 charging records from 2016 to 2020. The results found that rapid charger (up to 50kW DC) is 3 times more popular than the fast charger (single-phase 7kW) and delivers 4 times more energy than the fast charger. This work will greatly help Charge Point Operators and Power Distribution Networks Operators for EV charging infrastructures planning and operations.
This paper aims to quantify the effect of the Feed-in-Tariff (FiT) rates on the installed capacity of anaerobic digestion (AD) plants in the UK. The paper develops multiple Linear Regression models (LR) to understand the effect of the different factors that drive the diffusion of AD plants. Emerging results suggest that among the different incentives for AD, only the FiT has a significant effect. Secondly, this effect comes to play only after the announcements of revisions to the programme. And third, the results confirm that the FiT has a different effect for each plant size where the medium size plants are the most responsive to the programme, whilst the large plants are the least responsive.
Buildings consume a huge amount of energy and mainly utilize it for occupants’ thermal comfort satisfaction. Real-time thermal comfort assessment can enormously contribute to thermal comfort optimization and energy conservation in buildings. Existing thermal comfort models mainly focus on the real-time assessment of occupants’ current thermal comfort. However, in the transient thermal environment, occupants’ thermal comfort is unsteady and varies from time to time. Therefore, if we only assess occupants’ current thermal comfort, prediction error will be elicited. In order to address this problem, it is principally important to comprehend occupants’ real-time thermal sensation trend in the transient thermal environment. This study investigates a novel thermal sensation index that can directly represent an individual’s current thermal sensation trend. By incorporating the novel thermal sensation index into an ordinary thermal comfort model, a composite thermal comfort model is derived, which can simultaneously address an individuals’ current thermal comfort and current thermal sensation trend. Then, by utilizing a machine learning classification algorithm, we propose its intrusive assessment method using skin or clothing temperatures of ten local body parts measured by thermocouple thermometers and its non-intrusive assessment method using a low-cost portable infrared camera. The novel composite thermal comfort model can provide an early warning mechanism for thermal discomfort and contribute to energy conservation in buildings.
The performance of a proton exchange membrane fuel cell exhibits strong association with liquid transport in a gas diffusion layer. The content and distribution of Polytetrafluoroethylene are key factors that determine liquid transport behaviors in a gas diffusion layer. In this study, by employing a stochastic algorithm, a two-dimensional microstructure of a representative carbon paper type gas diffusion layer was reconstructed. Subsequently, the influence of Polytetrafluoroethylene content and various proposed gradient distributions of Polytetrafluoroethylene in the reconstructed gas diffusion layer on liquid transport behaviors was examined by implementing a two-phase Lattice Boltz-mann method. The results supported the findings that an increased content of Polytetrafluoroethylene in gas diffusion layer favored liquid removal, but an extremely high one could cause a remark decrease of the cor-responding porosity of the gas diffusion layer, hence weakening mass diffusion. An optimal gradient design of Polytetrafluoroethylene could enhance water removal performance of a gas diffusion layer reflected by a reduced liquid water saturation and liquid phase steady-state time, meanwhile could ensure an excellent mass diffusion with a relatively high effective porosity of gas diffusion layer, thereby benefitting fuel cell per-formance. The study here could provide guideline for the design of high performance of a fuel cell with a gradient gas diffusion layer.
Considering the hot weather annually and thus leading high cooling demands in Guangdong-Hong Kong-Macao Greater Bay Area (GBA) of China, a novel distributed cooling production system powered by gas-electric hybrid energy was proposed. The thermal, economic and environmental performances of this process were assessed. The conventional electric air conditioning cooling system was employed as a reference system to demonstrate the benefits of this proposed system. It was found that the total energy efficiency and energy saving rate of this newly proposed system were 201% and 27.8%, respectively, while the payback period was around 3 years. The CO2, SO2 and PM reduction ratios were found to be 54.8, 69.4 and 70.5%, respectively. Further discussion on the safety controlling methods has been included in order to reduce maintain operation cost and to achieve intelligence of this prototype
The coil material has an effect on the icing performance. In this paper, the dynamic simulation of the icing process of three different coil materials is carried out. The variation of temperature field and liquid phase rate over time at the characteristic sections were analyzed. The results show that the ice layer first appears on the lower and outer side of the coil and gradually wraps all the outer wall of the coil.The thermal conductivity of ice coil material has an effect on the icing process. Especially when the thermal conductivity of coil material is lower than that of ice, the lower the thermal conductivity, the longer the time required for icing. But it change when the thermal conductivity of coil material is higher than that of ice, the improvement of thermal conductivity of coil material has little effect on the time required for icing. The thermal conductivity of reinforced polyvinyl chloride is only 2.3 W/(m·K) higher than that of polyvinyl chloride, but the time required for water outside the coil to completely freeze is reduced by 46%. The thermal conductivity of steel is 37.5 W/(m·K) higher than that of reinforced polyvinyl chloride, but the time required for water outside the coil to completely freeze is only reduced by 7%. In terms of the time required for icing, reinforced polyvinyl chloride is expected to replace steel. The natural convection of water has an effect on the icing outside the coil.
The mixing of cold and hot fluids caused by inlet and outlet or natural convection in a heat strorage tank, interfere with temperature stratification will reduce the thermal performance of the heat storage tank. In this paper, a method of installing movable heat insulation board in a single tank heat storage is proposed. The temperature field of heat storage tank equipped with heat insulation board and traditional heat storage tank is simulated and analyzed. Results show that under static conditions, the mixing of cold and hot fluids in the traditional single tank will lead to rapid heat transfer, which is the main factor the maximum fluid temperature. The heat insulation board can prevent mixing, so as to ensure the heat storage system maintains higher exergy. The thermal performance of the heat storage tank can be improved by selecting materials with low thermal conductivity to make heat insulation board.
The battery is the primary power source of electrified vehicles (EV). Prediction of battery performances with digital models is essential for both the R&D stage and real-world operation. However, the battery model developed in the R&D stage is not suitable for all real-world conditions, and it will be good if it can be optimized online. This paper proposes an Online Double-layer System Identification (ODSI) scheme to calibrate a battery model for State-of-Health (SoH) prediction with measured data. To determine the unified settings for the base battery model, the ODSI scheme firstly conducts robust optimization in the lower layer based on offline particle swarm optimization (PSO). It then incorporates a deep convolutional neural network (DCNN) to the base model to enable knowledge transfer from offline optimization to online adaption for SoH prediction under different working conditions. By reducing the size of the learning dataset, the study indicates that the proposed scheme has high robustness of uncertainty management. Besides, the ODSI scheme saves the computation resource by avoiding training from scratch.
Compared with Pt/C catalysts, non-precious metal (NPM) catalysts, with the advantages of low price and high natural abundance, have received extensive attention as the competitive candidate for fuel cell cathode oxygen reduction reaction (ORR) catalyst. Due to the low metal content and active site density of traditional NPM catalysts, higher catalyst loading is commonly required in real fuel cell applications. However, the thicker catalyst layer will lead to the high mass transfer resistance, reducing the performance of NPM catalyst layer, especially in high current density region. This study provide a new idea for high Co-content catalyst design, based on the SiO2 coated zeolite imidazole framework (ZIF) material. By adjusting the Zn/Co ratio in the ZIF precursor and with or without the protection of and by exploring the influence of coated on the change of SiO2, the ZIF microstructure and Co nanoparticle size were carefully optimized. The introduction of SiO2 shell can effectively avoid the agglomeration of metal nanoparticles, during the high-temperature activation process. However, protective effect of SiO2 is also significantly different for ZIF precursors with different Zn/Co ratios. When the Co content in the catalyst precursor is higher (15%), SiO2 shell can significantly reduce the metal nanoparticle size, which provide a new insight for high metal content catalyst design of alkaline fuel cell cathode catalyst.
This study evaluates the sustainability of solar PV-based mini-grids for rural electrification in developing countries. A discounted cash flow method is used to compare the economic feasibility of a real-world solar-mini-grid and a diesel-fueled mini-grid located in West Africa and the subsidy needs of the two projects. It is found that both mini-grids currently need high subsidies due to demand stimulation problems and high distribution losses. Still, the results provide evidence that PV-based mini-grids are already economically feasible without subsidies if they are located in customer environments with an ability to pay (ATP) greater than 0.57 €/kWh (assuming soft loans, stimulated utilization rates as well as hardware cost decrease). The approach and findings are especially useful for mini-grid developers/operators and investors with a focus on rural electrification projects. This study further identifies cost reduction potentials by means of demand stimulation in green mini-grids.