Batteries are the bottleneck technology of electric vehicles (EVs), which hosts complex and hardly observable internal chemical reactions. This paper presents a big data-driven battery management method utilizing the deep learning algorithm, with the ability to work stably under dynamic conditions and whole battery life cycle. First, a Deep Belief Network-Extreme Learning Machine (DBN-ELM) algorithm-based battery model is established to extract the deep structure features of battery data, and in which the rain-flow cycle counting algorithm is used to reflect the battery degradation phenomenon. Next, to improve real-time performance of Battery Management System (BMS), a conjunction working mode between the Cloud-based BMS (C-BMS) and BMS in vehicles (V-BMS) is proposed, and a battery State of Charge (SoC) estimation method based on the interaction between C-BMS and V-BMS is also presented. Using the battery data to verify the model effectiveness and accuracy, the error of the battery SoC estimation is within 3%.
In recent years, as a multifunctional application of photovoltaic technologies, building-integrated photovoltaic (BIPV) glazing is used to generate power while natural lighting is provided as part of building façade. Unlike those PV windows made by crystalline silicon solar cells, the semi-transparent cadmium telluride (CdTe) photovoltaic (STPV) windows can admit natural daylight with a certain degree of transmittance without any shading. Therefore, it can provide better visual comfort to occupants. Adopting STPV windows will also affect the overall building energy consumption due to the low solar heat gain coefficient (SHGC). The thermal feature of common STPV windows can be beneficial for reducing the cooling load in the summer. However, when adopting the STPV windows in the heating-dominated regions, it will increase the heating load as most of the solar heat gain will be blocked by the solar cells in the PV windows. To improve the thermal insulation performance of STPV glazing, a novel vacuum photovoltaic insulated glass unit (VPV IGU) is proposed. This paper investigated the overall energy performance of a typical office in Harbin mounted with the innovative vacuum PV glazing systems. Two different configurations of the vacuum PV glazing were compared by the simulation work conducted by EnergyPlus. The results show that the first configuration, which the vacuum glazing is the internal layer, has better thermal performance from April to October, while the second configuration, the one with the vacuum glazing as an external layer, has superior thermal performance in winter as solar heat gain through PV glazing can contribute to indoor heating. The combination of STPV glazing and vacuum glazing may provide a significant energy saving potential in cold regions of China
The understanding of wind turbine wake interactions in large wind farms contributes to control power losses and turbulence increases, which is crucial to optimize the design of a wind farm. The wake effect in complexterrain wind farms is much more complicated, and the related problems are still not investigated in depth. This study tries to fill in this research gap from the experimental aspect. This paper based on the wind field measurement in a typical complex-terrain wind farm in north China. The wind turbines are built in mountainous positions and the maximum height difference between wind turbines is 171.3m. A vertical-wind-mast-type lidar and a threedimensional-scanning-wind lidar were used to measure wind turbine wakes. The multiple wake effect downstream of four aligned wind turbines are investigated. The numerical experimental data and results are demonstrated in this paper. Huge experimental difficulties exist in deciding proper lidar scan strategies and adopting effective integration of measured data from various remote sensing platforms. This study also summarizes the difficulties and gives out the experience from the measurements, which is a guidance for the future measurements in the complex-terrain wind farms.
High penetration of intermittent renewable energy creates challenges to system integration. Large solar production during the day shifts peak grid demand to evening hours. These along with random nature of EV charging require distribution scale testbed for thorough analysis. In this paper a larger testbed system that is developed and deployed at the University of California, Riverside (UCR) is described. The technical challenges to implement this testbed and make it operational are discussed. A multiobjective problem is formulated and strategies based on that are developed for the implementation of three microgrids. Operational results and environmental benefits are presented including a zero net energy building (ZNE), battery energy storage, peak shaving to help the local utility on their historic highest demand day.
In this article, a robust machine-learning based computational framework that couples multi-layer neural network (MLNN) proxies and multi-objective particle swarm optimizer (MOPSO) to design wateralternative-CO2 injection (CO2-WAG) projects is presented. The proposed optimization protocol considers various objective functions including oil recovery and CO2 storage volume. Expert MLNN systems are trained and employed as surrogate models of the high-fidelity compositional simulator in the optimization workflow. A large volume of blind testing applications is employed to confirm the validities of the proxies. When multiple objective functions are considered, two approaches are employed to treat the objectives: the weighted sum method and Pareto-front-based scheme. A field scale implementation to Morrow-B formation at Farnsworth Unit (FWU) to optimize the tertiary recovery strategy is discussed. In this work, investigations will focus on comparing the optimum solution found by the aggregative objective function and the solution repository covered by the Pareto front, which considers the physical and operational constraints and reduces uncertainties involved by the multi-objective optimization process. Necessary trade-offs need to be decided using the solution repository to balance the project economics and CO2 storage amount.
The increasing penetration of renewable energy sources (RES), battery energy storage systems (BESS), and other loads native to DC, raises the question if a DC backbone topology may be more suitable compared to the commonly used AC. A number of studies that focused on this question, demonstrate a wide range of results that depending on the application and external conditions simulated. In this work, simulated DC and AC topologies are tested in an office building located in Belgium using a modelling framework developed in Modelica. The building is assumed to have a large penetration of building-integrated photovoltaics (BIPV) and battery energy storage systems (BESS) and a wide range of key performance indicators (KPI) are used to quantify the comparison. The DC topologies demonstrate increased performance when the BIPV system produces large amounts of power. The performance gains may be further enhanced by sizing optimally the less efficiency system components.
—Membrane technology is an attractive approach for CO2 capture from flue gas derived from coal-power plants, due to its inherent advantages such as high energy-efficiency, small footprint and potentially low cost. The state-of-theart membranes are based on polar poly(ethylene oxide) (PEO), which exhibit high CO2 permeability and high CO2/N2 selectivity. In this work, these PEO containing materials were doped with zeolitic imidazolate framework (ZIF-8) nanoparticles to improve CO2 permeability. Specifically, ZIF-8 was incorporated into polymers prepared from poly(ethylene glycol) diacrylate (PEGDA). These ZIF-8 nanoparticles had high porosities and average pore aperture of 0.34 nm that was between the molecule size of CO2 (0.33 nm) and N2 (0.364 nm), indicating their potential of achieving high CO2 permeability and CO2/N2 selectivity. The in situ synthesis of ZIF-8 provided uniform nanoparticle size of about 100 nm, enabling a good dispersion in polymers at loadings as high as 50 wt%. Increasing the ZIF-8 loading dramatically increased CO2 permeability. For example, adding 10 wt% ZIF-8 increased the CO2 permeability from 130.8 Barrers in a polymer prepared from PEGDA to 318.3 Barrers without changing the CO2/N2 selectivity. At a loading of 50 wt%, the nanocomposite exhibited a CO2 permeability of 1334.5 Barrers and CO2/N2 selectivity of 33.1 at 35 oC, which was one of the best separation properties reported in the literature.
—In order to solve the problems of deficient CO2 adsorption sites on Zn/Co zeolitic imidazolate frameworks(ZIFs), Zn/Co ZIFs were thermally treated to promote physical adsorption sites on Zn-N and Co-N bonds and then impregnated with polyethyleneimine (PEI) to promote -NH- and -NH2- chemical adsorption sites. The CO2 adsorption capacity detected on Micromeritics ASAP 2020C increased by 53% to 1.07mmol/g at 298K and 1bar, when Zn/Co ZIF was treated at optimal temperature of 450 ºC to obtain the maximum Me-N2 unsaturated adsorption sites. This was because of a partial cleavage of coordination bonds between Zn-N, Co-N, C=N and C-N along with dissociation of rationally free methyl groups in the framework ligands, which was supported on density functional theory (DFT) calculation. The Zn/Co ZIF treated at 450 ºC and then impregnated with 40wt% PEI exhibited the highest CO2 adsorption capacity of 1.82 mmol/g under the condition of at 298K and 1bar, which was 2.6 times higher than that of raw Zn/Co ZIF. In addition, this adsorbent is proved to be regenerable and stable during 9 cycle CO2 adsorptiondesorption tests, therefore, PEI- thermally treated Zn/Co ZIF exhibits a very promising application in CO2 capture from flue gas and natural gas.
The virtual water and CO2 along with the energy trade has reshaped the water resources utilization and CO2 emissions, making the energy, water and carbon emission management more complicated. We employed the multi-regional input-output analysis to examine the virtual water-CO2 trade-offs driven by energy demand among Chinese regions in 2010. We observe different spatial distribution for water and CO2 footprints, which have high intensity in south and north China respectively, though most coastal provinces have high water and CO2 footprints than inland provinces. The virtual water and CO2 are transferring from central and west provinces to the coast, consistent with the energy transmission network, but at the risk of aggravating the water stress and CO2 emissions in especially Yellow River region (including Shanxi, Shaanxi, Henan, Inner Mongolia). By paying attention to different energy sectors, the major exporters are different, indicating the higher water pressure in Yangtze River region (including Anhui, Hunan, Hubei, Jiangxi), higher CO2 emission increase in Yellow River region induced by electricity sector, while the northeast region in both aspects induced by oil refining sector. To mitigate water consumption and CO2 emission both directly and indirectly, the sector interactions between energy and others highlight the upstream water use by agriculture, and the electricity sector’s water use and CO2 emissions. The environmental impacts driven by the same energy demand in each province are examined. Finally, policy implications are discussed based on the findings.
Although CO2 foam flooding is a proven technology to improve oil recovery; it has been criticized for lack of long term stability in saline environment and in the presence of crude oil. To generate a more stable foam front in the presence of crude oil and to overcome the capillary forces destabilizing the foam lamella, polyelectrolyte complex nanoparticles (PECNP) conjugated with surfactant oligomers were introduced to the lamella generated by high salinity aqueous phase to improve the EOR performance and produced water compatibility of supercritical CO2 (scCO2) foams. The formation of vesicular structures containing electrostatically hinged complexes of PECNP and surfactant was verified via transmission electron microscopy (TEM) while the structural changes associated with molecular complexation were identified using Raman spectroscopy. Accordingly, optimized ratios of PECNP: surfactant were employed to generate the most stable scCO2 foam in high salinity produced water and to improve the recovery of the foam flooding process. Conducting core-flooding experiments in wide range of salinities indicated that the highest incremental oil recovery and the lowest residual oil saturation were achieved by prioritizing PECNP: surfactant scCO2 foam flood.