WC/CNT, WC/CNT/Pt Composite Material and Preparation Process Therefor and Use Thereof. (International Patent)

(EN)Disclosed are a WC/CNT， WC/CNT/Pt composite material and a preparation process therefor and use thereof. The WC/CNT/Pt composite material comprises mesoporous spherical tungsten carbide with a diameter of 1-5 microns, carbon nanotubes and platinum nano particles, with the carbon nanotubes growing on the surface of the mesoporous spherical tungsten carbide and expanding outward, and the platinum nano particles growing on the surfaces of the mesoporous spherical tungsten carbide and carbon nanotubes. The WC/CNT composite material comprises mesoporous spherical tungsten carbide with a diameter of 1-5 microns, and carbon nanotubes, with the carbon nanotubes growing on the surface of the mesoporous spherical tungsten carbide and expanding outward. The WC/CNT/Pt composite material of the present invention can be used as an electro-catalyst in a methanol flue battery, significantly improving the catalytic conversion rate and the service life of the catalyst. The WC/CNT composite material can be used as an electro-catalyst in the electro-reduction of a nitro aromatic compound, significantly improving the efficiency of organic electro-synthesis.

WC/CNT, WC/CNT/Pt Composite Material and Preparation Process Therefor and Use Thereof. (US patent)

Disclosed are a WC/CNT， WC/CNT/Pt composite material and a preparation process therefor and use thereof. The WC/CNT/Pt composite material comprises mesoporous spherical tungsten carbide with a diameter of 1-5 microns, carbon nanotubes and platinum nano particles, with the carbon nanotubes growing on the surface of the mesoporous spherical tungsten carbide and expanding outward, and the platinum nano particles growing on the surfaces of the mesoporous spherical tungsten carbide and carbon nanotubes. The WC/CNT composite material comprises mesoporous spherical tungsten carbide with a diameter of 1-5 microns, and carbon nanotubes, with the carbon nanotubes growing on the surface of the mesoporous spherical tungsten carbide and expanding outward. The WC/CNT/Pt composite material of the present invention can be used as an electro-catalyst in a methanol flue battery, significantly improving the catalytic conversion rate and the service life of the catalyst. The WC/CNT composite material can be used as an electro-catalyst in the electro-reduction of a nitro aromatic compound, significantly improving the efficiency of organic electro-synthesis.

Mathematics and reading difficulty subtypes: Minor phonological influences on mathematics for 5-7 year olds

Linguistic influences in mathematics have previously been explored throughsubtyping methodology and by taking advantage of the componential nature ofmathematics and variations in language requirements that exist across tasks. Thepresent longitudinal investigation aimed to examine the language requirements of mathematical tasks in young children aged 5-7 years. Initially, 256 children were screened for mathematics and reading difficulties using standardised measures. Those scoring at or below the 35th percentile on either dimension were classified as having difficulty. From this screening, 115 children were allocated to each of the MD (n=26), MDRD (n=32), reading difficulty (RD, n=22) and typically achieving (TA, n=35) subtypes. These children were tested at four time points, separated by six monthly intervals, on a battery of seven mathematical tasks. Growth curve analysis indicated that, in contrast to previous research on older children, young children with MD and MDRD had very similar patterns of development on all mathematical tasks. Overall, the subtype comparisons suggested that language played only a minor mediating role in most tasks, and this was secondary in importance to non-verbal skills. Correlational evidence suggested that children from the different subtypescould have been using different mixes of verbal and non-verbal strategies to solve the mathematical problems.

In situ diagnostics and prognostics of solder fatigue in IGBT modules for electric vehicle drives

This paper proposes an in situ diagnostic and prognostic (D&P) technology to monitor the health condition of insulated gate bipolar transistors (IGBTs) used in EVs with a focus on the IGBTs' solder layer fatigue. IGBTs' thermal impedance and the junction temperature can be used as health indicators for through-life condition monitoring (CM) where the terminal characteristics are measured and the devices' internal temperature-sensitive parameters are employed as temperature sensors to estimate the junction temperature. An auxiliary power supply unit, which can be converted from the battery's 12-V dc supply, provides power to the in situ test circuits and CM data can be stored in the on-board data-logger for further offline analysis. The proposed method is experimentally validated on the developed test circuitry and also compared with finite-element thermoelectrical simulation. The test results from thermal cycling are also compared with acoustic microscope and thermal images. The developed circuitry is proved to be effective to detect solder fatigue while each IGBT in the converter can be examined sequentially during red-light stopping or services. The D&P circuitry can utilize existing on-board hardware and be embedded in the IGBT's gate drive unit.

Impact of electric vehicles on a carbon constrained power system—a post 2020 case study

Electric vehicles (EVs) offer great potential to move from fossil fuel dependency in transport once some of the technical barriers related to battery reliability and grid integration are resolved. The European Union has set a target to achieve a 10% reduction in greenhouse gas emissions by 2020 relative to 2005 levels. This target is binding in all the European Union member states. If electric vehicle issues are overcome then the challenge is to use as much renewable energy as possible to achieve this target. In this paper, the impacts of electric vehicle charged in the all-Ireland single wholesale electricity market after the 2020 deadline passes is investigated using a power system dispatch model. For the purpose of this work it is assumed that a 10% electric vehicle target in the Republic of Ireland is not achieved, but instead 8% is reached by 2025 considering the slow market uptake of electric vehicles. Our experimental study shows that the increasing penetration of EVs could contribute to approach the target of the EU and Ireland government on emissions reduction, regardless of different charging scenarios. Furthermore, among various charging scenarios, the off-peak charging is the best approach, contributing 2.07% to the target of 10% reduction of Greenhouse gas emissions by 2025.

Non-convex dynamic economic/environmental dispatch with plug-in electric vehicle loads

Electric vehicles are a key prospect for future transportation. A large penetration of electric vehicles has the potential to reduce the global fossil fuel consumption and hence the greenhouse gas emissions and air pollution. However, the additional stochastic loads imposed by plug-in electric vehicles will possibly introduce significant changes to existing load profiles. In his paper, electric vehicles loads are integrated into an 5-unit system using a non-convex dynamic dispatch model. The actual infrastructure characteristics including valve-point effects, load balance constrains and transmission loss have been included in the model. Multiple load profiles are comparatively studied and compared in terms of economic and environmental impacts in order o identify patterns to charge properly. The study as expected shows ha off-peak charging is the best scenario with respect to using less fuels and producing less emissions.

Computational scheduling methods for integrating plug-in electric vehicles with power systems: a review

Traditional internal combustion engine vehicles are a major contributor to global greenhouse gas emissions and other air pollutants, such as particulate matter and nitrogen oxides. If the tail pipe point emissions could be managed centrally without reducing the commercial and personal user functionalities, then one of the most attractive solutions for achieving a significant reduction of emissions in the transport sector would be the mass deployment of electric vehicles. Though electric vehicle sales are still hindered by battery performance, cost and a few other technological bottlenecks, focused commercialisation and support from government policies are encouraging large scale electric vehicle adoptions. The mass proliferation of plug-in electric vehicles is likely to bring a significant additional electric load onto the grid creating a highly complex operational problem for power system operators. Electric vehicle batteries also have the ability to act as energy storage points on the distribution system. This double charge and storage impact of many uncontrollable small kW loads, as consumers will want maximum flexibility, on a distribution system which was originally not designed for such operations has the potential to be detrimental to grid balancing. Intelligent scheduling methods if established correctly could smoothly integrate electric vehicles onto the grid. Intelligent scheduling methods will help to avoid cycling of large combustion plants, using expensive fossil fuel peaking plant, match renewable generation to electric vehicle charging and not overload the distribution system causing a reduction in power quality. In this paper, a state-of-the-art review of scheduling methods to integrate plug-in electric vehicles are reviewed, examined and categorised based on their computational techniques. Thus, in addition to various existing approaches covering analytical scheduling, conventional optimisation methods (e.g. linear, non-linear mixed integer programming and dynamic programming), and game theory, meta-heuristic algorithms including genetic algorithm and particle swarm optimisation, are all comprehensively surveyed, offering a systematic reference for grid scheduling considering intelligent electric vehicle integration.

Investigation into the potential of using micro gas turbines in plug-in hybrid electric vehicles

The transport sector is considered to be one of the most dependent sectors on fossil fuels. Meeting ecological, social and economic demands throughout the sector has got increasingly important in recent times. A passenger vehicle with a more environmentally friendly propulsion system is the hybrid electric vehicle. Combining an internal combustion engine and an electric motor offers the potential to reduce carbon dioxide emissions. The overall objective of this research is to provide an appraisal of the use of a micro gas turbine as the range extender in a plug-in hybrid electric vehicle. In this application, the gas turbine can always operate at its most efficient operating point as its only requirement is to recharge the battery. For this reason, it is highly suitable for this purpose. Gas turbines offer many benefits over traditional internal combustion engines which are traditionally used in this application. They offer a high power-to-weight ratio, multi-fuel capability and relatively low emission levels due to continuous combustion.

A techno-economic evaluation of changing from conventional vehicles to range extended electric vehicles

Transportation accounts for 22% of greenhouse gas emissions in the UK, and increases to 25% in Northern Ireland. Surface transport carbon dioxide emissions, consisting of road and rail, are dominated by cars. Demand for mobility is rising rapidly and vehicle numbers are expected to more than double by 2050. Car manufacturers are working towards reducing their carbon footprint through improving fuel efficiency and controlling exhaust emissions. Fuel efficiency is now a key consideration of consumers purchasing a new vehicle. While measures have been taken to help to reduce pollutants, in the future, alternative technologies will have to be used in the transportation industry to achieve sustainability. There are currently many alternatives to the market leader, the internal combustion engine. These alternatives include hydrogen fuel cell vehicles and electric vehicles, a term which is widely used to cover battery electric vehicles, plug-in hybrid electric vehicles and extended-range electric vehicles. This study draws direct comparisons measuring the differing performance in terms of fuel consumption, carbon emissions and range of a typical family saloon car using different fuel types. These comparisons will then be analysed to see what effect switching from a conventionally fuelled vehicle to a range extended electric vehicle would have not only on the end user, but also the UK government.

A new self-learning TLBO algorithm for RBF neural modelling of batteries in electric vehicles

One of the main purposes of building a battery model is for monitoring and control during battery charging/discharging as well as for estimating key factors of batteries such as the state of charge for electric vehicles. However, the model based on the electrochemical reactions within the batteries is highly complex and difficult to compute using conventional approaches. Radial basis function (RBF) neural networks have been widely used to model complex systems for estimation and control purpose, while the optimization of both the linear and non-linear parameters in the RBF model remains a key issue. A recently proposed meta-heuristic algorithm named Teaching-Learning-Based Optimization (TLBO) is free of presetting algorithm parameters and performs well in non-linear optimization. In this paper, a novel self-learning TLBO based RBF model is proposed for modelling electric vehicle batteries using RBF neural networks. The modelling approach has been applied to two battery testing data sets and compared with some other RBF based battery models, the training and validation results confirm the efficacy of the proposed method.

An integrated approach for real-time model-based state-of-charge estimation of lithium-ion batteries

Lithium-ion batteries have been widely adopted in electric vehicles (EVs), and accurate state of charge (SOC) estimation is of paramount importance for the EV battery management system. Though a number of methods have been proposed, the SOC estimation for Lithium-ion batteries, such as LiFePo4 battery, however, faces two key challenges: the flat open circuit voltage (OCV) vs SOC relationship for some SOC ranges and the hysteresis effect. To address these problems, an integrated approach for real-time model-based SOC estimation of Lithium-ion batteries is proposed in this paper. Firstly, an auto-regression model is adopted to reproduce the battery terminal behaviour, combined with a non-linear complementary model to capture the hysteresis effect. The model parameters, including linear parameters and non-linear parameters, are optimized off-line using a hybrid optimization method that combines a meta-heuristic method (i.e., the teaching learning based optimization method) and the least square method. Secondly, using the trained model, two real-time model-based SOC estimation methods are presented, one based on the real-time battery OCV regression model achieved through weighted recursive least square method, and the other based on the state estimation using the extended Kalman filter method (EKF). To tackle the problem caused by the flat OCV-vs-SOC segments when the OCV-based SOC estimation method is adopted, a method combining the coulombic counting and the OCV-based method is proposed. Finally, modelling results and SOC estimation results are presented and analysed using the data collected from LiFePo4 battery cell. The results confirmed the effectiveness of the proposed approach, in particular the joint-EKF method.

An effective three-dimensional ordered mesoporous ZnCo2O4 as electrocatalyst for Li-O2 batteries

Three-dimensional ordered mesoporous (3DOM) ZnCo₂O₄ materials have been synthesized via a hard template and used as bifunctional electrocatalysts for rechargeable Li-O₂ batteries. The as-prepared ZnCo₂O₄ nanoparticles possess a high specific surface area of 127.2 m² g^-1 and a spinel crystalline structure. The Li-O₂ battery utilizing 3DOM ZnCo₂O₄ shows a higher specific capacity of 6024 mAh g^-1 than that with pure Ketjen black (KB). Moreover, the ZnCo₂O₄-based electrode enables much enhanced cyclability with a smaller discharge-recharge voltage gap than that of the carbon-only cathode. Such excellent catalytic performance of ZnCo₂O₄ could be associated with its larger surface area and 3D ordered mesoporous structure

A design strategy of large grain lithium-rich layered oxides for lithium-ion batteries cathode

Li-rich materials are considered the most promising for Li-ion battery cathodes, as high capacity can be achieved. However, poor cycling stability is a critical drawback that leads to poor capacity retention. Here a strategy is used to synthesize a large-grain lithium-rich layered oxides to overcome this difficulty without sacrificing rate capability. This material is designed with micron scale grain with a width of about 300 nm and length of 1-3 μm. This unique structure has a better ability to overcome stress-induced structural collapse caused by Li-ion insertion/extraction and reduce the dissolution of Mn ions, which enable a reversible and stable capacity. As a result, this cathode material delivered a highest discharge capacity of around 308 mAh g^-1 at a current density of 30 mA g^-1 with retention of 88.3% (according to the highest discharge capacity) after 100 cycles, 190 mAh g^-1 at a current density of 300 mA g^-1 and almost no capacity fading after 100 cycles. Therefore, Lithium-rich material of large-grain structure is a promising cathode candidate in Lithium-ion batteries with high capacity and high cycle stability for application. This strategy of large grain may furthermore open the door to synthesize the other complex architectures for various applications.

One-dimensional porous La0.5Sr0.5CoO2.91 nanotubes as a highly efficient electrocatalyst for rechargeable lithium-oxygen batteries

The electrochemical performance of one-dimensional porous La_0.5Sr_0.5CoO_2.91 nanotubes as a cathode catalyst for rechargeable nonaqueous lithium-oxygen (Li-O₂) batteries is reported here for the first time. In this study, one-dimensional porous La_0.5Sr_0.5CoO_2.91 nanotubes were prepared by a simple and efficient electrospinning technique. These materials displayed an initial discharge capacity of 7205 mAh g^-1 with a plateau at around 2.66 V at a current density of 100 mA g^-1. It was found that the La_0.5Sr_0.5CoO_2.91 nanotubes promoted both oxygen reduction and oxygen evolution reactions in alkaline media and a nonaqueous electrolyte, thereby improving the energy and coulombic efficiency of the Li-O₂ batteries. The cyclability was maintained for 85 cycles without any sharp decay under a limited discharge depth of 1000 mAh g^-1, suggesting that such a bifunctional electrocatalyst is a promising candidate for the oxygen electrode in Li-O₂ batteries.

Three-dimensional graphene-Co3O4 cathodes for rechargeable Li-O2 batteries

A three-dimensional (3D) graphene-Co₃O₄ electrode was prepared by a two-step method in which graphene was initially deposited on a Ni foam with Co₃O₄ then grown on the resulting graphene structure. Cross-linked Co₃O₄ nanosheets with an open pore structure were fully and vertically distributed throughout the graphene skeleton. The free-standing and binder-free monolithic electrode was used directly as a cathode in a Li-O₂ battery. This composite structure exhibited enhanced performance with a specific capacity of 2453 mA h g^-1 at 0.1 mA cm^-2 and 62 stable cycles with 583 mA h g^-1 (1000 mA h g_carbon^-1). The excellent electrochemical performance is associated with the unique architecture and superior catalytic activity of the 3D electrode.