Classification and fault detection methods for fuel cell monitoring and quality control

In this paper, various types of fault detection methods for fuel cells are compared. For example, those that use a model based approach or a data driven approach or a combination of the two. The potential advantages and drawbacks of each method are discussed and comparisons between methods are made. In particular, classification algorithms are investigated, which separate a data set into classes or clusters based on some prior knowledge or measure of similarity. In particular, the application of classification methods to vectors of reconstructed currents by magnetic tomography or to vectors of magnetic field measurements directly is explored. Bases are simulated using the finite integration technique (FIT) and regularization techniques are employed to overcome ill-posedness. Fisher's linear discriminant is used to illustrate these concepts. Numerical experiments show that the ill-posedness of the magnetic tomography problem is a part of the classification problem on magnetic field measurements as well. This is independent of the particular working mode of the cell but influenced by the type of faulty behavior that is studied. The numerical results demonstrate the ill-posedness by the exponential decay behavior of the singular values for three examples of fault classes.

Microblock ionomers: a new concept in high temperature, swelling-resistant membranes for PEM fuel cells

A novel series of polyaromatic ionomers with similar equivalent weights but very different sulphonic acid distributions along the ionomer backbone has been designed and prepared. By synthetically organising the sequence-distribution so that it consists of fully defined ionic segments (containing singlets, doublets or quadruplets of sulphonic acid groups) alternating strictly with equally well-defined nonionic spacer segments, a new class of polymers which may be described as microblock ionomers has been developed. These materials exhibit very different properties and morphologies from analogous randomly substituted systems. Progressively extending the nonionic spacer length in the repeat unit (maintaining a constant equivalent weight by increasing the degree of sulphonation. of the ionic segment) leads to an increasing degree of nanophase separation between hydrophilic and hydrophobic domains in these materials. Membranes cast from ionomers with the more highly phase-separated morphologies show significantly higher onset temperatures for uncontrolled swelling in water. This new type of ionomer design has enabled the fabrication of swelling-resistant hydrocarbon membranes, suitable for fuel cell operation, with very much higher ion exchange capacities (>2 meq g(-1)) than those previously reported in the literature. When tested in a fuel cell at high temperature (120 degrees C) and low relative humidity (35% RH), the best microblock membrane matched the performance of Nafion 112. Moreover, comparative low load cycle testing of membrane -electrode assemblies suggests that the durability of the new membranes under conditions of high temperature and low relative humidity is superior to that of conventional perfluorinated materials.

Long term individual load forecast under different electrical vehicles uptake scenarios

More and more households are purchasing electric vehicles (EVs), and this will continue as we move towards a low carbon future. There are various projections as to the rate of EV uptake, but all predict an increase over the next ten years. Charging these EVs will produce one of the biggest loads on the low voltage network. To manage the network, we must not only take into account the number of EVs taken up, but where on the network they are charging, and at what time. To simulate the impact on the network from high, medium and low EV uptake (as outlined by the UK government), we present an agent-based model. We initialise the model to assign an EV to a household based on either random distribution or social influences - that is, a neighbour of an EV owner is more likely to also purchase an EV. Additionally, we examine the effect of peak behaviour on the network when charging is at day-time, night-time, or a mix of both. The model is implemented on a neighbourhood in south-east England using smart meter data (half hourly electricity readings) and real life charging patterns from an EV trial. Our results indicate that social influence can increase the peak demand on a local level (street or feeder), meaning that medium EV uptake can create higher peak demand than currently expected.

Hydrogen spillover on carbon-supported metal catalysts studied by inelastic neutron scattering. Surface vibrational states and hydrogen riding modes

Hydrogen spillover on carbon-supported precious metal catalysts has been investigated with inelastic neutron scattering (INS) spectroscopy. The aim, which was fully realized, was to identify spillover hydrogen on the carbon support. The inelastic neutron scattering spectra of Pt/C, Ru/C, and PtRu/C fuel cell catalysts dosed with hydrogen were determined in two sets of experiments: with the catalyst in the neutron beam and, using an annular cell, with carbon in the beam and catalyst pellets at the edge of the cell excluded from the beam. The vibrational modes observed in the INS spectra were assigned with reference to the INS of a polycyclic aromatic hydrocarbon, coronene, taken as a molecular model of a graphite layer, and with the aid of computational modeling. Two forms of spillover hydrogen were identified: H at edge sites of a graphite layer (formed after ambient dissociative chemisorption of H-2), and a weakly bound layer of mobile H atoms (formed by surface diffusion of H atoms after dissociative chernisorption of H-2 at 500 K). The INS spectra exhibited characteristic riding modes of H on carbon and on Pt or Ru. In these riding modes H atoms move in phase with vibrations of the carbon and metal lattices. The lattice modes are amplified by neutron scattering from the H atoms attached to lattice atoms. Uptake of hydrogen, and spillover, was greater for the Ru containing catalysts than for the Pt/C catalyst. The INS experiments have thus directly demonstrated H spillover to the carbon support of these metal catalysts.

Ion-conducting polymers and membranes comprising them

An ion-conducting polymer wherein at least 80% of the repeat units comprise an ion-conducting region and a spacer region is disclosed. The ion-conducting region has an aromatic backbone of one or more aromatic groups, wherein at least one ion-conducting functional group is attached to each aromatic group. The spacer region has an aromatic backbone of at least four aromatic groups, wherein no ion-conducting functional groups are attached to the aromatic backbone. The polymer is suitable for use as a fuel cell membrane, and can be incorporated into membrane electrode assemblies.

On discrimination algorithms for ill-posed problems with an application to magnetic tomography

In this paper we explore classification techniques for ill-posed problems. Two classes are linearly separable in some Hilbert space X if they can be separated by a hyperplane. We investigate stable separability, i.e. the case where we have a positive distance between two separating hyperplanes. When the data in the space Y is generated by a compact operator A applied to the system states ∈ X, we will show that in general we do not obtain stable separability in Y even if the problem in X is stably separable. In particular, we show this for the case where a nonlinear classification is generated from a non-convergent family of linear classes in X. We apply our results to the problem of quality control of fuel cells where we classify fuel cells according to their efficiency. We can potentially classify a fuel cell using either some external measured magnetic field or some internal current. However we cannot measure the current directly since we cannot access the fuel cell in operation. The first possibility is to apply discrimination techniques directly to the measured magnetic fields. The second approach first reconstructs currents and then carries out the classification on the current distributions. We show that both approaches need regularization and that the regularized classifications are not equivalent in general. Finally, we investigate a widely used linear classification algorithm Fisher's linear discriminant with respect to its ill-posedness when applied to data generated via a compact integral operator. We show that the method cannot stay stable when the number of measurement points becomes large.

The evolution of electricity demand and the role for demand side participation, in buildings and transport

This paper explores the possible evolution of UK electricity demand as we move along three potential transition pathways to a low carbon economy in 2050.The shift away from fossil fuels through the electrification of demand is discussed, particularly through the uptake of heat pumps and electric vehicles in the domestic and passenger transport sectors. Developments in the way people and institutions may use energy along each of the pathways are also considered and provide a rationale for the quantification of future annual electricity demands in various broad sectors. The paper then presents detailed modelling of hourly balancing of these demands in the context of potential low carbon generation mixes associated with the three pathways. In all cases, hourly balancing is shown to be a significant challenge. To minimise the need for conventional generation to operate with very low capacity factors, a variety of demand side participation measures are modelled and shown to provide significant benefits. Lastly, projections of greenhouse gas emissions from the UK and the imports of fossil fuels to the UK for each of the three pathways are presented.

Control methodologies: Peak reduction algorithms for DNO owned storage devices on the Low Voltage network

The Distribution Network Operators (DNOs) role is becoming more difficult as electric vehicles and electric heating penetrate the network, increasing the demand. As a result it becomes harder for the distribution networks infrastructure to remain within its operating constraints. Energy storage is a potential alternative to conventional network reinforcement such as upgrading cables and transformers. The research presented here in this paper shows that due to the volatile nature of the LV network, the control approach used for energy storage has a significant impact on performance. This paper presents and compares control methodologies for energy storage where the objective is to get the greatest possible peak demand reduction across the day from a pre-specified storage device. The results presented show the benefits and detriments of specific types of control on a storage device connected to a single phase of an LV network, using aggregated demand profiles based on real smart meter data from individual homes. The research demonstrates an important relationship between how predictable an aggregation is and the best control methodology required to achieve the objective.

The impact of location and type on the performance of Low-Voltage network connected Battery Energy Storage Systems

This paper assesses the impact of the location and configuration of Battery Energy Storage Systems (BESS) on Low-Voltage (LV) feeders. BESS are now being deployed on LV networks by Distribution Network Operators (DNOs) as an alternative to conventional reinforcement (e.g. upgrading cables and transformers) in response to increased electricity demand from new technologies such as electric vehicles. By storing energy during periods of low demand and then releasing that energy at times of high demand, the peak demand of a given LV substation on the grid can be reduced therefore mitigating or at least delaying the need for replacement and upgrade. However, existing research into this application of BESS tends to evaluate the aggregated impact of such systems at the substation level and does not systematically consider the impact of the location and configuration of BESS on the voltage profiles, losses and utilisation within a given feeder. In this paper, four configurations of BESS are considered: single-phase, unlinked three-phase, linked three-phase without storage for phase-balancing only, and linked three-phase with storage. These four configurations are then assessed based on models of two real LV networks. In each case, the impact of the BESS is systematically evaluated at every node in the LV network using Matlab linked with OpenDSS. The location and configuration of a BESS is shown to be critical when seeking the best overall network impact or when considering specific impacts on voltage, losses, or utilisation separately. Furthermore, the paper also demonstrates that phase-balancing without energy storage can provide much of the gains on unbalanced networks compared to systems with energy storage.