The Regeneration Games: Commodities, Gifts and the Economics of London 2012

This paper considers contradictions between two concurrent and tacit conceptions of the Olympic ‘legacy’, setting out one conception that understands the games and their legacies as gifts alongside and as counterpoint to the prevailing discourse, which conceives Olympic assets as commodities. The paper critically examines press and governmental discussion of legacy, in order to locate these in the context of a wider perspective contrasting ‘gift’ and ‘commodity’ Olympics – setting anthropological conceptions of gift-based sociality as a necessary supplement to contractual and dis-embedded socioeconomic organizational assumptions underpinning the commodity Olympics. Costbenefit planning is central to modern city building and mega-event delivery. The paper considers the insufficiency of this approach as the exclusive paradigm within which to frame and manage a dynamic socio-economic and cultural legacy arising from the 2012 games

Novel methods for measuring the thermal diffusivity and the thermal conductivity of a lithium-ion battery

Thermal conductivity is a fundamental parameter in every battery pack model. It allows for the calculation of internal temperature gradients which affect cell safety and cell degradation. The accuracy of the measurement for thermal conductivity is directly proportional to the accuracy of any thermal calculation. Currently the battery industry uses archaic methods for measuring this property which have errors up to 50 %. This includes the constituent material approach, the Searle's bar method, laser/Xeon flash and the transient plane source method. In this paper we detail three novel methods for measuring both the thermal conductivity and the thermal diffusivity to within 5.6 %. These have been specifically designed for bodies like lithium-ion batteries which are encased in a thermally conductive material. The novelty in these methods comes from maintaining a symmetrical thermal boundary condition about the middle of the cell. By using symmetric boundary conditions, the thermal pathway around the cell casing can be significantly reduced, leading to improved measurement accuracy. These novel methods represent the future for thermal characterisation of lithium-ion batteries. Continuing to use flawed measurement methods will only diminish the performance of battery packs and slow the rate of decarbonisation in the transport sector.</p

Diagnosing health in composite battery electrodes with explainable deep learning and partial charging data

Lithium-ion batteries with composite anodes of graphite and silicon are increasingly being used. However, their degradation pathways are complicated due to the blended nature of the electrodes, with graphite and silicon degrading at different rates. Here, we develop a deep learning health diagnostic framework to rapidly quantify and separate the different degradation rates of graphite and silicon in composite anodes using partial charging data. The convolutional neural network (CNN), trained with synthetic data, uses experimental partial charging data to diagnose electrode-level health of tested batteries, with errors of less than 3.1% (corresponding to the loss of active material reaching ∼75%). Sensitivity analysis of the capacity-voltage curve under different degradation modes is performed to provide a physically informed voltage window for diagnostics with partial charging data. By using the gradient-weighted class activation mapping approach, we provide explainable insights into how these CNNs work; highlighting regions of the voltage-curve to which they are most sensitive. Robustness is validated by introducing noise to the data, with no significant negative impact on the diagnostic accuracy for noise levels below 10 mV, thus highlighting the potential for deep learning approaches in the diagnostics of lithium-ion battery performance under real-world conditions. The framework presented here can be generalised to other cell formats and chemistries, providing robust and explainable battery diagnostics for both conventional single material electrodes, but also the more challenging composite electrodes.</p

Effect of thermal gradients on inhomogeneous degradation in lithium-ion batteries

Understanding lithium-ion battery degradation is critical to unlocking their full potential. Poor understanding leads to reduced energy and power density due to over-engineering, or conversely to increased safety risks and failure rates. Thermal management is necessary for all large battery packs, yet experimental studies have shown that the effect of thermal management on degradation is not understood sufficiently. Here we investigated the effect of thermal gradients on inhomogeneous degradation using a validated three-dimensional electro-thermal-degradation model. We have reproduced the effect of thermal gradients on degradation by running a distributed model over hundreds of cycles within hours and reproduced the positive feedback mechanism responsible for the accelerated rate of degradation. Thermal gradients of just 3 °C within the active region of a cell produced sufficient positive feedback to accelerate battery degradation by 300%. Here we show that the effects of inhomogeneous temperature and currents on degradation cannot and should not be ignored. Most attempts to reproduce realistic cell level degradation based upon a lumped model (i.e. no thermal gradients) have suffered from significant overfitting, leading to incorrect conclusions on the rate of degradation

Diffusion-aware voltage source: An equivalent circuit network to resolve lithium concentration gradients in active particles

Traditional equivalent circuit models (ECMs) have difficulties in estimating battery internal states due to the lack of relevant physics, such as the lithium diffusion in active particles. Here we configure a circuit network to describe the lithium diffusion and define it as a new high-level circuit element called diffusion-aware voltage source. The circuit representation is proven equivalent to the discretized diffusion equation. The new voltage source gives the electrode potential as a function of the surface concentration and thus automatically incorporates the diffusion overpotential. We show that an ECM with the proposed diffusion-aware voltage sources (called "shell ECM") can reproduce the single particle model simulation results, making it a trustworthy easy-to-implement substitute. Furthermore, the simplest shell ECM consisting of a single diffusion-aware voltage source and a resistor is validated against experimental constant-current discharges at various rates. The diffusion-aware voltage source can be used to measure diffusivity by fitting the diffusion resistance against experimental data. The viability of the shell ECM for onboard usage is confirmed by implementation into a battery management system of WAE Technologies. By tracking the internal concentration states, the shell ECM demonstrates robustness to dynamic applied-current profiles.Comment: 35 pages, 14 figure

PTFE mapping in gas diffusion media for PEMFCs using fluorescence microscopy

Differentiating between the various polytetrafluoroethylene based structures inside polymer electrolyte membrane fuel cells with a degree of certainty is necessary to optimize manufacturing processes and to investigate possible degradation mechanisms. We have developed a novel method using fluorescence microscopy for distinguishing the origin and location of PTFE and/or Nafion® in Membrane Electrode assemblies and the gas diffusion media from different sources and stages of processing. Fluorescent material was successfully diffused into the PTFE based structures in the GDM by addition to the ‘ink’ precursor for both the microporous layer and the catalyst layer; this made it possible to map separately both layers in a way that has not been reported before. It was found that hot pressing of membrane coated structures resulted in physical dispersion of those layers away from the membrane into the GDM itself. This fluorescence technique should be of interest to membrane electrode assembly manufacturers and fuel cell developers and could be used to track the degradation of different PTFE structures independently in the future

The heating triangle: A quantitative review of self-heating methods for lithium-ion batteries at low temperatures

Lithium-ion batteries at low temperatures have slow recharge times alongside reduced available power and energy. Battery heating is a viable way to address this issue, and self-heating techniques are appealing due to acceptable efficiency and speed. However, there are a lack of studies quantitatively comparing self-heating methods rather than qualitatively, because of the existence of many different batteries with varied heating parameters. In this work, we review the current state-of-the-art self-heating methods and propose the heating triangle as a new quantitative indicator for comparing self-heating methods, towards identifying/developing effective heating approaches. We define the heating triangle which considers three fundamental metrics: the specific heating rate (°C·g·J-1), coefficient of performance (COP) (-), and specific temperature difference (°C·hr), enabling a quantitative assessment of self-heating methods using data reported in the literature. Our analysis demonstrates that very similar metrics are observed for the same type of self-heating method, irrespective of the study case, supporting the universality of the proposed indicator. With the comparison insights, we identify research gaps and new avenues for developing advanced self-heating methods. This work demonstrates the value of the proposed heating triangle as a standardised approach to compare heating methods and drive innovation.Postprint (published version