Batteries: Imaging degradation

The degradation and failure of Li-ion batteries is strongly associated with electrode microstructure change upon (de)lithiation. Now, an operando X-ray tomography approach is shown to correlate changes in the microstructure of electrodes to cell performance, and thereby predict degradation pathways

On the origin and application of the Bruggeman correlation for analysing transport phenomena in electrochemical systems

The widely used Bruggeman equations correlate tortuosity factors of porous media with their porosity. Finding diverse application from optics to bubble formation, it received considerable attention in fuel cell and battery research, recently. The ability to estimate tortuous mass transport resistance based on porosity alone is attractive, because direct access to the tortuosity factors is notoriously difficult. The correlation, however, has limitations, which are not widely appreciated owing to the limited accessibility of the original manuscript. We retrace Bruggeman's derivation, together with its initial assumptions, and comment on validity and limitations apparent from the original work to offer some guidance on its use

TauFactor: An open-source application for calculating tortuosity factors from tomographic data

TauFactor is a MatLab application for efficiently calculating the tortuosity factor, as well as volume fractions, surface areas and triple phase boundary densities, from image based microstructural data. The tortuosity factor quantifies the apparent decrease in diffusive transport resulting from convolutions of the flow paths through porous media. TauFactor was originally developed to improve the understanding of electrode microstructures for batteries and fuel cells; however, the tortuosity factor has been of interest to a wide range of disciplines for over a century, including geoscience, biology and optics. It is still common practice to use correlations, such as that developed by Bruggeman, to approximate the tortuosity factor, but in recent years the increasing availability of 3D imaging techniques has spurred interest in calculating this quantity more directly. This tool provides a fast and accurate computational platform applicable to the big datasets (>10^8 voxels) typical of modern tomography, without requiring high computational power

Tortuosity in electrochemical devices: a review of calculation approaches

The tortuosity of a structure plays a vital role in the transport of mass and charge in electrochemical devices. Concentration polarisation losses at high current densities are caused by mass transport limitations and are thus a function of microstructural characteristics. As tortuosity is notoriously difficult to ascertain, a wide and diverse range of methods have been developed to extract the tortuosity of a structure. These methods differ significantly in terms of calculation approach and data preparation techniques. Here, a review of tortuosity calculation procedures applied in the field of electrochemical devices is presented to better understand the resulting values presented in the literature. Visible differences between calculation methods are observed, especially when using porosity–tortuosity relationships and when comparing geometric and flux-based tortuosity calculation approaches

Optimal integrated energy systems design incorporating variable renewable energy sources

The effect of variability in renewable input sources on the optimal design and reliability of an integrated energy system designed for off-grid mining operation is investigated via a two-stage approach. Firstly, possible energy system designs are generated by solving a deterministic non-linear programming (NLP) optimization problem to minimize the capital cost for a number of input scenarios. Two measures of reliability, the loss of power supply probability (LPSP) and energy index of reliability (EIR), are then evaluated for each design based on the minimization of the external energy required to satisfy load demands under a variety of input conditions. Two case studies of mining operations located in regions with different degrees of variability are presented. The results show that the degree of variability has an impact on the design configuration, cost and performance, and highlights the limitations associated with deterministic decision making for high variability systems

X-ray attenuation properties of commonly employed solid oxide fuel cell materials

X-ray nano CT has been vastly applied to study the microstructure of solid oxide fuel cell (SOFC) electrodes. One widely accepted indicator of electrochemical performance is the triple phase boundary (TPB): a location where the three materials responsible for ionic, electronic and gas-phase reactant transport are in contact. X-ray absorption tomography has been used extensively in the characterisation of these TPBs, utilising the different attenuation properties of the constituent materials. Here we present a quantitative comparison of the attenuation properties for elements commonly employed in solid oxide fuel cell materials

Temperature, Ageing and Thermal Management of Lithium-Ion Batteries

Heat generation and therefore thermal transport plays a critical role in ensuring performance, ageing and safety for lithium-ion batteries (LIB). Increased battery temperature is the most important ageing accelerator. Understanding and managing temperature and ageing for batteries in operation is thus a multiscale challenge, ranging from the micro/nanoscale within the single material layers to large, integrated LIB packs. This paper includes an extended literature survey of experimental studies on commercial cells investigating the capacity and performance degradation of LIB. It compares the degradation behavior in terms of the influence of operating conditions for different chemistries and cell sizes. A simple thermal model for linking some of these parameters together is presented as well. While the temperature appears to have a large impact on ageing acceleration above room temperature during cycling for all studied cells, the effect of SOC and C rate appear to be rather cell dependent.Through the application of new simulations, it is shown that during cell testing, the actual cell temperature can deviate severely from the reported temperature depending on the thermal management during testing and C rate. It is shown, that the battery lifetime reduction at high C rates can be for large parts due to an increase in temperature especially for high energy cells and poor cooling during cycling studies. Measuring and reporting the actual battery (surface) temperature allow for a proper interpretation of results and transferring results from laboratory experiments to real applications

Understanding transport phenomena in electrochemical energy devices via X-ray nano CT

Porous support layers in electrochemical devices ensure mechanical stability of membrane assemblies such as solid oxide fuel cells and oxygen transport membranes (OTMs). At the same time, porous layers affect diffusive mass transport of gaseous reactants and contribute to performance losses at high fuel utilisation and conversion ratios. Microstructural characteristics are vital to calculate mass transport phenomena, where tortuosity remains notoriously difficult to determine. Here, the tortuosity of tubular porous support layers of OTMs is evaluated via high resolution X-ray nano computed tomography. The high resolution reveals the complex microstructure of the samples to then execute a selection of image-based tortuosity calculation algorithms. Visible differences between geometric and flux-based algorithms are observed and have thus to be applied with caution

Machine learning as an online diagnostic tool for proton exchange membrane fuel cells

Proton exchange membrane fuel cells are considered a promising power supply system with high efficiency and zero emissions. They typically work within a relatively narrow range of temperature and humidity to achieve optimal performance; however, this makes the system difficult to control, leading to faults and accelerated degradation. Two main approaches can be used for diagnosis, limited data input which provides an unintrusive, rapid but limited analysis, or advanced characterisation that provides a more accurate diagnosis but often requires invasive or slow measurements. To provide an accurate diagnosis with rapid data acquisition, machine learning methods have shown great potential. However, there is a broad approach to the diagnostic algorithms and signals used in the field. This article provides a critical view of the current approaches and suggests recommendations for future methodologies of machine learning in fuel cell diagnostic applications