Global sensitivity analysis of the single particle lithium-ion battery model with electrolyte

The importance of global sensitivity analysis (GSA) has been well established in many scientific areas. However, despite its critical role in evaluating a model’s plausibility and relevance, most lithium ion battery models are published without any sensitivity analysis. In order to improve the lifetime performance of battery packs, researchers are investigating the application of physics based electrochemical models, such as the single particle model with electrolyte (SPMe). This is a challenging research area from both the parameter estimation and modelling perspective. One key challenge is the number of unknown parameters: the SPMe contains 31 parameters, many of which are themselves non-linear functions of other parameters. As such, relatively few authors have tackled this parameter estimation problem. This is exacerbated because there are no GSAs of the SPMe which have been published previously. This article addresses this gap in the literature and identifies the most sensitive parameter, preventing time being wasted on refining parameters which the output is insensitive to

Accelerated energy capacity measurement of lithium-ion cells to support future circular economy strategies for electric vehicles

Within the academic and industrial communities there has been an increasing desire to better understand the sustainability of producing vehicles that contain embedded electrochemical energy storage. Underpinning a number of studies that evaluate different circular economy strategies for the electric vehicle (EV) or Hybrid electric vehicle (HEV) battery system are implicit assumptions about the retained capacity or State of Health (SOH) of the battery. International standards and bestpractice guides exist that address the performance evaluation of both EV and HEV battery systems. However, a common theme is that the test duration can be excessive and last for a number of hours. The aim of this research is to assess whether energy capacity measurements of Li-ion cells can be accelerated; reducing the test duration to a value that may facilitate further EOL options. Experimental results are presented that highlight it is possible to significantly reduce the duration of the battery characterisation test by 70% - 90% while still retaining levels of measurement accuracy for retained energy capacity in the order of 1% for cell temperatures equal to 250C. Even at elevated temperatures of 400C, the peak measurement error was found to be only 3%. Based on these experimental results, a simple cost-function is formulated to highlight the flexibility of the proposed test framework. This approach would allow different organizations to prioritize the relative importance of test accuracy verses experimental test time when grading used Li-ion cells for different end-of-life (EOL) applications

Utilisation d'un réseau de neurones pour appliquer le modèle de Muskingum aux réseaux d'assainissement

L'application du modèle de Muskingum pour simuler l'écoulement à surface libre dans les canaux d'irrigation a été largement utilisée et validée. Par extension, ce modèle est également employé pour simuler les écoulements en réseau d'assainissement. Or, nous avons pu montrer des erreurs allant jusqu'à 80% du débit de pointe entre le modèle de Muskingum à paramètres fixes et le modèle de référence de Barré de Saint-Venant. Nous proposons une nouvelle paramétrisation du modèle de Muskingum pour l'écoulement en collecteur circulaire en réseau d'assainissement et ceci pour un large domaine de longueurs, pentes et diamètres de collecteurs. Ce nouveau modèle non-linéaire a été calé par minimisation d'une fonction objectif traduisant la proximité du modèle proposé avec les résultats de la résolution des équations de Barré de Saint-Venant pour des hydrogrammes rectangulaires. Un réseau de neurones a été utilisé pour paramétrer le modèle. Cette nouvelle application des équations de Muskingum permet l'obtention d'erreurs relatives moyennes inférieures à 6% sur la valeur et l'instant du débit de pointe, ceci dans le cas de collecteurs ayant jusqu'à 6500 m de longueur, des pentes variant entre 0.5% et 1% et des diamètres entre 150 et 2500 mm et des hydrogrammes de débit de pointe proche de la capacité du collecteur. Le modèle a également été validé sur un hydrogramme de forme quelconque.Certain towns and cities frequently suffer from failures of their sewer networks, especially in rainy weather. Pollution of the host environment, as the direct consequence of occasionally untimely spills, is not appreciated by the natural environment or the human population. Improving the quality of the natural environment therefore involves an increasingly sophisticated control of the hydraulics and the pollutant load in drainage systems, and especially in sewer networks. Real-time management of sewer networks can provide a solution for the protection of the natural environment. In this case, control strategies are provided for the sluices and pumps of the sewer network during a rainy event to minimize the urban effluent. Moreover, a better understanding and modeling of the transport of pollution in the mains is required.To that end, not only must the hydraulic operation of the mains be correctly modeled (shape of the hydrograph, value and temporal position of the peak flow), but this numerical model must also be stable and converge towards the solution, irrespective of the initial conditions for modeling of the pollution, and the computer time must be compatible with the requirements of real-time management. The most representative model of unidimensional flows is that of Barré de Saint-Venant (1871). The non-linearity of the model, resulting in difficulties in solving these equations, together with the computer time required, are such that not all the criteria for a real-time application can be met. The conceptual equations model of Muskingum is another model that can be used.In the case of round sewerage mains with a slope ranging from practically nil to a few per-thousandths and a few kilometers long, the K and α coefficients traditionally used do not yield correct results with respect to the benchmark model of Barré de Saint-Venant. To keep the advantages of the simplification of the Muskingum equations, and to avoid having to solve the Barré de Saint Venant system, we propose new parameters for the Muskingum equations and we use optimization and correlation calculation techniques using neural networks.In modeling the mains of a sewer network, the discretization of their length, within the usual limits [50 m; 1000 m] is chosen empirically. This discretization plays an essential part in the propagation of the wave in a main. To take this effect into account, the round main of length L is discretized into N sections, and K is expressed on the basis of the maximum speed of the flow Vmax. The model setting parameters are now N and α, and will be calibrated for a wide range of slopes, lengths and flow rates for round mains with a constant roughness.The calculation procedure is as follows: - Setting of the optimal values of N and alpha giving results close to those calculated by Barré de Saint Venant; - Determination of correlations of the parameters N and alpha according to the slope, length and diameter; - Validation of the Muskingum model in relation to that of Barré de Saint-Venant. The parameters alpha and N are set by minimizing an objective function giving the agreement between the results of the hydraulic simulations by Barré de Saint-Venant and the simulations of the proposed model. The objective function is defined by the sum of the relative quadratic deviations of the values and times of maximum flow rates. The maximum errors are in fact reduced from 90% to 10% on peak flows and from 30% to 10% at a given point in time during the peak flow. The mean error is reduced forty-fold for peak flow, and five-fold in the temporal position, with a reduction of the same order for the standard deviations. Correlations of alpha and N are sought according to the slope, length and diameter of the mains modeled. As linear type relations failed to provide satisfactory results, the multi-layer Perceptron type (artificial) neural network model was used. The model includes 3 inputs and 2 outputs. The first, essential stage consists of finding the optimal number of neurons in the masked layer. It is important to mention that despite maximum errors of 40% and 20% on the prediction of time and peak flow rate, mean errors of only 3% and 4% are observed. Given this result, 4 neurons were chosen in the masked layer. This model therefore includes 3 inputs, 4 neurons in the masked layer, and 2 outputs. Following the learning phase with the results of the optimization phase, the so-called prediction phase was then performed. This consists of using the neural network with data with intermediary values with respect to those used in the learning phase. The neural network is used solely to predict values within the minimum and maximum limits of the learning phase. The prediction (or validation) phase revealed that the mean errors are in the order of 2.7% for the peak flow value and 5.5% for the instant of the same flow. The choice of 4 neurons in the masked layer during the prediction phase gives results with the same order of magnitude as in the learning phase, thus validating the structure of the neural network chosen. Subsequently, the proximity of the value and of the time position of the maximum flow rate for the propagation of rectangular hydrograms was studied. The performance of the model proposed is now verified by studying the propagation of a hydrogram of any given shape. Use of this model, validated on a hydrogram of any given shape and presenting several peaks of different intensities, yields a satisfactory reproduction of the output hydrogram and is a distinct improvement on the classic Muskingum model

Estimation of health effects of prenatal methylmercury exposure using structural equation models

BACKGROUND: Observational studies in epidemiology always involve concerns regarding validity, especially measurement error, confounding, missing data, and other problems that may affect the study outcomes. Widely used standard statistical techniques, such as multiple regression analysis, may to some extent adjust for these shortcomings. However, structural equations may incorporate most of these considerations, thereby providing overall adjusted estimations of associations. This approach was used in a large epidemiological data set from a prospective study of developmental methyl-mercury toxicity. RESULTS: Structural equation models were developed for assessment of the association between biomarkers of prenatal mercury exposure and neuropsychological test scores in 7 year old children. Eleven neurobehavioral outcomes were grouped into motor function and verbally mediated function. Adjustment for local dependence and item bias was necessary for a satisfactory fit of the model, but had little impact on the estimated mercury effects. The mercury effect on the two latent neurobehavioral functions was similar to the strongest effects seen for individual test scores of motor function and verbal skills. Adjustment for contaminant exposure to poly chlorinated biphenyls (PCBs) changed the estimates only marginally, but the mercury effect could be reduced to non-significance by assuming a large measurement error for the PCB biomarker. CONCLUSIONS: The structural equation analysis allows correction for measurement error in exposure variables, incorporation of multiple outcomes and incomplete cases. This approach therefore deserves to be applied more frequently in the analysis of complex epidemiological data sets

Cycle life of lithium ion batteries after flash cryogenic freezing

Growing global sales of electric vehicles (EVs) are raising concerns about the reverse logistics challenge of transporting damaged, defective and waste lithium ion battery (LIB) packs. The European Union Battery Directive stipulates that 50% of LIBs must be recycled and EV manufacturers are responsible for collection, treatment and recycling. The International Carriage of Dangerous Goods by Road requirement to transport damaged or defective LIB packs in approved explosion proof steel containers imposes expensive certification. Further, the physical weight and volume of LIB packaging increases transport costs of damaged or defective packs as part of a complete recycling or repurposing strategy. Cryogenic flash freezing (CFF) removes the possibility of thermal runaway in LIBs even in extreme abuse conditions. Meaning damaged or defective LIBs may be transported safely whilst cryogenically frozen. Herein, LIBs are cycled until 20% capacity fade to establish that CFF does not affect electrical performance (energy capacity and impedance) during ageing. This is demonstrated on two different cell chemistries and form factors. The potential to remanufacture or reuse cells/modules subjected to CFF supports circular economy principles through extending useful life and reducing raw material usage. Thereby improving the environmental sustainability of transitioning from internal combustion engines to EVs

Critical impact of Ehrlich-Schwöbel barrier on GaN surface morphology during homoepitaxial growth

We discuss the impact of kinetics, and in particular the effect of the Ehrlich-Schwöbel barrier (ESB), on the growth and surface morphology of homoepitaxial GaN layers. The presence of an ESB can lead to various self-assembled surface features, which strongly affect the surface roughness. We present an in-depth study of this phenomenon on GaN homoepitaxial layers grown by metal organic vapor phase epitaxy and molecular beam epitaxy. We show how a proper tuning of the growth parameters allows for the control of the surface morphology, independent of the growth technique

Thermal modeling of lithium ion batteries for temperature rise predictions in hybrid vehicle application

In order to develop a hybrid vehicle with lithium ion battery packs, it is necessary to understand the thermal behaviour of the lithium ion batteries used. This paper focuses on predicting the temperature rise of lithium ion batteries during a drive cycle in hybrid two wheeler applications. To predict the rise in temperature, a model is developed in Simulink, parameterized using the empirical parameters. The model is based on the Joule heating effect and heat capacity equation while considering the variation of internal resistance with respect to ambient temperature of operation, state of charge and C rate of operation. The internal resistance is measured by parameter evaluation testing through the pulse power characterisation method. To validate the Simulink model, the lithium ion batteries are tested on standard drive cycles and constant current discharges, and the rise in temperature is measured. The accuracy of the Simulink model was found to be ± 2.2°C, which is acceptable for this study and comparable to the other available models in the literature

Wolff-Parkinson-White syndrome in an adolescent with depression

Wolff-Parkinson-White (WPW) syndrome is a congenital disorder characterised by a macro-reentrant arrhythmia caused by preexcitation of the ventricles. A significant proportion of cases are detected incidentally in asymptomatic patients during routine checkups. Because little is known about the use of selective serotonin reuptake inhibitors in youths with preexisting cardiac disease, we report here a 15-year-old adolescent with asymptomatic WPW syndrome and severe depressive symptoms. An improvement in depressive symptoms was observed with sertraline therapy and the absence of cardiac adverse effects after 5 months of treatment suggested that sertraline could be used at therapeutic doses in adolescents with preexisting but asymptomatic WPW syndrome