Kalman-variant estimators for state of charge in lithium-sulfur batteries

Lithium-sulfur batteries are now commercially available, offering high specific energy density, low production costs and high safety. However, there is no commercially-available battery management system for them, and there are no published methods for determining state of charge in situ. This paper describes a study to address this gap. The properties and behaviours of lithium-sulfur are briefly introduced, and the applicability of ‘standard’ lithium-ion state-of-charge estimation methods is explored. Open-circuit voltage methods and ‘Coulomb counting’ are found to have a poor fit for lithium-sulfur, and model-based methods, particularly recursive Bayesian filters, are identified as showing strong promise. Three recursive Bayesian filters are implemented: an extended Kalman filter (EKF), an unscented Kalman filter (UKF) and a particle filter (PF). These estimators are tested through practical experimentation, considering both a pulse-discharge test and a test based on the New European Driving Cycle (NEDC). Experimentation is carried out at a constant temperature, mirroring the environment expected in the authors' target automotive application. It is shown that the estimators, which are based on a relatively simple equivalent-circuit–network model, can deliver useful results. If the three estimators implemented, the unscented Kalman filter gives the most robust and accurate performance, with an acceptable computational effort

Financial-distress prediction of Islamic banks using tree-based stochastic techniques

Purpose Financial distress is a socially and economically important problem that affects companies the world over. Having the power to better understand – and hence aid businesses from failing, has the potential to save not only the company, but also potentially prevent economies from sustained downturn. Although Islamic banks constitute a fraction of total banking assets, their importance have been substantially increasing, as their asset growth rate has surpassed that of conventional banks in recent years. The paper aims to discuss these issues. Design/methodology/approach This paper uses a data set comprising 101 international publicly listed Islamic banks to work on advancing financial distress prediction (FDP) by utilising cutting-edge stochastic models, namely decision trees, stochastic gradient boosting and random forests. The most important variables pertaining to forecasting corporate failure are determined from an initial set of 18 variables. Findings The results indicate that the “Working Capital/Total Assets” ratio is the most crucial variable relating to forecasting financial distress using both the traditional “Altman Z-Score” and the “Altman Z-Score for Service Firms” methods. However, using the “Standardised Profits” method, the “Return on Revenue” ratio was found to be the most important variable. This provides empirical evidence to support the recommendations made by Basel Accords for assessing a bank’s capital risks, specifically in relation to the application to Islamic banking. Originality/value These findings provide a valuable addition to the limited literature surrounding Islamic banking in general, and FDP pertaining to Islamic banking in particular, by showcasing the most pertinent variables in forecasting financial distress so that appropriate proactive actions can be taken. </jats:sec

Fitting Prediction Rule Ensembles with R Package pre

Prediction rule ensembles (PREs) are sparse collections of rules, offering highly interpretable regression and classification models. This paper presents the R package pre, which derives PREs through the methodology of Friedman and Popescu (2008). The implementation and functionality of package pre is described and illustrated through application on a dataset on the prediction of depression. Furthermore, accuracy and sparsity of PREs is compared with that of single trees, random forest and lasso regression in four benchmark datasets. Results indicate that pre derives ensembles with predictive accuracy comparable to that of random forests, while using a smaller number of variables for prediction

Optimization in a Simulation Setting: Use of Function Approximation in Debt Strategy Analysis

The stochastic simulation model suggested by Bolder (2003) for the analysis of the federal government's debt-management strategy provides a wide variety of useful information. It does not, however, assist in determining an optimal debt-management strategy for the government in its current form. Including optimization in the debt-strategy model would be useful, since it could substantially broaden the range of policy questions that can be addressed. Finding such an optimal strategy is nonetheless complicated by two challenges. First, performing optimization with traditional techniques in a simulation setting is computationally intractable. Second, it is necessary to define precisely what one means by an "optimal" debt strategy. The authors detail a possible approach for addressing these two challenges. They address the first challenge by approximating the numerically computed objective function using a function-approximation technique. They consider the use of ordinary least squares, kernel regression, multivariate adaptive regression splines, and projection-pursuit regressions as approximation algorithms. The second challenge is addressed by proposing a wide range of possible government objective functions and examining them in the context of an illustrative example. The authors' view is that the approach permits debt and fiscal managers to address a number of policy questions that could not be fully addressed with the current stochastic simulation engine.Debt management; Econometric and statistical methods; Fiscal policy; Financial markets