Temperature prediction of a battery plays a significant role in terms of energy efficiency and safety of electric vehicles, as well as several kinds of electric and electronic devices. In this regard, it is crucial to identify an adequate model to study the thermal behavior of a battery. This article reports a comparative study on thermal modeling approaches by using a ${\mathrm{LiCoO}}_2$ 26650 lithium-ion battery, and provides a methodology to characterize electrothermal phenomena. Three approaches have been implemented numerically—a thermal lumped model, a 3D computational fluid dynamics model, and an electrochemical model based on Newman, Tiedemann, Gu and Kim formulation. The last two methods were solved using ANSYS Fluent software. Simulations were validated with experimental measurements of the cell surface temperature at constant current discharge and under a highway driving cycle. Results show that the three models are consistent with actual temperature measurements. The electrochemical method has the lower error at 0.5C. Nevertheless, this model provides the higher error ( $1.3$${}^{\circ }\mathrm{C}$) at 1.5C, where the maximum temperature increase of the cell was $18.1$${}^{\circ }\mathrm{C}$. Under the driving cycle, all the models are in the same order of error. Lumped model is suitable to simulate a wide range of battery operating conditions. Furthermore, this work was expanded to study heat generation, voltage and heat transfer coefficient under natural convection.

This paper analyses and compares the performance of a number of approaches implemented for the detection of capacity regeneration phenomena (measured in ampere-hours) in the degradation trend of energy storage devices, particularly Lithium-Ion battery cells. All implemented approaches are based on a combination of information-theoretic measures and sequential Monte Carlo methods for state estimation in nonlinear, non-Gaussian dynamic systems. Properties of information measures are conveniently used to quantify the impact of process measurements on the posterior probability density function of the state, assuming that sub-optimal Bayesian estimation algorithms (such as classic or risk-sensitive particle filters) are to be used to obtain an empirical representation of the system uncertainty. The proposed anomaly detection strategies are tested and evaluated both in terms of (i) detection time (early detection) and (ii) false alarm rates. Verification of detection schemes is performed using simulated data for battery State-Of-Health accelerated degradation tests, to ensure absolute knowledge on the time instant where a regeneration phenomenon occurs.

A model of the power coefficient of a mid-scale Magnus wind turbine using numerical solutions of the Blade Element Momentum Theory and symbolic regression is presented. A direct method is proposed for solving the nonlinear system of equations which govern the phenomena under study. The influence of the tip-speed ratio and the number, aspect ratio, and the angular speed of the cylinders on the turbine performance is obtained. Results show that the maximum power coefficient is on the order of 0.2, which is obtained with two low aspect ratio cylinders, a dimensionless cylinder speed ratio of 2, and a turbine tip-speed ratio between 2 and 3. The predicted power coefficient at low tip-speed ratio suggests that a Magnus turbine may be adequate in the urban environment.