The growing demand for electrical energy and the impact of global warming leads to a paradigm shift in the power sector. This has led to the increased usage of renewable energy sources. Due to the intermittent nature of the renewable sources of energy, devices capable of storing electrical energy are required to increase its reliability. The most common means of storing electrical energy is battery systems. Battery usage is increasing in the modern days, since all mobile systems such as electric vehicles, smart phones, laptops, etc., rely on the energy stored within the device to operate. The increased penetration rate of the battery system requires accurate modelling of charging profiles to optimise performance. This paper presents an extensive study of various battery models such as electrochemical models, mathematical models, circuit-oriented models and combined models for different types of batteries. It also discusses the advantages and drawbacks of these types of modelling. With AI emerging and accelerating all over the world, there is a scope for researchers to explore its application in multiple fields. Hence, this work discusses the application of several machine learning and meta heuristic algorithms for battery management systems. This work details the charging and discharging characteristics using the black box and grey box techniques for modelling the lithium-ion battery. The approaches, advantages and disadvantages of black box and grey box type battery modelling are analysed. In addition, analysis has been carried out for extracting parameters of a lithium-ion battery model using evolutionary algorithms.

Traditional Multi-Criteria Decision Making (MCDM) methods have now become outdated; therefore, most researchers are focusing on more robust hybrid MCDM models that combine two or more MCDM techniques to address decision-making problems. The authors attempted to create two novel hybrid MCDM systems in this paper by integrating Additive Ratio ASsessment (ARAS) with Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and Complex PRoportional ASsessment (COPRAS). To demonstrate the ability and effectiveness of these two hybrid models i.e., TOPSIS-ARAS and COPRAS-ARAS were applied to solve a real-time robot selection problem with 12 alternative robots and five selection criteria, while evaluating the parametric importance using the CRiteria Importance Through Inter criteria Correlation (CRITIC) objective weighting estimation tool. The rankings of the robot alternatives gained from these two hybrid models were also compared to the obtained results from eight other solo MCDM tools. Although the rankings by the applied methods slightly differ from each other, the final outcomes from all of the adopted techniques are consistent enough to suggest that robot 12 is the best choice followed by robot 11, and robot 4 is the worst one among these 12 alternatives. Spearman Correlation Coefficient (SCC) also reveals that the proposed rankings derived from various methods have a strong ranking relationship with one another. Finally, sensitivity analysis was performed to investigate the effects of weight variation and to validate the robustness of the implemented MCDM approaches.

A lightweight, highly corrosive resistant, and high-strength wrought alloy in the alumi-num family is the Aluminium 8006 alloy. The AA8006 alloy can be formed, welded, and adhesively bonded. However, the recommended welding methods such as laser, TIG (Tungsten Inert Gas welding), and ultrasonic are more costly. This investigation aims to reduce the cost of welding with-out compromising joint quality by means of friction stir welding. The aluminum alloy-friendly re-inforcement agent zirconia is utilized as particles during the weld to improve the performance of the newly identified material AA8006 alloy in friction stir welding (FSW). The objectives of this research are to identify the level of process parameters for the friction stir welding of AA8006 to reduce the variability by the trial-and-error experimental method, thereby reducing the number of samples needing to be characterized to optimize the process parameters. To enhance the quality of the weld, the friction stir processing concept will be adapted with zirconia reinforcement during welding. The friction stir-processed samples were investigated regarding their mechanical proper-ties such as tensile strength and Vickers microhardness. The welded samples were included in the corrosion testing to ensure that no foreign corrosive elements were included during the welding. The quality of the weld was investigated in terms of its surface morphology, including aspects such as the dispersion of reinforced particles on the welded area, the incorporation of foreign elements during the weld, micro defects or damage, and other notable changes through scanning electron microscopy analysis. The process of 3D profilometry was employed to perform optical microscopy investigation on the specimens inspected to ensure their surface quality and finish. Based on the outcomes, the optimal process parameters are suggested. Future directions for further investigation are highlighted.

The choice of suitable inoculants in the grain refinement process and subsequent enhancement of the characteristics of the composites developed is an important materials research topic, having wide scope. In this regard, the present work is aimed at finding the appropriate composition and size of fly ash as inoculants for grain refinement of the aluminum AA 5083 composites. Fly ash particles, which are by products of the combustion process in thermal power plants, contributing to the large-scale pollution and landfills can be effectively utilized as inoculants and interatomic lubricants in the composite matrix–reinforcement subspaces synthesized in the inert atmosphere using ultrasonic assisted stir casting setup. Thus, the work involves the study of the influence of percentage and size of the fly ash dispersions on the tensile and impact strength characteristics of the aluminum AA 5083/7.5SiC composites. The C type of fly ash with the particle size in the series of 40–75 µm, 76–100 µm, and 101–125 µm and weight % in the series of 0.5, 1, 1.5, 2, and 2.5 are selected for the work. The influence of fly ash as distinct material inoculants for the grain refinement has worked out well with the increase in the ultimate tensile strength, yield strength, and impact strength of the composites, with the fly ash as material inoculants up to 2 wt. % beyond which the tensile and impact characteristics decrease due to the micro coring and segregation. This is evident from the microstructural observations for the composite specimens. Moreover, the role of fly ash as material inoculants is distinctly identified with the X-Ray Diffraction (XRD) for the phase and grain growth epitaxy and the Energy Dispersive Spectroscopy (EDS) for analyzing the characteristic X-Rays of the fly ash particles as inoculant agents in the energy spectrum.

The effect of reinforcements and thermal exposure on the tensile properties of aluminium AA 5083–silicon carbide (SiC)–fly ash composites were studied in the present work. The specimens were fabricated with varying wt.% of fly ash and silicon carbide and subjected to T6 thermal cycle conditions to enhance the properties through “precipitation hardening”. The analyses of the microstructure and the elemental distribution were carried out using scanning electron microscopic (SEM) images and energy dispersive spectroscopy (EDS). The composite specimens thus subjected to thermal treatment exhibit uniform distribution of the reinforcements, and the energy dispersive spectrum exhibit the presence of Al, Si, Mg, O elements, along with the traces of few other elements. The effects of reinforcements and heat treatment on the tensile properties were investigated through a set of scientifically designed experimental trials. From the investigations, it is observed that the tensile and yield strength increases up to 160 °C, beyond which there is a slight reduction in the tensile and yield strength with an increase in temperature (i.e., 200 °C). Additionally, the % elongation of the composites decreases substantially with the inclusion of the reinforcements and thermal exposure, leading to an increase in stiffness and elastic modulus of the specimens. The improvement in the strength and elastic modulus of the composites is attributed to a number of factors, i.e., the diffusion mechanism, composition of the reinforcements, heat treatment temperatures, and grain refinement. Further, the optimisation studies and ANN modelling validated the experimental outcomes and provided the training models for the test data with the correlation coefficients for interpolating the results for different sets of parameters, thereby facilitating the fabrication of hybrid composite components for various automotive and aerospace applications.

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Nano aluminum oxide was prepared by the combustion method using aluminum nitrate as the oxidizer and urea as a fuel. Characterization of synthesized materials was performed using SEM (scanning electron microscope), powder XRD (X-ray diffraction), FTIR (Fourier transform infrared spectroscopy), and TEM (transmission electron microscope). Al-Mg/Al2O3 (2, 4, 6, and 8 wt%) metal matrix nanocomposites were prepared by liquid metallurgy route-vertex technique. The homogeneous dispersion of nano Al2O3 particles in Al-Mg/Al2O3 metal matrix nanocomposites (MMNCs) was revealed from the field emission SEM analysis. The reinforcement particles present in the matrix were analyzed through energy-dispersive X-ray spectroscopy method. The properties (corrosion and mechanical) of the fabricated composites were evaluated. The mechanical and corrosion properties of the prepared nanocomposites initially increased and then decreased with the addition of nano Al2O3 particles in Al-Mg Matrix. The studies show that, the presence of 6 wt% of nano Al2O3 particles in the matrix improved the properties of other combinations of nano Al2O3 in the Al-Mg matrix material. Further, the Al-Mg/Al2O3 (6 wt%) MMNCs are joined by friction stir welding and evaluated for microstructural, mechanical, and corrosion properties. Al-Mg/Al2O3 MMNCs may find applications in the marine field. The response surface method (RSM) was used for the optimization of tensile strength, Young’s modulus, and microhardness of the synthesized material which resulted in a 95% of statistical confidence level. Artificial neural network (ANN) analysis was also carried out which perfectly predicted these two properties. The ANN model is optimized to obtain 99.9% accurate predictions by changing the number of neurons in the hidden layer.

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