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Traditional Multi-Criteria Decision Making (MCDM) methods have now become outdated; therefore, most researchers are focusing on more robust hybrid MCDM models that combine two or more MCDM techniques to address decision-making problems.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.

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.

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.