Electric vehicles (EVs) are a compelling alternative for mitigating CO2-equivalent emissions. In the context of EVs, the architecture and operational efficiency of a hybrid energy storage system (HESS) are pivotal. The present study focuses on a HESS model based on a parallel full-active configuration that integrates a lithium-ion (Li-ion) battery with an ultracapacitor facilitated by two direct current-to-direct current converters. The energy management strategy governing the HESS emerges as a critical element in the overall performance of EVs. Conventionally, fuzzy control strategies have been extensively utilised at the supervisory and high-control levels; these are largely dependent on expert experience. This paper introduces an innovative fuzzy control system that employs teaching–learning-based optimisation (TLBO) in the energy management strategy domain of a HESS in EVs. This intelligent control mechanism aims to optimise various performance metrics – such as energy consumption, lithium battery output current, and peak power. The optimisation focuses on fine-tuning the parameters constituting the fuzzy rule base and membership functions. Simulation results substantiate the adaptability of the TLBO-based fuzzy energy management system in efficiently allocating power across various driving conditions. Comparative analyses are conducted with respect to the power-sharing capabilities of the proposed TLBO-fuzzy (TLBO-F), particle swarm optimisation-fuzzy (PSO-F), and non-optimisation-fuzzy (NO-F) strategies under two distinct driving conditions: the urban dynamometer driving schedule and the European extra-urban driving cycle. The main objective of this research is to extend battery lifetime by minimising both the lithium battery output power and overall energy consumption. In this way, the study aims not only to prolong the lifespan of Li-ion batteries but also to mitigate the associated energy costs for battery charging. Ultimately, the proposed method enhances the power sharing of the ultracapacitor during a span of 1400 seconds. It also confirms the battery ratio power, ${R.P}_{b}^{\Box } $ , is decreased for the proposed method (TLBO-F) to 6.4% compared to the PSO-F strategy and is decreased 35% relative to the NO-F algorithm. The battery’s state of charge is increased to 14% by implementing the TLBO-F method relative to the PSO-F and increased to 30% compared to the NO-F algorithm. This diminishes the charging and discharging rate of the battery during the driving cycle while extending the lifespan of the Li-ion battery and adheres to the ultracapacitor’s state-of-charge constraints.

This paper presents a micromachining process for lithium niobate (LiNbO₃) material for the rapid prototyping of a resonant sensor design for medical devices applications. Laser micromachining was used to fabricate samples of lithium niobate material. A qualitative visual check of the surface was performed using scanning electron microscopy. The surface roughness was quantitatively investigated using an optical surface profiler. A surface roughness of 0.526 μm was achieved by laser micromachining. The performance of the laser-micromachined sensor has been examined in different working environments and different modes of operation. The sensor exhibits a Quality-factor (Q-factor) of 646 in a vacuum; and a Q-factor of 222 in air. The good match between the modelling and experimental results shows that the laser-micromachined sensor has a high potential to be used as a resonance biosensor.

High-entropy alloys (HEAs) offer a new approach to the design of superior metallic materials, wherein alloys are based on multiple principal elements rather than just one. Deep Neural Networks (DNNs), machine learning tools that are efficiently used for prediction purposes, are transforming fields, from speech recognition to computational medicine. In this study, we extend DNN applications to the field phase prediction of high-entropy alloys. Using the built-in capabilities in TensorFlow and Keras, we train DNNs with different layers and numbers of neurons, achieving a 90% prediction accuracy. The DDN prediction model is examined in detail with different datasets to verify model robustness. Due to the high cost of HEAs and in order to save time, it is important to predict phases in order to design alloy composition. Through this study, we show trained DNNs to be a viable tool for predicting the phases of high-entropy alloys, where 90% phase prediction accuracy was achieved in this work.

In this work, nanocomposite material consisting of titanium dioxide (TiO2) nanoparticles and rhodamine 6G (R6G) laser dye was doped with different ratios of polystyrene (PS) and polymethylmethacrylate (PMMA) polymer blends (i.e. 40% PS/60% PMMA and 50% PS/50% PMMA) using the casting method. The optical properties of the prepared samples were measured using UV-visible spectroscopy. These measurements include the calculations of the absorption (A%), absorption coefficient (α), energy gap (Eg), refractive index (n), extinction coefficient (K) and real and imaginary parts. These nanocomposite materials were investigated using a scanning electron microscope and photoluminescence (PL) spectra emission. The SEM results show that the optimum results were at 50% PS/50% PMMA/TiO2/R6G with all the dye being decorated in the process blend, while the TiO2 nanoparticle demonstrated that the pores were incomplete in some places and complete in others. The optical results show that there are red shifts in all absorption peaks caused by the increased PS ratio. The Eg decreased from 3.2 eV to 2.8 eV as the concentration of PS in the polymer matrix increased. The PL spectrum for the nanocomposites, with a different ratio of PS, shows that sharp peaks of PL emission occur at a wavelength of 570 nm and shift towards longer wavelengths with an increased PS%.