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State of charge (SOC) is a crucial index used in the assessment of electric vehicle (EV) battery storage systems. Thus, SOC estimation of lithium-ion batteries has been widely investigated because of their fast charging, long-life cycle, and high energy density characteristics. However, precise SOC assessment of lithium-ion batteries remains challenging because of their varying characteristics under different working environments. Machine learning techniques have been widely used to design an advanced SOC estimation method without the information of battery chemical reactions, battery models, internal properties, and additional filters. Here, the capacity of optimized machine learning techniques are presented toward enhanced SOC estimation in terms of learning capability, accuracy, generalization performance, and convergence speed. We validate the proposed method through lithium-ion battery experiments, EV drive cycles, temperature, noise, and aging effects. We show that the proposed method outperforms several state-of-the-art approaches in terms of accuracy, adaptability, and robustness under diverse operating conditions.

Accurate state of charge (SOC) estimation of lithium-ion (Li-ion) batteries is crucial in prolonging cell lifespan and ensuring its safe operation for electric vehicle applications. In this article, we propose the deep learning-based transformer model trained with self-supervised learning (SSL) for end-to-end SOC estimation without the requirements of feature engineering or adaptive filtering. We demonstrate that with the SSL framework, the proposed deep learning transformer model achieves the lowest root-mean-square-error (RMSE) of 0.90% and a mean-absolute-error (MAE) of 0.44% at constant ambient temperature, and RMSE of 1.19% and a MAE of 0.7% at varying ambient temperature. With SSL, the proposed model can be trained with as few as 5 epochs using only 20% of the total training data and still achieves less than 1.9% RMSE on the test data. Finally, we also demonstrate that the learning weights during the SSL training can be transferred to a new Li-ion cell with different chemistry and still achieve on-par performance compared to the models trained from scratch on the new cell.

The agricultural industry has made a tremendous contribution to the foundations of civilization. Basic essentials such as food, beverages, clothes and domestic materials are enriched by the agricultural industry. However, the traditional method in agriculture cultivation is labor-intensive and inadequate to meet the accelerating nature of human demands. This scenario raises the need to explore state-of-the-art crop cultivation and harvesting technologies. In this regard, optics and photonics technologies have proven to be effective solutions. This paper aims to present a comprehensive review of three photonic techniques, namely imaging, spectroscopy and spectral imaging, in a comparative manner for agriculture applications. Essentially, the spectral imaging technique is a robust solution which combines the benefits of both imaging and spectroscopy but faces the risk of underutilization. This review also comprehends the practicality of all three techniques by presenting existing examples in agricultural applications. Furthermore, the potential of these techniques is reviewed and critiqued by looking into agricultural activities involving palm oil, rubber, and agro-food crops. All the possible issues and challenges in implementing the photonic techniques in agriculture are given prominence with a few selective recommendations. The highlighted insights in this review will hopefully lead to an increased effort in the development of photonics applications for the future agricultural industry.

Three-phase induction motors (TIMs) are widely used for machines in industrial operations. As an accurate and robust controller, fuzzy logic controller (FLC) is crucial in designing TIMs control systems. The performance of FLC highly depends on the membership function (MF) variables, which are evaluated by heuristic approaches, leading to a high processing time. To address these issues, optimisation algorithms for TIMs have received increasing interest among researchers and industrialists. Here, we present an advanced and efficient quantum-inspired lightning search algorithm (QLSA) to avoid exhaustive conventional heuristic procedures when obtaining MFs. The accuracy of the QLSA based FLC (QLSAF) speed control is superior to other controllers in terms of transient response, damping capability and minimisation of statistical errors under diverse speeds and loads. The performance of the proposed QLSAF speed controller is validated through experiments. Test results under different conditions show consistent speed responses and stator currents with the simulation results. Though optimization algorithms for fuzzy logic controller (FIC)-based three-phase induction motor (TIM) systems are attractive for improving efficiency, existing methods have limited search capability. Here, the authors report a quantum-inspired lightning search algorithm with enhanced performance.

The optimization of thermophotovoltaic (TPV) cell efficiency is essential since it leads to a significant increase in the output power. Typically, the optimization of In 0.53 Ga 0.47 As TPV cell has been limited to single variable such as the emitter thickness, while the effects of the variation in other design variables are assumed to be negligible. The reported efficiencies of In 0.53 Ga 0.47 As TPV cell mostly remain < 15%. Therefore, this work develops a multi-variable or multi-dimensional optimization of In 0.53 Ga 0.47 As TPV cell using the real coded genetic algorithm (RCGA) at various radiation temperatures. RCGA was developed using Visual Basic and it was hybridized with Silvaco TCAD for the electrical characteristics simulation. Under radiation temperatures from 800 to 2000 K, the optimized In 0.53 Ga 0.47 As TPV cell efficiency increases by an average percentage of 11.86% (from 8.5 to 20.35%) as compared to the non-optimized structure. It was found that the incorporation of a thicker base layer with the back-barrier layers enhances the separation of charge carriers and increases the collection of photo-generated carriers near the band-edge, producing an optimum output power of 0.55 W/cm 2 (cell efficiency of 22.06%, without antireflection coating) at 1400 K radiation spectrum. The results of this work demonstrate the great potential to generate electricity sustainably from industrial waste heat and the multi-dimensional optimization methodology can be adopted to optimize semiconductor devices, such as solar cell, TPV cell and photodetectors.

The essential oil of Backhousia citriodora, commonly known as lemon myrtle oil, possesses various beneficial properties due to its richness in bioactive compounds. This study aimed to characterize the chemical profile of the essential oil isolated from leaves of Backhousia citriodora (BCEO) and its biological properties, including antioxidant, antibacterial, and antibiofilm activities. Using gas chromatography–mass spectrometry, 21 compounds were identified in BCEO, representing 98.50% of the total oil content. The isomers of citral, geranial (52.13%), and neral (37.65%) were detected as the main constituents. The evaluation of DPPH radical scavenging activity and ferric reducing antioxidant power showed that BCEO exhibited strong antioxidant activity at IC50 of 42.57 μg/mL and EC50 of 20.03 μg/mL, respectively. The antibacterial activity results showed that BCEO exhibited stronger antibacterial activity against Gram-positive bacteria (Staphylococcus aureus and Staphylococcus epidermidis) than against Gram-negative bacteria (Escherichia coli and Klebsiella pneumoniae). For the agar disk diffusion method, S. epidermidis was the most sensitive to BCEO with an inhibition zone diameter of 50.17 mm, followed by S. aureus (31.13 mm), E. coli (20.33 mm), and K. pneumoniae (12.67 mm). The results from the microdilution method showed that BCEO exhibited the highest activity against S. epidermidis and S. aureus, with the minimal inhibitory concentration (MIC) value of 6.25 μL/mL. BCEO acts as a potent antibiofilm agent with dual actions, inhibiting (85.10% to 96.44%) and eradicating (70.92% to 90.73%) of the biofilms formed by the four tested bacteria strains, compared with streptomycin (biofilm inhibition, 67.65% to 94.29% and biofilm eradication, 49.97% to 89.73%). This study highlights that BCEO can potentially be a natural antioxidant agent, antibacterial agent, and antibiofilm agent that could be applied in the pharmaceutical and food industries. To the best of the authors’ knowledge, this is the first report, on the antibiofilm activity of BCEO against four common nosocomial pathogens.

A photodetector converts optical signals to detectable electrical signals. Lately, self-powered photodetectors have been widely studied because of their advantages in device miniaturization and low power consumption, which make them preferable in various applications, especially those related to green technology and flexible electronics. Since self-powered photodetectors do not have an external power supply at zero bias, it is important to ensure that the built-in potential in the device produces a sufficiently thick depletion region that efficiently sweeps the carriers across the junction, resulting in detectable electrical signals even at very low-optical power signals. Therefore, two-dimensional (2D) materials are explored as an alternative to silicon-based active regions in the photodetector. In addition, plasmonic effects coupled with self-powered photodetectors will further enhance light absorption and scattering, which contribute to the improvement of the device’s photocurrent generation. Hence, this review focuses on the employment of 2D materials such as graphene and molybdenum disulfide (MoS2) with the insertion of hexagonal boron nitride (h-BN) and plasmonic nanoparticles. All these approaches have shown performance improvement of photodetectors for self-powering applications. A comprehensive analysis encompassing 2D material characterization, theoretical and numerical modelling, device physics, fabrication and characterization of photodetectors with graphene/MoS2 and graphene/h-BN/MoS2 heterostructures with plasmonic effect is presented with potential leads to new research opportunities.

This study uses an artificial neural network (ANN) as an intelligent controller for the management and scheduling of a number of microgrids (MGs) in virtual power plants (VPP). Two ANN-based scheduling control approaches are presented: the ANN-based backtracking search algorithm (ANN-BBSA) and ANN-based binary practical swarm optimization (ANN-BPSO) algorithm. Both algorithms provide the optimal schedule for every distribution generation (DG) to limit fuel consumption, reduce CO2 emission, and increase the system efficiency towards smart and economic VPP operation as well as grid decarbonization. Different test scenarios are executed to evaluate the controllers’ robustness and performance under changing system conditions. The test cases are different load curves to evaluate the ANN’s performance on untrained data. The untrained and trained load models used are real-load parameter data recorders in northern parts of Malaysia. The test results are analyzed to investigate the performance of these controllers under varying power system conditions. Additionally, a comparative study is performed to compare their performances with other solutions available in the literature based on several parameters. Results show the superiority of the ANN-based controllers in terms of cost reduction and efficiency.

Water can be an irritant to a power transformer, as it is recognized as a major hazard to the operation of transformers. The water content of a transformer insulation system comprises the water in the transformer insulation oil and in the cellulose paper. The increase in the water content in the insulation system leads to reduced breakdown voltage, accelerated aging of the oil–paper insulation system, and the possibility of producing bubbles at high temperatures. Therefore, various techniques have been applied to measure the water content in both oil and paper insulation. This article comprehensively reviews and analyzes the methods (technically or nontechnically) that have been used to monitor the water content in transformer insulation systems. Apart from discussing the advantages and major drawbacks of these methods, the accuracy, measurement time, and cost of each technique are also elucidated in this review. This review can be extremely useful to the utility in monitoring and maintaining the good condition of transformers. Based on the reviewed methods and their challenges, a few future research directions and prospects for determining the water content in transformer insulation systems are outlined, such as utilizing artificial intelligence and enhancing current techniques.

This article demonstrates maximum power point tracking (MPPT) using a DC-DC boost converter. It introduces an intelligent control technique with fuzzy-based pattern search (PS) optimization for the MPPT controller, enhancing energy conversion efficiency. The fuzzy-PS approach is further refined with PA optimization. A comprehensive performance evaluation compares it with various optimization algorithms. The controller is tested under changes in irradiance and temperature, showing its performance against the Perturb and Observe (P&O) algorithm. The fuzzy controller is optimized to provide the best membership functions (MFs) using PS optimization, particle swarm optimization (PSO), and genetic algorithm (GA), with root mean square error (RMSE) as the objective function. PS optimization outperforms other algorithms. The fuzzy-PS optimization achieves the lowest RMSE of 0.6861 after 100 iterations, while fuzzy-GA and fuzzy-PSO reach RMSEs of 1.257 and 0.9454, respectively. The proposed fuzzy-PS MPPT controller effectively adapts to irradiance and temperature variations, achieving maximum power outputs up to 74.48 kW and Comparative evaluations revealed an average MPPT efficiency of 99.7%, demonstrating superior tracking performance compared to the P&O algorithm.