• Energy Research

The lithium-ion battery has high energy and power density, long life cycle, low toxicity, low discharge rate, more reliability, and better efficiency compared to other batteries. On the other hand, the issue of a reduction in charging time of the lithium-ion battery is still a bottleneck for the commercialization of electric vehicles (EVs). Therefore, an approach to charge lithium-ion batteries at a faster rate is needed. This paper proposes an efficient, real-time, fast-charging methodology of lithium-ion batteries. Fuzzy logic was adopted to drive the charging current trajectory. A temperature control unit was also implemented to evade the effects of fast charging on the aging mechanism. The proposed method of charging also protects the battery from overvoltage and overheating. Extensive testing and comprehensive analysis were conducted to examine the proposed charging technique. The results show that the proposed charging strategy favors a full battery recharging in 9.76% less time than the conventional constant-current–constant-voltage (CC/CV) method. The strategy charges the battery at a 99.26% state of charge (SOC) without significant degradation. The entire scheme was implemented in real time, using Arduino interfaced with MATLABTM Simulink. This decrease in charging time assists in the fast charging of cell phones and notebooks and in the large-scale deployment of EVs.

The microgrids operate in tie-up (TU) mode with the main grid normally, and operate in isolation (IN) mode without the main grid during faults. In a dynamic operational regime, protecting the microgrids is highly challenging. This article proposes a new microgrid protection scheme based on a state observer (SO) aided by a recurrent neural network (RNN). Initially, the particle filter (PF) serves as a SO to estimate the measured current/voltage signals from the corresponding bus. Then, a natural log of the difference between the estimated and measured current signal is taken to estimate the per-phase particle filter deviation (PFD). If the PFD of any single phase exceeds the preset threshold limit, the proposed scheme successfully detects and classifies the faults. Finally, the RNN is implemented on the SO-estimated voltage and current signals to retrieve the non-fundamental harmonic features, which are then utilized to compute RNN-based state observation energy (SOE). The directional attributes of the RNN-based SOE are employed for the localization of faults in a microgrid. The scheme is tested using Matlab® Simulink 2022b on an International Electrotechnical Commission (IEC) microgrid test bed. The results indicate the efficacy of the proposed method in the TU and IN operation regimes on radial, loop, and meshed networks. Furthermore, the scheme can detect both high-impedance (HI) and low-impedance (LI) faults with 99.6% of accuracy.

Environment protection and energy saving are the most attractive trends in zero-carbon buildings. The most promising and environmentally friendly technique is building integrated photovoltaics (BIPV), which can also replace conventional buildings based on non-renewable energy. Despite the recent advances in technology, the cost of BIPV systems is still very high. Hence, reducing the cost is a major challenge. This paper examines and validates the effectiveness of low-cost aluminum (Al) foil as a reflector. The design and the performance of planer-reflector for BIPV systems are analyzed in detail. A Bi-reflector solar PV system (BRPVS) with thin film Al-foil reflector and an LLC converter for a BIPV system is proposed and experimented with a 400-W prototype. A cadmium–sulfide (CdS) photo-resistor sensor and an Arduino-based algorithm was developed to control the working of the reflectors. Furthermore, the effect of Al-foil reflectors on the temperature of PV module has been examined. The developed LLC converter confirmed stable output voltage despite large variation in input voltage proving its effectiveness for the proposed BRPVS. The experimental results of the proposed BRPVS with an Al-reflector of the same size as that of the solar PV module offered an enhancement of 28.47% in the output power.

Abstract The application of Photovoltaic (PV) in the distributed generation system is acquiring more consideration with the developments in power electronics technology and global environmental concerns. Solar PV is playing a key role in consuming the solar energy for the generation of electric power. The use of solar PV is growing exponentially due to its clean, pollution-free, abundant, and inexhaustible nature. In grid-connected PV systems, significant attention is required in the design and operation of the inverter to achieve high efficiency for diverse power structures. The requirements for the grid-connected inverter include; low total harmonic distortion of the currents injected into the grid, maximum power point tracking, high efficiency, and controlled power injected into the grid. The performance of the inverters connected to the grid depends mainly on the control scheme applied. In this review, the global status of the PV market, classification of the PV system, configurations of the grid-connected PV inverter, classification of various inverter types, and topologies are discussed, described and presented in a schematic manner. A concise summary of the control methods for single- and three-phase inverters has also been presented. In addition, various controllers applied to grid-tied inverter are thoroughly reviewed and compared. Finally, the criteria for the selection of inverters and the future trends are comprehensively presented.

In this study, the effects of metallic zinc (Zn) particles were studied to increase surface temperature on a solar-still absorber, which is a major component of increased production. Various concentrations of Zn particles were mixed in black paint and applied to the absorber plate. SEM and XRD were used to examine and confirm the surface morphology and phase identification of as-received powder. UV-Vis spectroscopy was used to examine light-absorption properties. Finally, extensive indoor testing (using an improvised solar emulator) and outdoor testing were conducted to optimize the concentration. The specimens containing 10 wt% Zn in black paint showed the highest increase in temperature, i.e., 103.53 °C in indoor conditions at 1000 W/m2 irradiation, which is 59.17% higher than a bare aluminum plate and 17.57% higher than an only black-paint-coated aluminum plate. On the other hand, specimens containing 10 wt% Zn reached just 87.53 °C, compared to 80.00 °C for an only black-paint-coated aluminum plate and 60.62 °C for bare aluminum.

Online accurate estimation of remaining useful life (RUL) of lithium-ion batteries is a necessary feature of any smart battery management system (BMS). In this paper, a novel partial discharge data (PDD)-based support vector machine (SVM) model is proposed for RUL prediction. The proposed algorithm extracts the critical features from the voltage and temperature of PDD to train the SVM models. The classification and regression attributes of SVM are utilized to classify and predict accurate RUL. The different ranges of PDD were analyzed to find the optimal range for training the SVM model. The SVM model trained with optimal PDD features classifies the RUL into six different classes for gross estimation, and the support vector regression is used to estimate the accurate value of the last class. The classification and predictive performance of SVM model trained using the full discharge data and PDD are compared for publicly available data. Results show that the SVM classification and regression model trained with PDD features can accurately predict the RUL with low storage pressure on BMS. The PDD-based SVM model can be utilized for online RUL estimation in electric vehicles.

Solar photovoltaics (PV) are susceptible to environmental and operational stresses due to their operation in an open atmosphere. Early detection and treatment of stress prevents hotspots and the total failure of solar panels. In response, the literature has proposed several approaches, each with its own limitations, such as high processing system requirements, large amounts of memory, long execution times, fewer types of faults diagnosed, failure to extract relevant features, and so on. Therefore, this research proposes a fast framework with the least memory and computing system requirements for the six different faults of a solar panel. Infrared thermographs from solar panels are fed into intense and architecturally complex deep convolutional networks capable of differentiating one million images into 1000 classes. Features without backpropagation are calculated to reduce execution time. Afterward, deep features are fed to shallow classifiers due to their fast training time. The proposed approach trains the shallow classifier in approximately 13 s with 95.5% testing accuracy. The approach is validated by manually extracting thermograph features and through the transfer of learned deep neural network approaches in terms of accuracy and speed. The proposed method is also compared with other existing methods.