Energy storage system (ESS) technology is still the logjam for the electric vehicle (EV) industry. Lithium-ion (Li-ion) batteries have attracted considerable attention in the EV industry owing to their high energy density, lifespan, nominal voltage, power density, and cost. In EVs, a smart battery management system (BMS) is one of the essential components; it not only measures the states of battery accurately, but also ensures safe operation and prolongs the battery life. The accurate estimation of the state of charge (SOC) of a Li-ion battery is a very challenging task because the Li-ion battery is a highly time variant, non-linear, and complex electrochemical system. This paper explains the workings of a Li-ion battery, provides the main features of a smart BMS, and comprehensively reviews its SOC estimation methods. These SOC estimation methods have been classified into four main categories depending on their nature. A critical explanation, including their merits, limitations, and their estimation errors from other studies, is provided. Some recommendations depending on the development of technology are suggested to improve the online estimation.

Medium-term electricity consumption and load forecasting in smart grids is an attractive topic of study, especially using innovative data analysis approaches for future energy consumption trends. Loss of electricity during generation and use is also a problem to be addressed. Both consumers and utilities can benefit from a predictive study of electricity demand and pricing. In this study, we used a new machine learning approach called AdaBoost to identify key features from an ISO-NE dataset that includes daily consumption data over eight years. Moreover, the DT classifier and RF are widely used to extract the best features from the dataset. Moreover, we predicted the electricity load and price using machine learning techniques including support vector machine (SVM) and deep learning techniques such as a convolutional neural network (CNN). Coronavirus herd immunity optimization (CHIO), a novel optimization approach, was used to modify the hyperparameters to increase efficiency, and it used classifiers to improve the performance of our classifier. By adding additional layers to the CNN and fine-tuning its parameters, the probability of overfitting the classifier was reduced. For method validation, we compared our proposed models with several benchmarks. MAE, MAPE, MSE, RMSE, the f1 score, recall, precision, and accuracy were the measures used for performance evaluation. Moreover, seven different forms of statistical analysis were given to show why our proposed approaches are preferable. The proposed CNN-CHIO and SVM techniques had the lowest MAPE error rates of 6% and 8%, respectively, and the highest accuracy rates of 95% and 92%, respectively.

Summary This paper proposes a new framework for early hotspot detection in the photovoltaic (PV) panels using color image descriptors and a machine learning algorithm. In the proposed approach, the acquired thermographic images of PV panels are divided into non‐overlapping regions, and then color image descriptors are computed for the regions. The color descriptors are then used as features to train different machine learning algorithms to classify the PV panels into three classes (ie, normal, hotspot, and defective). After extensive testing and comprehensive analysis, the experimental results show that Red‐Green Scale‐Invariant Feature Transform (rgSIFT) descriptor with k‐Nearest Neighbor (k‐NN) outperforms all other images descriptors and machine learning combinations with an accuracy rate of 98.7%. The experimental results also show the effects of the size of non‐overlapping regions on the classification accuracy. It is observed that the classification accuracy decreases as size is increased or decreased around the optimal non‐overlapping region image size of 71 × 71 pixels. The proposed method has a significant role in carbon‐free cities and can easily be implemented to inspect the PV system.

The growing human population and the increasing energy needs have produced a serious energy crisis, which has stimulated researchers to look for alternative energy sources. The diffusion of small-scale renewable distributed generations (DG) with micro-grids can be a promising solution to meet the environmental obligations. The uncertainty and sporadic nature of renewable energy sources (RES) is the main obstacle to their use as autonomous energy sources. In order to overcome this, a storage system is required. This paper proposes an optimized strategy for a hybrid photovoltaic (PV) and battery storage system (BSS) connected to a low-voltage grid. In this study, a cost function is formulated to minimize the net cost of electricity purchased from the grid. The charging and discharging of the battery are operated optimally to minimize the defined cost function. Half-hourly electricity consumer load data and solar irradiance data collected from the United Kingdom (UK) for a whole year are utilized in the proposed methodology. Five cases are discussed for a comparative cost analysis of the electricity imported and exported. The proposed scheme provides a techno-economic analysis of the combination of a BSS with a low-voltage grid, benefitting from the feed-in tariff (FIT) scheme.

As coronavirus disease 2019 vaccines are being increasingly administered worldwide, subsequent side effects, such as myocarditis, pericarditis, and myopericarditis, are becoming increasingly more common. Our case describes a 64-year-old male who developed chest pain and shortness of breath one week after receiving the Moderna (Cambridge, Massachusetts) COVID-19 mRNA vaccine. He was found to have a large, left-sided pleural effusion and a small pericardial effusion. The patient underwent thoracentesis and video-assisted thoracoscopic procedure with chest tube placement, which drained bloody pleural and pericardial fluid. He was treated with a course of colchicine. Subsequent imaging revealed the resolution of pericardial and pleural effusions, along with the resolution of symptoms.