Battery ensures power solutions for many necessary portable devices such as electric vehicles, mobiles, and laptops. Owing to the rapid growth of Li-ion battery users, unwanted incidents involving Li-ion batteries have also increased to some extent. In particular, the sudden breakdown of industrial and lightweight machinery due to battery failure causes a substantial economic loss for the industry. Consequently, battery state estimation, management system, and estimation of the remaining useful life (RUL) have become a topic of interest for researchers. Considering this, appropriate battery data acquisition and proper information on available battery data sets may require. This review paper is mainly focused on three parts. The first one is battery data acquisitions with commercially and freely available Li-ion battery data set information. The second is the estimation of the states of battery with the battery management system. And third is battery RUL estimation. Various RUL prognostic methods applied for Li-ion batteries are classified, discussed, and reviewed based on their essential performance parameters. Information on commercially and publicly available data sets of many battery models under various conditions is also reviewed. Various battery states are reviewed considering advanced battery management systems. To that end, a comparative study of Li-ion battery RUL prediction is provided together with the investigation of various RUL prediction algorithms and mathematical modelling.

This review provides a comprehensive overview of the state-of-the-art methods of graph-based networks from a deep learning perspective. Graph networks provide a generalized form to exploit non-euclidean space data. A graph can be visualized as an aggregation of nodes and edges without having any order. Data-driven architecture tends to follow a fixed neural network trying to find the pattern in feature space. These strategies have successfully been applied to many applications for euclidean space data. Since graph data in a non-euclidean space does not follow any kind of order, these solutions can be applied to exploit the node relationships. Graph Neural Networks (GNNs) solve this problem by exploiting the relationships among graph data. Recent developments in computational hardware and optimization allow graph networks possible to learn the complex graph relationships. Graph networks are therefore being actively used to solve many problems including protein interface, classification, and learning representations of fingerprints. To encapsulate the importance of graph models, in this paper, we formulate a systematic categorization of GNN models according to their applications from theory to real-life problems and provide a direction of the future scope for the applications of graph models as well as highlight the limitations of existing graph networks.

Robots that can move autonomously and can make intelligent decisions by perceiving their environments and surrounding objects are known as autonomous mobile robots. Such robots have rapidly moved from laboratories to automated industries to fill a variety of roles in our lives, homes, offices, hospitals, industries, and even on the streets. The interest in mobile robots is growing rapidly, prompting an enormous amount of research over the last 30 years, on critical factors of mobile robots such as locomotion, perception, localization, mapping, ego-motion tracking, and dynamic navigation. This article surveys these essential factors of autonomous mobile robots in terms of mathematical modeling, control issues, and challenging factors. Brief discussions are provided on the fundamentals of these technologies, popular algorithms in comprehensive mode, future challenges, and promising directions to guide the construction of an autonomous mobile robot with high accuracy and effectiveness. Since it is difficult to find complete coverage of those topics in a single location, this paper provides a guideline for researchers entering the field or for innovators in the mobile robotics sector. The paper also examines open challenges in indoor mobile robots and identifies potential futures for autonomous mobile robots.

Accurate fault classification and detection for the microgrid (MG) becomes a concern among the researchers from the state-of-art of fault diagnosis as it increases the chance to increase the transient response. The MG frequently experiences a number of shunt faults during the distribution of power from the generation end to user premises, which affects the system reliability, damages the load, and increases the fault line restoration cost. Therefore, a noise-immune and precise fault diagnosis model is required to perform the fast recovery of the unhealthy phases. This paper presents a review on the MG fault diagnosis techniques with their limitations and proposes a novel discrete-wavelet transform (DWT) based probabilistic generative model to explore the precise solution for fault diagnosis of MG. The proposed model is made of multiple layers with a restricted Boltzmann machine (RBM), which enables the model to make the probability reconstruction over its inputs. The individual RBM layer is trained with an unsupervised learning approach where an artificial neural network (ANN) algorithm tunes the model for minimizing the error between the true and predicted class. The effectiveness of the proposed model is studied by varying the input signal and sampling frequencies. A level of considered noise is added with the sample data to test the robustness of the studied model. Results prove that the proposed fault detection and classification model has the ability to perform the precise diagnosis of MG faults. A comparative study among the proposed, kernel extreme learning machine (KELM), multi KELM, and support vector machine (SVM) approaches is studied to confirm the robust superior performance of the proposed model.

The idea of microgrid, smart grid, and virtual power plant (VPP) is being developed to resolve the challenges of climate change in the 21st century, to ensure the use of renewable energy in the electrical grid. For the increasing demand for electricity, raising public consciousness about reducing carbon emission, the microgrid is established which is transformed into a virtual power plant (VPP) or a smart grid with the blessing of modern communication systems, intelligence technology, and smart devices. So, to keep these systems up-to-date and to ensure security, it is important to know the details about the uses and benefits of these systems in the developed world and also to improve control methods and automation, it is important to achieve the present essence of such systems. This paper is focused to contribute to this flourishing area of energy sustainability covering microgrid, smart grid, and virtual power plant by compiling and recapping their recent advancements, technical requirements, control problems, and solutions. The paper is mainly intended to address the role of control strategies applied to the microgrid, smart grid, and virtual power plant towards future energy generation, distribution, management, and security.