A novel integrated approach of augmented grey wolf optimizer and ANN for estimating axial load carrying-capacity of concrete-filled steel tube columns

Recently, electric vehicle (EV) technology has received massive attention worldwide due to its improved performance efficiency and significant contributions to addressing carbon emission problems. In line with that, EVs could play a vital role in achieving sustainable development goals (SDGs). However, EVs face some challenges such as battery health degradation, battery management complexities, power electronics integration, and appropriate charging strategies. Therefore, further investigation is essential to select appropriate battery storage and management system, technologies, algorithms, controllers, and optimization schemes. Although numerous studies have been carried out on EV technology, the state-of-the-art technology, progress, limitations, and their impacts on achieving SDGs have not yet been examined. Hence, this review paper comprehensively and critically describes the various technological advancements of EVs, focusing on key aspects such as storage technology, battery management system, power electronics technology, charging strategies, methods, algorithms, and optimizations. Moreover, numerous open issues, challenges, and concerns are discussed to identify the existing research gaps. Furthermore, this paper develops the relationship between EVs benefits and SDGs concerning social, economic, and environmental impacts. The analysis reveals that EVs have a substantial influence on various goals of sustainable development, such as affordable and clean energy, sustainable cities and communities, industry, economic growth, and climate actions. Lastly, this review delivers fruitful and effective suggestions for future enhancement of EV technology that would be beneficial to the EV engineers and industrialists to develop efficient battery storage, charging approaches, converters, controllers, and optimizations toward targeting SDGs.

https://www.mdpi.com/2313-0105/8/9/119/pdf?version=1662626927

Battery Management, Key Technologies, Methods, Issues, and Future Trends of Electric Vehicles: A Pathway toward Achieving Sustainable Development Goals

Vehicles around the world are being converted to electric power in order to combat climate change and lower pollution levels. Sustaining this process calls for more electric vehicle charging stations (EVCS) to be made available to the general population. The uncoordinated surge of electric vehicles (EV) and the EVCS will have repercussions on the distribution network, environment, EV users, and charging stations, posing significant technical, economic, and environmental issues. Coordinating and scheduling the charging/discharging of EVs at EVCSs is essential for preventing the issues. EVs may be employed as sources of distributed energy storage and leveraged to improve network performance and efficiency with suitable charge/discharge control management. This paper examines various approaches for the optimal placement of EVCS, including the distribution network operator (DNO) approach, the charging station operator (CSO) approach, the EV user approach, and the environmental approach with different objective functions, their combinations, and constraints. The charging procedures, charging control and management, and coordinated EV flows in the EVCS and distribution network are evaluated. In addition, alternative optimization strategies for EVCS placement with objective functions are discussed. In this review study, the effects of EV load on the distribution system, the environment, and the economy are also analyzed. 

Review of electric vehicles integration impacts in distribution networks: Placement, charging/discharging strategies, objectives and optimisation models

Recent advancements in data science and artificial intelligence, as well as the development of clean and sustainable energy sources, have created numerous opportunities for energy researchers to conduct their studies. Artificial Intelligence (AI) and Machine Learning (ML) techniques have been applied to Renewable Energy Systems (RES) for several years, and their intensity and scope have grown in recent years. Since artificial intelligence and machine learning have a wide range of applications, it may be difficult to select and implement suitable methods for future research. In order to address this issue, this study examines several of the most popular and well-known AI techniques in renewable energy. This paper describes over ten of the most prevalent RES modeling and optimization algorithms, including Artificial Neural Networks (ANN), Long and Short-Term Memory (LSTM), Recurrent and Convolutional Neural Networks (RNNs and CNNs), the Genetic Algorithm (GA), and the Particle Swarm Optimization algorithm (PSO). More than a hundred different studies between 2020 and 2022 have been compiled and organized in the results section according to the method used and the field of application. At the end, the results are discussed, and the limitations of the current study will be mentioned, followed by some suggestions to complete our work and make it more useful. 

A review of the applications of artificial intelligence in renewable energy systems: An approach-based study

Abstract With the rapid expansion of the world population and economy, the need for energy has also increased. To minimize energy waste, it is crucial that energy be supplied to residences and businesses in an efficient manner. Smart Grids (SG) have the potential to decrease these power losses by improving the energy grid's efficiency. The effective integration of methods like artificial intelligence and blockchain into SGs has made it easier to predict customer preferences. Immediate analysis and comparison of the different AI and blockchain approaches is necessary in order to determine which approach is best for SGs. This study aims to know about some basic of smart grid. Furthermore, this study also looks at different types of layers and attacks on these layers and provide solutions of these attacks using AI and blockchain methods. At the end the study, also focus on open issues in this field and gives some future directions. By classifying attacks, proposing innovative defenses, and exploring AI and blockchain solutions, the study contributes to safeguarding smart grid infrastructures. Future research directions include addressing emerging attack strategies and refining technology for enhanced security. 

Enhancing Smart Grid Cybersecurity: A Comprehensive Analysis of Attacks, Defenses, and Innovative AI-Blockchain Solutions

EV-Based reconfigurable smart grid management using support vector regression learning technique machine learning

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EV-Based reconfigurable smart grid management using support vector regression learning technique machine learning