This paper develops a state of charge (SOC) estimation model for a lithium-ion battery using an improved extreme learning machine (ELM) algorithm. ELM is suitable for SOC estimation since ELM algorithm has fast estimation speed, good generalization performance, and high accuracy. However, the performance of ELM is highly dependent on training accuracy and number of neurons in a hidden layer. Hence, gravitational search algorithm (GSA) is applied to improve the ELM computational intelligence by searching for the optimal value hidden layer neurons. The optimal ELM based GSA model does not require internal battery knowledge and mathematical model for SOC estimation. The model robustness is validated at different temperatures using different electric vehicle (EV) drive cycles. The performance of ELM-GSA model is verified with two popular neural network methods; back-propagation neural network (BPNN) and radial basis function neural network (RBFNN). The results are evaluated using different error rates and computation cost. The results demonstrate that the ELM based GSA model offers high accuracy and low SOC error rate than BPNN based GSA and RBFNN based GSA models. Furthermore, a detailed comparative study between the proposed model and existing SOC strategies is conducted which also demonstrates the superiority of the proposed model.

Solar photovoltaic (PV) is one of the prominent sustainable energy sources which shares a greater percentage of the energy generated from renewable resources. As the need for solar energy has risen tremendously in the last few decades, monitoring technologies have received considerable attention in relation to performance enhancement. Recently, the solar PV monitoring system has been integrated with a wireless platform that comprises data acquisition from various sensors and nodes through wireless data transmission. However, several issues could affect the performance of solar PV monitoring, such as large data management, signal interference, long-range data transmission, and security. Therefore, this paper comprehensively reviews the progress of several solar PV-based monitoring technologies focusing on various data processing modules and data transmission protocols. Each module and transmission protocol-based monitoring technology is investigated with regard to type, design, implementations, specifications, and limitations. The critical discussion and analysis are carried out with respect to configurations, parameters monitored, software, platform, achievements, and suggestions. Moreover, various key issues and challenges are explored to identify the existing research gaps. Finally, this review delivers selective proposals for future research works. All the highlighted insights of this review will hopefully lead to increased efforts toward the enhancement of the monitoring technologies in future sustainable solar PV applications.

Globally, wind energy is growing rapidly and has received huge consideration to fulfill global energy requirements. An accurate wind power forecasting is crucial to achieve a stable and reliable operation of the power grid. However, the unpredictability and stochastic characteristics of wind power affect the grid planning and operation adversely. To address these concerns, a substantial amount of research has been carried out to introduce an efficient wind power forecasting approach. Artificial Intelligence (AI) approaches have demonstrated high precision, better generalization performance and improved learning capability, thus can be ideal to handle unstable, inflexible and intermittent wind power. Recently, AI-based hybrid approaches have become popular due to their high precision, strong adaptability and improved performance. Thus, the goal of this review paper is to present the recent progress of AI-enabled hybrid approaches for wind power forecasting emphasizing classification, structure, strength, weakness and performance analysis. Moreover, this review explores the various influential factors toward the implementations of AI-based hybrid wind power forecasting including data preprocessing, feature selection, hyperparameters adjustment, training algorithm, activation functions and evaluation process. Besides, various key issues, challenges and difficulties are discussed to identify the existing limitations and research gaps. Finally, the review delivers a few selective future proposals that would be valuable to the industrialists and researchers to develop an advanced AI-based hybrid approach for accurate wind power forecasting toward sustainable grid operation.

Electric vehicles are receiving widespread attention around the world due to their improved performance and zero carbon emissions. The effectiveness of electric vehicles depends on proper interfacing between energy storage systems and power electronics converters. However, the power delivered by energy storage systems illustrates unstable, unregulated and substantial voltage drops. To overcome these limitations, electric vehicle converters, controllers and modulation schemes are necessary to achieve a secured and reliable power transfer from energy storage systems to the electric motor. Nonetheless, electric vehicle converters and controllers have shortcomings including a large number of components, high current stress, high switching loss, slow dynamic response and computational complexity. Therefore, this review presents a detailed investigation of different electric vehicle converters highlighting topology, features, components, operation, strengths and weaknesses. Moreover, this review explores the various types of electric vehicle converter controllers and modulation techniques concerning functional capabilities, operation, benefits and drawbacks. Besides, the significance of optimization algorithms in electric vehicle converters is illustrated along with their objective functions, executions and various factors. Furthermore, this review explores the key issues and challenges of electric vehicle converters, controllers and optimizations to identify future research gaps. Finally, important and specific suggestions are delivered toward the development of an efficient converter for future sustainable electric vehicle applications.

The use of distributed generation has received wide attention due to low maintenance costs, reduced transmission line losses and network congestion, as well as minimal impact on climate change and global warming. However, the distributed generation integrated to the distribution network introduces various protection problems that cannot be solved by conventional protection systems. These obstacles include the bi-directional power flow and the variation in the fault current level during the topology change. Thus, appropriate fault detection and protection scheme are strongly recommended to increase safety and reliability of the distributed network. This paper presents a comprehensive overview of the distributed network fault detection and protection strategies incorporated with distributed generation. This review also investigates the various fault detection approaches concerning types, communication methods, operation mode, constraints and benefits. Additionally, numerous island detection techniques are explored, focusing on generation types, parameters, cost and advantages. Moreover, the review outlines the various protection schemes highlighting categories, operation, constraints, strength and shortcomings. The key issues and challenges are discussed along with selective proposals for future research. All the highlighted viewpoints of this research will hopefully be beneficial to power system engineers and researchers for the advancement of distributed network fault and protection strategies for suitable operation and management of future distributed generation systems.