• Energy Research

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.

Electric vehicles (EVs) have received widespread attention in the automotive industry as the most promising solution for lowering CO2 emissions and mitigating worldwide environmental concerns. However, the effectiveness of EVs can be affected due to battery health degradation and performance deterioration with lifespan. Therefore, an advanced and smart battery management technology is essential for accurate state estimation, charge balancing, thermal management, and fault diagnosis in enhancing safety and reliability as well as optimizing an EV’s performance effectively. This paper presents an analytical and technical evaluation of the smart battery management system (BMS) in EVs. The analytical study is based on 110 highly influential articles using the Scopus database from the year 2010 to 2020. The analytical analysis evaluates vital indicators, including current research trends, keyword assessment, publishers, research categorization, country analysis, authorship, and collaboration. The technical assessment examines the key components and functions of BMS technology as well as state-of-the-art methods, algorithms, optimization, and control surgeries used in EVs. Furthermore, various key issues and challenges along with several essential guidelines and suggestions are delivered for future improvement. The analytical analysis can guide future researchers in enhancing the technologies of battery energy storage and management for EV applications toward achieving sustainable development goals.

Varios avances importantes en la integración del almacenamiento de energía en las microrredes han impulsado una gran cantidad de investigación y desarrollo en los últimos diez años para lograr el objetivo de descarbonización global para 2050. La integración efectiva del sistema de almacenamiento de energía en la microrred es esencial para garantizar un funcionamiento seguro, fiable y resistente. Sin embargo, la utilización del almacenamiento de energía en las microrredes plantea varios problemas, como la mala calidad de la energía y las características de intermitencia. Para abordar estas preocupaciones, se requieren controladores de almacenamiento de energía y esquemas de optimización adecuados para gestionar y optimizar la energía de manera eficiente y segura. Aunque se han realizado y publicado varios trabajos de investigación a lo largo de los años, aún no se ha llevado a cabo la evaluación analítica de los controladores de almacenamiento de energía y la integración de los esquemas de optimización en las microrredes. Por lo tanto, este documento presenta una evaluación analítica exhaustiva de los controladores de almacenamiento de energía y los esquemas de optimización en Microgrid mediante el reconocimiento y la evaluación de los 110 manuscritos altamente influyentes que utilizan la base de datos Scopus en el año 2010-2021. El análisis analítico enfatiza las tendencias actuales de la investigación, la evaluación de palabras clave, la clasificación de la investigación, el análisis de países, la autoría y la colaboración en la investigación. El documento también analiza y compara 24 controladores y 21 esquemas de optimización en los 110 manuscritos altamente citados. Además, la discusión crítica y la evaluación se llevan a cabo en 15 áreas temáticas emergentes. El análisis constructivo identifica las limitaciones existentes y las lagunas de investigación en los 110 trabajos seleccionados. Al analizar los problemas existentes, este manuscrito proporciona varias pautas y sugerencias para mejoras futuras. Esta encuesta ayudará a profundizar los conceptos de desarrollo para lograr una mejor calidad de energía, prosperidad económica, ahorro de energía y una mayor eficiencia hacia la operación y gestión sostenibles en la microrred. Plusieurs avancées importantes dans l'intégration du stockage de l'énergie dans les micro-réseaux ont alimenté de nombreuses activités de recherche et de développement au cours des dix dernières années pour atteindre l'objectif mondial de décarbonisation d'ici 2050. L'intégration efficace du système de stockage d'énergie dans le micro-réseau est essentielle pour assurer un fonctionnement sûr, fiable et résilient. Néanmoins, l'utilisation du stockage d'énergie dans les micro-réseaux soulève plusieurs problèmes, notamment la mauvaise qualité de l'énergie et les caractéristiques d'intermittence. Pour répondre à ces préoccupations, des contrôleurs de stockage d'énergie et des schémas d'optimisation appropriés sont nécessaires pour gérer et optimiser l'énergie de manière efficace et sécurisée. Bien que divers travaux de recherche aient été réalisés et publiés au fil des ans, l'évaluation analytique des contrôleurs de stockage d'énergie et des schémas d'optimisation de l'intégration dans les micro-réseaux n'a pas encore été réalisée. Ainsi, cet article présente une évaluation analytique complète des contrôleurs de stockage d'énergie et des schémas d'optimisation dans Microgrid en reconnaissant et en évaluant les 110 manuscrits très influents utilisant la base de données Scopus au cours de l'année 2010-2021. L'analyse analytique met l'accent sur les tendances actuelles de la recherche, l'évaluation des mots clés, la classification de la recherche, l'analyse par pays, la paternité et la collaboration en matière de recherche. L'article discute et compare également 24 contrôleurs et 21 schémas d'optimisation dans les 110 manuscrits très cités. En outre, une discussion et une évaluation critiques sont menées sur 15 domaines émergents. L'analyse constructive identifie les limites existantes et les lacunes de recherche dans les 110 articles sélectionnés. En analysant les problèmes existants, ce manuscrit fournit plusieurs lignes directrices et suggestions pour des améliorations futures. Cette enquête aidera à approfondir les concepts de développement pour améliorer la qualité de l'énergie, la prospérité économique, les économies d'énergie et l'efficacité accrue en vue d'une exploitation et d'une gestion durables du micro-réseau. Several important advancements in the integration of energy storage into microgrids have fueled a lot of research and development over the last ten years to achieve the global decarbonization goal by 2050. The effective integration of the energy storage system in the microgrid is essential to ensure a safe, reliable, and resilient operation. Nevertheless, the utilization of energy storage in microgrids brings several issues, including poor power quality and intermittence characteristics. To address these concerns, appropriate energy storage controllers and optimization schemes are required to manage and optimize the power efficiently and securely. Although various research works have been performed and published over the years, the analytical assessment of energy storage controllers and optimization schemes integration into microgrids has not been carried out yet. Thus, this paper presents a comprehensive analytical evaluation of energy storage controllers and optimization schemes in Microgrid by recognizing and evaluating the highly influential 110 manuscripts using the Scopus database within the year 2010-2021. The analytical analysis emphasizes the current research trends, keyword evaluation, research classification, country analysis, authorship, and research collaboration. The paper also discusses and compares 24 controllers and 21 optimization schemes in the highly cited 110 manuscripts. Besides, critical discussion and assessment are conducted over 15 emerging subject areas. The constructive analysis identifies the existing limitations and research gaps in the selected 110 papers. By analyzing the existing issues, this manuscript provides several guidelines and suggestions for future improvement. This survey will help to deepen the development concepts to achieve improved power quality, economic prosperity, energy savings, and increased efficiency towards sustainable operation and management in the microgrid. أدت العديد من التطورات المهمة في دمج تخزين الطاقة في الشبكات الدقيقة إلى تغذية الكثير من البحث والتطوير على مدى السنوات العشر الماضية لتحقيق الهدف العالمي لإزالة الكربون بحلول عام 2050. يعد التكامل الفعال لنظام تخزين الطاقة في الشبكة الدقيقة أمرًا ضروريًا لضمان التشغيل الآمن والموثوق والمرن. ومع ذلك، فإن استخدام تخزين الطاقة في الشبكات الدقيقة يجلب العديد من المشكلات، بما في ذلك ضعف جودة الطاقة وخصائص الانقطاع. لمعالجة هذه المخاوف، يلزم وجود وحدات تحكم مناسبة في تخزين الطاقة وخطط تحسين لإدارة الطاقة وتحسينها بكفاءة وأمان. على الرغم من إجراء العديد من الأعمال البحثية ونشرها على مر السنين، إلا أن التقييم التحليلي لوحدات التحكم في تخزين الطاقة ودمج خطط التحسين في الشبكات الصغيرة لم يتم تنفيذه بعد. وبالتالي، تقدم هذه الورقة تقييمًا تحليليًا شاملاً لوحدات التحكم في تخزين الطاقة وخطط التحسين في Microgrid من خلال التعرف على 110 مخطوطات ذات تأثير كبير وتقييمها باستخدام قاعدة بيانات Scopus خلال العام 2010-2021. يؤكد التحليل التحليلي على اتجاهات البحث الحالية، وتقييم الكلمات الرئيسية، وتصنيف البحث، والتحليل القطري، والتأليف، والتعاون البحثي. كما تناقش الورقة وتقارن بين 24 وحدة تحكم و 21 مخططًا للتحسين في 110 مخطوطة مستشهد بها بشدة. إلى جانب ذلك، يتم إجراء مناقشة وتقييم نقديين لأكثر من 15 مجالًا موضوعيًا ناشئًا. يحدد التحليل البنّاء القيود الحالية والثغرات البحثية في الأوراق البحثية الـ 110 المختارة. من خلال تحليل المشكلات الحالية، توفر هذه المخطوطة العديد من الإرشادات والاقتراحات للتحسين المستقبلي. سيساعد هذا المسح على تعميق مفاهيم التنمية لتحقيق تحسين جودة الطاقة والازدهار الاقتصادي وتوفير الطاقة وزيادة الكفاءة نحو التشغيل والإدارة المستدامين في الشبكة الصغيرة.

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.

The development of a supercapacitor management system (SMS) for clean energy applications is crucial to addressing carbon emissions problems. Consequently, state of charge (SOC), state of health (SOH), and remaining useful life (RUL) for SMS must be developed to evaluate supercapacitor robustness and reliability for mitigating supercapacitor issues related to safety and economic loss. State estimation of SMS results in safe operation and eliminates undesirable event occurrences and malfunctions. However, state estimations of SMS are challenging and tedious, as SMS is subject to various internal and external factors such as internal degradation mechanism and environmental factors. This review presents a comprehensive discussion and analysis of model-based and data-driven-based techniques for SOC, SOH, and RUL estimations of SMS concerning outcomes, advantages, disadvantages, and research gaps. The work also investigates various key implementation factors such as a supercapacitor test bench platform, experiments, a supercapacitor cell, data pre-processing, data size, model operation, functions, hyperparameter adjustments, and computational capability. Several key limitations, challenges, and issues regarding SOC, SOH, and RUL estimations are outlined. Lastly, effective suggestions are outlined for future research improvements towards delivering accurate and effective SOC, SOH, and RUL estimations of SMS. Critical analysis and discussion would be useful for developing accurate SMS technology for state estimation of a supercapacitor with clean energy and high reliability, and will provide significant contributions towards reducing greenhouse gas (GHG) to achieve global collaboration and sustainable development goals (SDGs).

Remaining useful life (RUL) is a crucial assessment indicator to evaluate battery efficiency, robustness, and accuracy by determining battery failure occurrence in electric vehicle (EV) applications. RUL prediction is necessary for timely maintenance and replacement of the battery in EVs. This paper proposes an artificial neural network (ANN) technique to predict the RUL of lithium-ion batteries under various training datasets. A multi-channel input (MCI) profile is implemented and compared with single-channel input (SCI) or single input (SI) with diverse datasets. A NASA battery dataset is utilized and systematic sampling is implemented to extract 10 sample values of voltage, current, and temperature at equal intervals from each charging cycle to reconstitute the input training profile. The experimental results demonstrate that MCI profile-based RUL prediction is highly accurate compared to SCI profile under diverse datasets. It is reported that RMSE for the proposed MCI profile-based ANN technique is 0.0819 compared to 0.5130 with SCI profile for the B0005 battery dataset. Moreover, RMSE is higher when the proposed model is trained with two datasets and one dataset, respectively. Additionally, the importance of capacity regeneration phenomena in batteries B0006 and B0018 to predict battery RUL is investigated. The results demonstrate that RMSE for the testing battery dataset B0005 is 3.7092, 3.9373 when trained with B0006, B0018, respectively, while it is 3.3678 when trained with B0007 due to the effect of capacity regeneration in B0006 and B0018 battery datasets.

Real-time battery SOX estimation including the state of charge (SOC), state of energy (SOE), and state of health (SOH) is the crucial evaluation indicator to assess the performance of automotive battery management systems (BMSs). Recently, intelligent models in terms of deep learning (DL) have received massive attention in electric vehicle (EV) BMS applications due to their improved generalization performance and strong computation capability to work under different conditions. However, estimation of accurate and robust SOC, SOH, and SOE in real-time is challenging since they are internal battery parameters and depend on the battery’s materials, chemical reactions, and aging as well as environmental temperature settings. Therefore, the goal of this review is to present a comprehensive explanation of various DL approaches for battery SOX estimation, highlighting features, configurations, datasets, battery chemistries, targets, results, and contributions. Various DL methods are critically discussed, outlining advantages, disadvantages, and research gaps. In addition, various open challenges, issues, and concerns are investigated to identify existing concerns, limitations, and challenges. Finally, future suggestions and guidelines are delivered toward accurate and robust SOX estimation for sustainable operation and management in EV operation.

Remaining Useful Life (RUL) prediction for lithium-ion batteries has received increasing attention as it evaluates the reliability of batteries to determine the advent of failure and mitigate battery risks. The accurate prediction of RUL can ensure safe operation and prevent risk failure and unwanted catastrophic occurrence of the battery storage system. However, precise prediction for RUL is challenging due to the battery capacity degradation and performance variation under temperature and aging impacts. Therefore, this paper proposes the Multi-Channel Input (MCI) profile with the Recurrent Neural Network (RNN) algorithm to predict RUL for lithium-ion batteries under the various combinations of datasets. Two methodologies, namely the Single-Channel Input (SCI) profile and the MCI profile, are implemented, and their results are analyzed. The verification of the proposed model is carried out by combining various datasets provided by NASA. The experimental results suggest that the MCI profile-based method demonstrates better prediction results than the SCI profile-based method with a significant reduction in prediction error with regard to various evaluation metrics. Additionally, the comparative analysis has illustrated that the proposed RNN method significantly outperforms the Feed Forward Neural Network (FFNN), Back Propagation Neural Network (BPNN), Function Fitting Neural Network (FNN), and Cascade Forward Neural Network (CFNN) under different battery datasets.