• Energy Research

Fuel cell electric vehicles (FCEVs) have received significant attention in recent times due to various advantageous features, such as high energy efficiency, zero emissions, and extended driving range. However, FCEVs have some drawbacks, including high production costs; limited hydrogen refueling infrastructure; and the complexity of converters, controllers, and method execution. To address these challenges, smart energy management involving appropriate converters, controllers, intelligent algorithms, and optimizations is essential for enhancing the effectiveness of FCEVs towards sustainable transportation. Therefore, this paper presents emerging energy management strategies for FCEVs to improve energy efficiency, system reliability, and overall performance. In this context, a comprehensive analytical assessment is conducted to examine several factors, including research trends, types of publications, citation analysis, keyword occurrences, collaborations, influential authors, and the countries conducting research in this area. Moreover, emerging energy management schemes are investigated, with a focus on intelligent algorithms, optimization techniques, and control strategies, highlighting contributions, key findings, issues, and research gaps. Furthermore, the state-of-the-art research domains of FCEVs are thoroughly discussed in order to explore various research domains, relevant outcomes, and existing challenges. Additionally, this paper addresses open issues and challenges and offers valuable future research opportunities for advancing FCEVs, emphasizing the importance of suitable algorithms, controllers, and optimization techniques to enhance their performance. The outcomes and key findings of this review will be helpful for researchers and automotive engineers in developing advanced methods, control schemes, and optimization strategies for FCEVs towards greener transportation.

This paper presents an optimal state of energy (SOE) estimation strategy of a lithium-ion battery using the back-propagation neural network (BPNN). Two heuristic optmization techniques named backtracketing search algorithm (BSA) and particle swarm optimization (PSO) algorithm are applied to improve the accuracy of BPNN model. Optimization algorithms are developed to determine the optimal value of hidden layer neurons and learning rate of BPNN model. Three most influencing factors including current, voltage and temperature are considered as the inputs to the optimal BPNN model. Federal Urban Driving Schedule (FUDS) is used to check the model robustness at 0°C, 25°C and 45°C. The model performance is evaluated based on the root mean square error (RMSE) and mean absolute error (MAE). The results show that the proposed model obtains good accuracy with an absolute error of ±5%. The BPNN based BSA model improves the SOE estimation accuracy by reducing RMSE and MAE by 2.8% and 4.4% compared to BPNN based PSO model at 25°C.

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.

The state of charge (SOC) is the residual capacity of a battery, which indicates the available charge left inside a battery to drive a vehicle. Accurate SOC estimation is of great significance for a lithium-ion battery to ensure its safe operation and to prevent it from over-charging or over-discharging. However, it is difficult to get an accurate value of SOC since it is an inner state of a battery cell, which cannot be directly measured. This paper presents an improved SOC estimation strategy for a lithium-ion battery using the back-propagation neural network (BPNN). Two algorithms, principal component analysis (PCA) and particle swarm optimization (PSO), are used to enhance the accuracy and robustness. PCA is utilized to select the most significant input features. The PSO algorithm is developed to determine the optimal value of hidden layer neurons and the learning rate since these parameters are the most critical factors in constructing an optimal BPNN model. The proposed model is tested and evaluated by using three electric vehicle drive cycles. The performance of the proposed model is compared with common BPNN and radial basis function neural network (RBFNN) models and verified based on the root mean square error (RMSE), mean square error (MSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and SOC error. The validation results are very effective in predicting SOC with very narrow SOC error which demonstrates the model robustness. The results indicate that the proposed model computes RMSE to be 0.58%, 0.72%, and 0.47% for the Beijing Dynamic Stress Test (BJDST), Federal Urban Drive Schedule (FUDS), and US06, cycle, respectively.

Boost converters and multilevel inverters (MLI) are frequently included in low-voltage solar photovoltaic (PV) systems for grid integration. However, the use of an inductor-based boost converter makes the system bulky and increases control complexity. Therefore, the switched-capacitor-based MLI emerges as an efficient DC/AC voltage convertor with boosting capability. To make classical topologies more efficient and cost-effective for sustainable power generation, newer topologies and control techniques are continually evolving. This paper proposes a reduced-component-count five-level inverter design for generating stable AC voltages for sustainable grid-integrated solar photovoltaic applications. The proposed topology uses seven switching devices of lower total standing voltage (TSV), three diodes, and two DC-link capacitors to generate five-level outputs. By charging and discharging cycles, the DC capacitor voltages are automatically balanced. Thus, no additional sensors or control circuitry is required. It has inherent voltage-boosting capability without any input boost converter. A low-frequency-based half-height (HH) modulation technique is employed in the standalone system for better voltage quality. Extensive simulations are performed in a MATLAB/Simulink environment to estimate the performance of the proposed topology, and 17.58% THDs are obtained in the phase voltages. Using a small inductor in series or an inductive load, the current THD reduces to 8.23%. Better dynamic performance is also observed with different loading conditions. A miniature five-level single-phase laboratory prototype is developed to verify the accuracy of the simulation results and the viability of the proposed topology.

Energy resiliency plays an important role in the proper functioning of data centers as they heavily rely on an uninterrupted power supply to ensure smooth operation. In the case of a power outage, the data center’s operation might be hampered, which results in system downtime, data, and economic loss. This issue is severe in developing countries where power supply infrastructures are inadequate and conventional. Microgrids can be an effective solution in this regard. Although several studies developed microgrids to observe the energy resilience benefit for some critical facilities, critical facilities like data centers are often overlooked. In addition, sustainability analysis of a microgrid is also scarce in the present literature. Therefore, one new resilience and sustainability indicator has been developed and implemented in this analysis to fill this gap. For this, new indicators, such as the resilience cost index (RCI) and renewable energy penetration (REP), were used in this study. This study used HOMER version 3.13.3 and REopt software to simulate a robust photovoltaic (PV) and battery microgrid for a hypothetical data center in Bangladesh. A random (48 h) outage was assigned to witness the adaptability of the modelled micro-grid. The suitable size of PV and battery was found to be 249,219 kW and 398,547 kWh, respectively. The system’s USD 18,079,948 net present value (NPV) demonstrates the economic potential of utilizing PV and battery microgrids for data centers. The RCI of the system is found to be 35%, while the REP is 87%. The energy consumption saving of the system is USD 21,822,076. The system emits 652% less CO2 than the grid. The result of this system is also compared with a diesel-based system. After comparison, it is found that the developed PV/battery microgrid provides better environmental and economical service than the diesel generator. During blackouts, the system keeps the data center powered up without interruption while improving energy resilience and lowering carbon emissions. The outcome of this current analysis can serve as a blueprint for other microgrid projects in Bangladesh and other developing countries. By integrating PV/battery microgrids, data centers can cut costs, reduce emissions, and optimize energy use. This will make data centers less reliant on grid services and more flexible to forthcoming development.

Abstract Global carbon emissions caused by fossil fuels and diesel-based vehicles have urged the necessity to move toward the development of electric vehicles and related battery storage systems. Lithium-ion batteries are the ideal candidate for electric vehicle due to their superior performance with regard to high energy density and long lifespan. The state of charge of lithium-ion batteries is one of the crucial evaluation indicators of the battery management system that confirms the extended battery life, better charging-discharging profiles, and safe driving of electric vehicles. However, the accuracy of the state of charge is influenced by several issues such as battery aging cycles, noise effects, and temperature impacts. Therefore, this review presents a detailed classification of the recent data-driven state of charge estimation highlighting algorithm, input features, configuration, execution process, strength, weakness and estimation error. This review critically investigates the various key implementation factors of the data-driven algorithms in terms of data preprocessing, hyperparameter adjustment, activation function, evaluation criteria, computational cost and robustness validation under uncertainties. In addition, the review explores the deficiencies of existing data-driven state of charge estimation algorithms to identify the gaps for future research. Finally, the review provides some effective future directions that would be beneficial to the automobile researchers and industrialists to design an accurate and robust state of charge estimation technique toward future sustainable electric vehicle applications.