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This systematic review paper examines the current integration of artificial intelligence into energy management systems for electric vehicles. Using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) methodology, 46 highly relevant articles were systematically identified from extensive literature research. Recent advancements in artificial intelligence, including machine learning, deep learning, and genetic algorithms, have been analyzed for their impact on improving electric vehicle performance, energy efficiency, and range. This study highlights significant advancements in energy management optimization, route planning, energy demand forecasting, and real-time adaptation to driving conditions through advanced control algorithms. Additionally, this paper explores artificial intelligence’s role in diagnosing faults, predictive maintenance of electric propulsion systems and batteries, and personalized driving experiences based on driver preferences and environmental factors. Furthermore, the integration of artificial intelligence into addressing security and cybersecurity threats in electric vehicles’ energy management systems is discussed. The findings underscore artificial intelligence’s potential to foster innovation and efficiency in sustainable mobility, emphasizing the need for further research to overcome current challenges and optimize practical applications.

The increasing deployment of renewable energy sources is reshaping power systems and presenting new challenges for the integration of distributed generation and energy storage. Power converters have become essential to manage energy flows, coordinate storage systems, and maintain grid stability. This study presents a literature review following the PRISMA 2020 methodology, covering 71 peer-reviewed articles published between 2014 and 2024. The analysis organizes current research into five main areas: converter topologies, storage integration, grid interaction, advanced control strategies, and renewable energy applications. Recent developments include progress in multilevel and bidirectional converter designs, the use of wide-bandgap semiconductors (SiC, GaN), and the application of advanced control techniques such as model predictive control, fuzzy logic, and reinforcement learning. However, several challenges remain unresolved, including the lack of standardized validation protocols, limited implementation of modular and scalable converter solutions, and insufficient integration of hybrid storage technologies such as hydrogen and second-life batteries. Future efforts should focus on developing interoperable control platforms, extending field validation studies, and incorporating digital twins and AI-based supervisory systems to improve the reliability, efficiency, and scalability of converter-based energy storage solutions under high renewable energy scenarios.

The increasing demand for more efficient and sustainable power systems, driven by the integration of renewable energy, underscores the critical role of energy storage systems (ESS) and electric vehicles (EVs) in optimizing microgrid operations. This paper provides a systematic literature review, conducted in accordance with the PRISMA 2020 Statement, focusing on studies published between 2014 and 2024 and sourced from Web of Science and Scopus, resulting in 97 selected works. The review highlights the potential of EVs, not only as sustainable transport solutions but also as mobile storage resources, enhancing microgrid flexibility and stability through vehicle-to-grid (V2G) systems. It also underscores the importance of advanced control strategies, such as Model Predictive Control (MPC) and hybrid AC/DC microgrids, for improving energy flow management and operational resilience. Despite these advancements, gaps remain in the comprehensive integration of ESS and EVs, particularly regarding interoperability between microgrid components and the lack of optimization frameworks that holistically address dynamic pricing, grid stability, and renewable energy integration. This paper synthesizes existing technologies and offers insights for future research aimed at advancing the sustainability, efficiency, and economic viability of microgrids.

The growing complexity of distributed energy systems and the rise of peer-to-peer energy markets demand innovative solutions for efficient, resilient, and sustainable energy management. However, existing research often remains fragmented, with limited integration between control strategies, optimization frameworks, and practical implementation. This paper presents a comprehensive systematic review, following the PRISMA methodology, that synthesizes findings from 94 high-quality studies and addresses the lack of consolidated insights across technical, operational, and architectural layers. This review highlights advancements in six key areas: optimization and modeling, multi-agent systems, simulations, blockchain and smart contracts, robust frameworks, and electric machines. Despite progress, several studies reveal challenges related to scalability, data privacy, computational complexity, and system adaptability, particularly in dynamic and decentralized environments. Stochastic–robust optimization and multi-agent systems improve decentralized coordination, while blockchain enhances security and automation in peer-to-peer trading. Simulations validate energy strategies, bridging theory and practice, and electric machines support renewable integration and grid flexibility. The synthesis underscores the need for unified frameworks that combine artificial intelligence, predictive control, and secure communication protocols. This review aims to provide a roadmap for advancing distributed energy systems toward scalable, resilient, and sustainable energy solutions.

This study reviews advancements in high-frequency converters for renewable energy systems and electric vehicles, emphasizing their role in enhancing energy efficiency and sustainability. Using the PRISMA 2020 methodology, 73 high-quality studies from 2014 to 2024 were synthesized to evaluate innovative designs, advanced materials, control strategies, and future opportunities. Key findings reveal significant progress in converter topologies, such as dual active bridge and LLC resonant designs, which enhance efficiency and scalability through soft-switching. Wide-bandgap semiconductors, including silicon carbide and gallium nitride, have driven improvements in power density, thermal management, and compactness. Advanced control strategies, including adaptive and AI-driven methods, enhance stability and efficiency in microgrids and vehicle-to-grid systems. Applications in photovoltaic and wind energy systems demonstrate the converters’ impact on improving energy conversion and system reliability. Future opportunities focus on hybrid and multifunctional designs that integrate renewable energy, storage, and electric mobility with intelligent control technologies like digital twins and AI. These innovations highlight the transformative potential of high-frequency converters in addressing global energy challenges driving sustainable energy and transportation solutions. This review offers critical insights into current advancements and pathways for further research and development in this field.

The internet of things is undergoing rapid expansion, transforming diverse industries by facilitating device connectivity and supporting advanced applications. In the domain of energy production, internet of things holds substantial promise for streamlining processes and enhancing efficiency. This research introduces a comprehensive monitoring and energy management model tailored for the University of Cuenca’s microgrid system, employing internet of things and ThingSpeak as pivotal technologies. The proposed approach capitalizes on intelligent environments and employs ThingSpeak as a robust platform for presenting and analyzing data. Through the integration of internet of things devices and sensors, the photovoltaic system’s parameters, including solar radiation and temperature, are monitored in real time. The collected data undergo analysis using sophisticated models and are presented visually through ThingSpeak, facilitating effective energy management and decision making. The developed monitoring system underwent rigorous testing in a laboratory microgrid setup, where the photovoltaic system is interconnected with other generation and storage systems, as well as the electrical grid. This seamless integration enhances visibility and control over the microgrid’s energy production. The results attest to the successful implementation of the monitoring system, highlighting its efficacy in improving the supervision, automation, and analysis of daily energy production. By leveraging internet of things technologies and ThingSpeak, stakeholders gain access to real-time data, enabling them to analyze performance trends and optimize energy resources. This research underscores the practical application of internet of things in enhancing the monitoring and management of energy systems with tangible benefits for stakeholders involved.

This study evaluates a solar-powered electric mobility pilot implemented at the University of Cuenca (Ecuador), combining two electric vans with daytime charging from a 35 kWp PV microgrid. Real-world monitoring with SCADA covered one year of operation, including efficiency tests across urban, highway, and mountainous routes. Over the monitored period, the fleet completed 5256 km in 1384 trips with an average occupancy of approximately 87%. Energy use averaged 0.17 kWh/km, totaling 893.52 kWh, of which about 98.2% came directly from on-site PV generation; only 2.41% of the annual PV output was required for vehicle charging. This avoided 1310.52 kg of CO2 emissions compared to conventional vehicles. Operating costs were reduced by institutional electricity tariffs (0.065 USD/kWh) and the absence of additional PV investment, with estimated savings of around USD 2432 per vehicle annually. Practical guidance from the pilot includes aligning fleet schedules with peak solar generation, ensuring access to slow daytime charging points, maintaining high occupancy through route management, and using basic monitoring to verify performance. These results confirm the technical feasibility, economic competitiveness, and replicability of solar-electric transport in institutional settings with suitable solar resources and infrastructure.

The rapid integration of photovoltaic (PV) energy into weak low-voltage networks presents significant challenges to grid stability and power quality, highlighting the need for effective power smoothing methods. This paper proposes and evaluates three fuzzy logic-enhanced power smoothing techniques—V2GGlide (low-pass filter), V2GSUN (moving average filter), and V2GSmooth (ramp rate filter)—integrated with a lithium-ion battery energy storage system. The methods were tested under three distinct state-of-charge (SoC) conditions. Results show that V2GGlide achieved the highest variance reduction (22.24%) at high SoC levels, providing superior performance in mitigating power fluctuations and ensuring stable grid output. V2GSUN demonstrated consistent effectiveness, achieving variance reductions of up to 17.82% under low SoC conditions, making it well-suited for systems with intermediate storage availability. V2GSmooth exhibited balanced performance across all SoC levels, combining efficient energy delivery with reduced battery degradation, particularly at lower SoC levels. The proposed methods were validated under real-world conditions in a low-voltage microgrid laboratory connected to a public distribution network, confirming their practical applicability for enhancing PV energy integration and grid stability.

The integration of renewable energy sources into isolated microgrids introduces significant power fluctuations due to their intermittent nature. This study addresses the need for advanced power smoothing methods to enhance the stability of isolated networks. An innovative adaptive strategy is presented, combining photovoltaic solar generation with vehicle-to-grid technology, utilizing an enhanced adaptive moving average filter with fuzzy logic control. The primary objective is to dynamically optimize the time frame of the Li-ion battery energy storage system for immediate power stabilization, leveraging the high energy density and rapid response capabilities inherent in electric vehicle batteries. The methodology encompasses data acquisition from photovoltaic panels, definition of fuzzy logic control rules, and implementation of the proposed method within a computer-controlled system connected to a bidirectional three-phase inverter. Experimental results highlight the proposed method’s superiority over conventional moving averages and ramp-rate filters.

As the world increasingly embraces renewable energy as a sustainable power source, accurately assessing of solar energy potential becomes paramount. Photovoltaic (PV) systems, especially those integrated into urban rooftops, offer a promising solution to address the challenges posed by aging energy grids and rising fossil fuel prices. However, optimizing the placement of PV panels on rooftops remains a complex task due to factors like building shape, location, and the surrounding environment. This study introduces the Roof-Solar-Max methodology, which aims to maximize the placement of PV panels on urban rooftops while avoiding shading and panel overlap. Leveraging geographic information systems technology and 3D models, this methodology provides precise estimates of PV generation potential. Key contributions of this research include a roof categorization model, identification of PV-ready rooftops, optimal spatial distribution of PV panels, and innovative evaluation technology. Practical implementation in a real urban setting demonstrates the methodology’s utility for decision making in the planning and development of solar energy systems in urban areas. The main findings highlight substantial potential for PV energy generation in the studied urban area, with capacities reaching up to 444.44 kW. Furthermore, implementing PV systems on residential rooftops has proven to be an effective strategy for reducing CO2 emissions and addressing climate change, contributing to a cleaner and more sustainable energy mix in urban environments.