Accurate State-of-Charge (SOC) estimation is essential for ensuring the safety, efficiency, and longevity of lithium-ion batteries in electric vehicles (EVs). However, achieving reliable SOC prediction under real-world conditions remains challenging due to nonlinear electrochemical behavior, temperature dependence, and dynamic load variability. This study presents a comprehensive evaluation of SOC estimation performance under diverse operating conditions, focusing on the combined effects of temperature, current profile complexity, and SOC range. To acquire the experimental dataset, a test setup was designed, and experiments were conducted at 10 degrees C, 25 degrees C, and 40 degrees C for charge-discharge and Hybrid Pulse Power Characterization (HPPC) tests. These datasets encompass a wide range of operational scenarios, ranging from low-rate and steady-state conditions to highly dynamic load pattern representative of actual EV operations. Two data-driven approaches, Feedforward Neural Network (FNN), and Random Forest (RF), were trained and evaluated across all datasets to investigate their estimation robustness and generalization capability. The results demonstrate that both models achieve their highest accuracy under nominal thermal conditions (25 degrees C) and within the mid-SOC range (20-80%), whereas their performance deteriorates significantly under extreme temperatures and irregular current profiles. The FNN model consistently outperformed the RF, yielding lower maximum errors and smoother error distributions. These findings underscore the importance of dataset structure, thermal conditions, and SOC range in determining estimation accuracy, and highlight the necessity of realistic, scenario-driven testing for robust algorithm validation. By providing one of the most comprehensive experimental assessments of SOC estimation under realistic EV operating conditions, this study establishes a solid foundation for developing adaptive algorithms and more reliable battery management systems.

Reliable and resilient communication is essential for disaster recovery and emergency response, yet terrestrial infrastructure often fails during large-scale natural disasters. This paper proposes a High-Altitude Platform Station (HAPS) and Reconfigurable Intelligent Surfaces (RIS)-assisted Internet of Things (IoT) communication system to restore connectivity in disaster-affected areas. Distributed IoT sensors collect critical environmental data and forward it to nearby gateways via short-range links, while the HAPS-RIS system provides backhaul to these gateways. To overcome the severe double path loss of passive RIS at high altitudes, we propose a dynamically adjustable sub-connected active RIS architecture that can reconfigure the number of elements connected to each power amplifier through switching mechanisms. Simulation results demonstrate substantial gains in downlink and uplink data rates, as well as system energy efficiency, compared with conventional passive RIS schemes. Moreover, a 1 dB increase in ground-station transmit power yields approximately 20-30 Mbps improvement in gateway data rates. These findings confirm that HAPS-RIS technology offers an effective and energy-efficient approach for resilient IoT backhaul in 6G non-terrestrial networks, particularly in line-of-sight (LoS)-dominant HAPS-ground backhaul scenarios.

As natural disasters become more frequent and severe, ensuring a resilient communications infrastructure is of paramount importance for effective disaster response and recovery. This disaster-resilient infrastructure should also respond to sustainability goals by providing an energy-efficient and economically feasible network that is accessible to everyone. To this end, this paper provides a comprehensive exploration of the technological solutions and strategies necessary to build and maintain resilient communications networks that can withstand and quickly recover from disaster scenarios. The paper starts with a survey of existing literature and related reviews to establish a solid foundation, followed by an overview of the global landscape of disaster communications and power supply management. We then introduce the key enablers of communications and energy resource technologies to support communications infrastructure, examining emerging trends that improve the resilience of these systems. Pre-disaster planning is emphasized as a critical phase where proactive communication and energy supply strategies can significantly mitigate the impact of disasters. We also explore the essential technologies for disaster response, focusing on real-time communications and energy solutions that support rapid deployment and coordination in times of crisis. The paper then presents post-disaster communication and energy management planning for effective rescue and evacuation operations. The main findings derived from the comprehensive survey are also summarized for each disaster phase. This is followed by an analysis of existing vendor products and services as well as standardization efforts and ongoing projects that contribute to the development of resilient infrastructures. A detailed case study of the Turkiye earthquakes is presented to illustrate the practical application of these technologies and strategies. Finally, we address the open issues and challenges in realizing sustainable and resilient communication infrastructures and provide insights into future research directions. By incorporating lessons learned from various disaster scenarios, this paper presents strategic recommendations that enhance the resilience and adaptability of communication systems in the context of disaster relief and management.

This paper proposes an enhanced droop-based grid-forming (GFM) converter control framework incorporating a variable operating frequency (VOF) mechanism to prevent active power limit violations during large frequency excursions. In addition, a linear quadratic integral (LQI)-based inner voltage controller is implemented and systematically compared with a classical cascaded PI structure within the same GFM configuration. The proposed approach is evaluated using selected model acceptance tests derived from the Australian Energy Market Operator (AEMO) Dynamic Model Acceptance Testing (DMAT) guidelines under grid conditions characterized by short circuit ratio (SCR) = 10 and X/R = 14 at the Point of Connection (POC). The proposed VOF mechanism maintained the inverter's active power output within a predefined range of 0.5 pu to 1.15 pu during extreme frequency deviations. Furthermore, the FRT algorithm, designed separately for both PI and LQI-based models, kept the current below 1.2 pu. Across voltage, frequency, fault ride-through, and phase-angle disturbance tests, the LQI-based controller exhibits improved transient damping, reduced oscillatory behavior, and faster settling compared to the PI-based implementation, while maintaining comparable steady-state performance. The proposed framework is further validated on the IEEE 9-bus system under multi-machine fault conditions. The results provide a DMAT-based and quantitatively validated framework for enhancing droop-based GFM converter robustness under grid-code-relevant large disturbances in compliance with IEEE Std 2800-2022 requirements.

High-Altitude Platform Stations (HAPS) have emerged as a promising solution for 6G and beyond communication systems due to their wide coverage capability and strong line-of-sight characteristics. Reconfigurable Intelligent Surfaces (RIS), on the other hand, represent a promising technology capable of extending coverage by controlling the phase of electromagnetic waves. However, employing a dedicated phase shifter for each element in large-scale RIS systems significantly increases hardware complexity, computational burden, and power consumption. In this work, a low-complexity architecture based on phase-shifter sharing is proposed for HAPS–RIS assisted communication systems. In the proposed structure, multiple RIS elements are controlled by a single phase shifter, thereby reducing hardware complexity, computational load, and circuit power consumption. Simulation results demonstrate that the proposed architecture can effectively reduce hardware complexity while preserving system performance.

Accurate state-of-charge (SOC) estimation is a key requirement for the safe and efficient management of lithium-ion batteries in electric vehicles, especially under varying thermal and dynamic operating conditions. This study presents a comprehensive, algorithm-oriented assessment of several deep learning and hybrid SOC estimation architectures-including feedforward neural networks (FNN), gated recurrent networks (GRU), long short-term memory networks (LSTM), temporal convolutional networks (TCN), and their hybrid combinations-using a multi-temperature dataset collected at 10 degrees C, 25 degrees C, and 40 degrees C under diverse dynamic load profiles and standardized drive cycles such as UDDS, HWFET, US06, and LA92. All architectures were trained and evaluated under a unified preprocessing and training configuration to ensure methodological consistency and a fair basis for comparison. The evaluation highlights how different recurrent, convolutional, and hybrid architectures respond to thermal variations and dynamic load transitions, revealing model-specific strengths and limitations under realistic operating conditions. Among the evaluated models, the hybrid FNN + GRU architecture demonstrated the most reliable overall performance, achieving an RMSE of 1.11 % and reducing peak estimation errors to 3.6 % under nominal temperature conditions. SOC-zone analysis further showed characteristic error amplification at low and high SOC levels, emphasizing the importance of architectures capable of capturing nonlinear boundary dynamics. Computational benchmarking indicated that hybrid structures-particularly FNN + GRU-also provide an advantageous balance between estimation accuracy and inference speed, supporting their suitability for embedded Battery Management Systems (BMSs) with real-time constraints. Overall, this study contributes a unified evaluation framework that simultaneously addresses thermal robustness, dynamic load variability, SOC-dependent behavior, and computational efficiency, offering practical guidance for selecting reliable and deployable SOC estimation models for next-generation electric vehicle BMSs.

This paper investigates the performance of high-altitude platform station (HAPS)-assisted communication systems employing either reconfigurable intelligent surfaces (RIS) or relay stations (RS) under non-orthogonal multiple access (NOMA) scheme. Practical system impairments, including hardware impairments (HWI) and imperfect channel state information (CSI), are explicitly considered. The results show that HAPS-RIS outperforms HAPS-RS in terms of both sum-rate and energy efficiency under non-ideal conditions due to its passive nature, which avoids noise amplification. Furthermore, it is demonstrated that RIS element allocation and user spatial distribution significantly impact NOMA performance, where increased user separation and proper allocation enhance channel disparity and improve system efficiency. Despite its higher sensitivity to imperfect CSI, HAPS-RIS can effectively compensate for performance degradation through large-scale RIS element deployment, maintaining a performance advantage over half-duplex RS-based systems. These insights provide useful design guidelines for impairment-aware HAPS-assisted 6G communication systems.

The increasing intensity and frequency of disasters worldwide necessitate the development of more resilient, efficient, and adaptable disaster management systems. Conventional centralized systems often fail to meet the complex requirements of disaster scenarios and are inefficient in terms of communication, energy, and decision-making processes. This paper proposes a novel blockchain-based Proof of Genesis (PoG) method to strengthen systems’ resilience in disaster scenarios. Unlike traditional blockchain mechanisms, which may not be optimized for the high risks and dynamic nature of disaster scenarios, PoG uses meiosis, mutation, recombination and natural selection steps to ensure the robustness, scalability and sustainability of the incapacitated system during a disaster. A comprehensive architecture that integrates PoG with strategic energy and communications frameworks to create a resilient, decentralized system that can withstand and quickly recover from the effects of disasters, is proposed. Through comparative analyses and extensive simulations, we show the superiority of the PoG method over conventional blockchain approaches by offering high security as Proof of Work (PoW), and less energy consumption as Proof of Stake (PoS).Moreover, it is more scalable than Bitcoin and Ethereum and can be scaled as nearly as Polygon. Our results show that the proposed approach offers a promising way to revolutionize disaster management systems.

Accurate state-of-health (SOH) estimation is essential for reliable lifetime management of lithium-ion batteries. However, complex nonlinear electrochemical degradation processes make SOH estimation challenging. This study proposes an electrochemically informed SOH estimation framework integrating incremental capacity analysis (ICA) with single and hybrid deep learning architectures. Voltage, current, temperature, and filtered ICA features are extracted from aging experiments on 2800 mAh lithium-ion cells covering an SOH range of 80 %–100 %. A systematic comparison is performed using FNN, TCN, GRU, LSTM, Bi-LSTM, and hybrid architectures under identical conditions. In addition to conventional performance metrics, SOH-level error analysis evaluates model behavior across degradation stages, while controlled feature ablation quantifies the incremental contribution of ICA features relative to voltage, current, and temperature inputs. Hybrid architectures outperform single-model approaches. For discharge-profile datasets, TCN + GRU achieves the best performance with an RMSE of 0.73 %, MAE of 0.58 %, and maximum error of 3.08 %, while TCN + GRU + FNN achieves an RMSE of 0.82 % for charge-profile datasets. Ablation results demonstrate consistent reductions in RMSE, MAE, and MAPE with ICA incorporation, confirming that ICA provides additional degradation-sensitive information and that hybrid architectures improve SOH estimation accuracy and stability across the investigated aging stages.

Accurate forecasting of day-ahead electricity prices is crucial for market participants in the electricity market due to their impact on trading strategies, cost management and grid stability. Unlike traditional daily hourly forecasts, sub-hourly (e.g., 15 min) forecasts capture intra-hour ramps and volatility caused by high wind and solar penetration. They enable faster imbalance detection and mitigation, more flexible operations such as battery energy storage and demand response, and improved bidding performance. There is a lack studies in the literature on forecasting day-ahead electricity prices over such shorter time period. In this paper, a novel AutoGluon-based framework for electricity price forecasting is presented. This framework solves optimization problem that minimizes the mean absolute error of a proposed weighted ensemble forecasting model for different electricity price periods. It dynamically adapts to different time periods during the day and improves the forecast accuracy by feature extraction and fine-tuning the AutoGluon parameters. An initial analysis of electricity price fluctuations over different time periods shows the importance of capturing temporal price patterns. The proposed method newly generates data set and it utilizes feature extraction to identify the key price determinants and employs the AutoGluon machine learning model for more granular price forecasting. Furthermore, the framework is extended to predict electricity prices for shorter time intervals, which improves the adaptability to market dynamics. The results show that the proposed approach improves the prediction accuracy compared to conventional models and provides valuable insights to market operators and participants.

Voltage stability is an important issue to be studied in power systems. The ever-increasing demand ofelectrical energy, new power generation plant installations to meet the increasing demand to meet theincreasing demand, and failures in the power system adversely affect the voltage stability. Today, FlexibleAlternating Current Transmission Systems (FACTS devices) are used in power systems to improve voltage stability, and Distributed Generation sources (DGs) are integrated into power systems to meet energy demandand improve voltage stability. In this study, the IEEE 30 bus power system and the North-west Anatolia (KBA) power system are investigated in base (nominal), overload, and line outage cases regarding voltagestability. Static Var Compensator (SVC) and Thyristor Controlled Series Capacitor (TCSC) from FACTSdevices, Fuel Cell (YH), Solar Power Plant (GES), and Wind Power Plant (RES) from DGs are connectedseparately and together in the power systems. Changes in bus voltage values and loading parameters of powersystems are analyzed.

Intelligent Reflecting Surfaces (IRS) have emerged as a promising technology for 6G and beyond, enabling control over wireless communication channels. This study focuses on Simultaneously Transmitting and Reflecting (STAR)-IRS architectures, which possess the capability to both transmit and reflect signals simultaneously. The performance of active and passive STAR-IRS configurations is comparatively analyzed. In a High Altitude Platform Station (HAPS)-base station scenario, a STAR-IRS is mounted on an Uncrewed Aerial Vehicle (UAV) and its performance is evaluated accordingly. The proposed system is examined under both orthogonal multiple access (OMA) and non-orthogonal multiple access (NOMA) schemes. Monte Carlo simulations are conducted under varying transmission powers and different numbers of IRS elements. The results reveal that active STAR-IRS-assisted NOMA configurations yield significant improvements in both spectral and energy efficiency.

Natural disasters often disrupt communication networks and severely hamper emergency response and disaster management. Existing solutions, such as portable communication units and cloud-based network architectures, have improved disaster resilience but fall short if both the Radio Access Network (RAN) and backhaul infrastructure become inoperable. To address these challenges, we propose a demand-driven communication system supported by High Altitude Platform Stations (HAPS) to restore communication in an affected area and enable effective disaster relief. The proposed emergency response network is a promising solution as it provides a rapidly deployable, resilient communications infrastructure. The proposed HAPS-based communication can play a crucial role not only in ensuring connectivity for mobile users but also in restoring backhaul connections when terrestrial networks fail. As a bridge between the disaster management center and the affected areas, it can facilitate the exchange of information in real time, collect data from the affected regions, and relay crucial updates to emergency responders. Enhancing situational awareness, coordination between relief agencies, and ensuring efficient resource allocation can significantly strengthen disaster response capabilities. In this article, simulations show that HAPS with hybrid optical/THz links boosts backhaul capacity and resilience, even in harsh conditions. HAPS-enabled RAN in S- and Ka-bands ensures reliable communication for first responders and disaster-affected populations. This article also explores the integration of HAPS into emergency communication frameworks and standards, as it has the potential to improve network resilience and support effective disaster management.

This paper investigates the integration of active reconfigurable intelligent surfaces (RIS) relay with high-altitude platform stations (HAPS) to enhance non-terrestrial network (NTN) performance in next-generation wireless systems. While prior studies focused on passive RIS architectures, the severe path loss and double fading in long-distance HAPS links make active RIS a more suitable alternative due to its inherent signal amplification capabilities. We formulate a sum-rate maximization problem to jointly optimize power allocation and RIS element assignment for ground user equipments (UEs) supported by a HAPS-based active RIS-assisted communication system. To reduce power consumption and hardware complexity, several sub-connected active RIS architectures are also explored. Simulation results reveal that active RIS configurations significantly outperform passive RIS in terms of quality of service (QoS). Moreover, although fully-connected architectures achieve the highest throughput, sub-connected schemes demonstrate superior energy efficiency under practical power constraints. These findings highlight the potential of active RIS-enabled HAPS systems to meet the growing demands of beyond-cellular coverage and green networking.

Intelligent Reflecting Surfaces (IRS) technology lets us to control the wireless channel that provides end to end to communication between the transmitter and receiver devices. Thanks to this feature that IRS is among the prominent technologies for 6G communication systems. It is important to reveal the effects of the IRS, which can be in the passive or active form according to the functionality, over different communication scenarios and different system models. Thus, in this study, both passive and active IRS types are combined with orthogonal multiple access (OMA) and non-orthogonal multiple access (NOMA) communication system models. The performance effect of both passive and active IRS types over the OMA and NOMA systems are examined in detail by obtaining Monte Carlo simulations under different parameters such as transmission power, the position of IRS, and the number of IRS elements.

A virtual power plant is a system containing multiple distributed generators aggregated and flexibly coordinated to act as a single power source. This study investigates the active power, reactive power, frequency, and voltage support provided by a virtual power plant interconnected with the grid. The investigation encompasses the analysis of grid-forming (GFM)-controlled wind and solar power plant units, considering the fluctuating power generation from solar and wind sources. The real hourly wind and solar generation profiles from currently operational plants are used as the active power set points. The stability and power reference tracking of grid-connected converters are analyzed using dispatchable virtual oscillator control (dVOC) and droop control-based methods. The results show that both control strategies substantially improve the virtual power plant's ability to ensure grid stability and accurately track power references, despite the inherent variability of wind and solar energy generation.

In electrical power systems where the proportion of synchronous generators (SG) is gradually decreasing, grid-forming (GFM) converters need to be installed and controlled to meet all the system requirements that SGs have provided to date. Modeling, control, and implementation of GFM converters have been the subject of numerous studies in recent years, particularly in the context of ensuring grid stability during the transition to non-synchronous renewable energy sources. This paper provides a comprehensive literature review on the modeling and control of grid-connected converters. In particular, the focus is placed on GFM-type control structures, objectives, and applications. Both grid-following (GFL) and GFM control structures are detailed. Then, the objectives of controlling GFM converters in power systems are discussed in detail. Finally, some completed and ongoing GFM installation projects around the world are summarized under the subheadings of battery energy storage system (BESS), GFM wind, hybrid, and high voltage direct current (HVDC).

Natural disasters can have catastrophic consequences, a poignant example is the series of $7.7$ and $7.6$ magnitude earthquakes that devastated T\"urkiye on February 6, 2023. To limit the damage, it is essential to maintain the communications infrastructure to ensure individuals impacted by the disaster can receive critical information. The disastrous earthquakes in T\"urkiye have revealed the importance of considering communications and energy solutions together to build resilient and sustainable infrastructure. Thus, this paper proposes an integrated space-air-ground-sea network architecture that utilizes various communications and energy-enabling technologies. This study aims to contribute to the development of robust and sustainable disaster-response frameworks. In light of the T\"urkiye earthquakes, two methods for network management are proposed: the first aims to ensure sustainability in the pre-disaster phase and the second aims to maintain communications during the in-disaster phase. In these frameworks, communications technologies such as High Altitude Platform Station(s)(HAPS), which are among the key enablers to unlock the potential of 6G networks, and energy technologies such as Renewable Energy Sources (RES), Battery Energy Storage Systems (BESSs), and Electric Vehicles (EVs) have been used as the prominent technologies. By simulating a case study, we demonstrate the performance of a proposed framework for providing network resiliency. The paper concludes with potential challenges and future directions to achieve a disaster-resilient network architecture solution.

Gerilim kararlılığı, güç sistemlerinde incelenmesi gereken önemli bir konudur. Sürekli artan elektrik enerjisi talebi, artan talebi karşılamak için yeni elektrik üretim santrallerinin kurulumu ve güç sistem arızaları gerilim kararlılığını olumsuz yönde etkilemektedir. Günümüzde gerilim kararlılığını iyileştirmek için güç sistemlerinde Esnek Alternatif Akım İletim Sistemleri (FACTS cihazları) kullanılmakta olup, ayrıca enerji talebini karşılamak ve gerilim kararlılığını iyileştirmek için güç sistemlerine Dağıtık Üretim Kaynakları (DGs) entegre edilmektedir. Bu çalışmada, IEEE 30 baralı güç sistemi ile Kuzeybatı Anadolu (KBA) güç sistemi, gerilim kararlılığı bakımından temel (nominal), aşırı yüklenme ve hat kopması durumlarında incelenmiştir. İncelenen güç sistemlerine FACTS cihazlarından Statik Var Kompanzatör (SVC) ve Tristör Kontrollü Seri Kapasitör (TCSC), DGs’lerden ise Yakıt Hücresi (YH), Güneş Enerji Santrali (GES) ve Rüzgâr Enerji Santrali (RES) ayrı ayrı ve birlikte bağlanmıştır. Güç sistemlerinin bara gerilim değerlerinde ve yüklenme parametresinde meydana gelen değişimler analiz edilmiştir.