Search Results (5,986)

Maritime power systems must reduce fuel use and emissions while improving resilience. We study a shipboard PV–battery subsystem interfaced with a DC–DC converter running maximum power point tracking (MPPT) and curve-scanning GMPPT to manage partial shading. Dynamic average-value models capture irradiance steps and show GMPPT sustains operation near the global MPP without local peak trapping. We compare converter options—conventional single-port stages, high-gain bidirectional dual-PWM converters, and three-level three-port topologies—provide sizing rules for passives, and note soft-switching in order to limit loss. A Fourier framework links the switching ripple to power quality metrics: as irradiance falls, the current THD rises while the PCC voltage distortion remains constant on a stiff bus. We make the loss relation explicit via \({{I}_{rms}^{2}R}\) scaling with THDi and propose a simple reactive power policy, assigning VAR ranges to active power bins. For AC-coupled cases, a hybrid EMT plus transient stability workflow estimates ride-through margins and critical clearing times, providing a practical path from modeling to monitoring. Full article

The search for high-capacity, stable anode materials is crucial for advancing lithium-ion battery (LIB) technology. Although carbon nanotubes (CNTs) are known for their excellent electrical conductivity and mechanical strength, their practical capacity is still limited. This study presents an advanced anode design by molecular functionalizing both single-walled and multi-walled carbon nanotubes (SWCNTs and MWCNTs) with tetrabromocobalt phthalocyanine (CoPc), resulting in CoPc/SWCNT and CoPc/MWCNT hybrid materials. Metal phthalocyanines (MPcs) are recognized for their tunable and redox-active properties. In CoPc, the redox-active metal centers and π-conjugated structure are uniformly attached to the CNT surface through strong π-π interactions. This synergistic combination significantly boosts the lithium-ion (Li-ion) storage capacity by offering numerous coordination sites for Li-ions and enhancing charge transfer kinetics. Electrochemical analysis shows that the CoPc-SWCNT active anode electrode material shows an impressive reversible capacity of 1216 mAh g⁻¹ after 100 cycles at a current density of 0.1 A g⁻¹, substantially surpassing the capacities of pristine CoPc (327 mAh g⁻¹) and a CoPc/MWCNT hybrid (488 mAh g⁻¹). Furthermore, the CoPc/SWCNT anode exhibited exceptional rate capability and outstanding long-term cyclability. These results underscore the effectiveness of non-covalent functionalization with SWCNTs in enhancing the electrical conductivity, structural stability, and active site utilization of CoPc, positioning CoPc/SWCNT hybrids as a highly promising anode material for high-performance Li-ion storage. Full article

Fault diagnosis of battery energy storage systems (BESSs) in dynamic operating conditions presents significant challenges due to complex spatiotemporal patterns and measurement noise. This research proposes a novel thermal fault diagnosis framework for BESSs based on Bayesian inference and a Kalman filter. Firstly, PLS-based spatiotemporal feature extraction is designed to capture temporal dependencies. Based on Bayesian global exploration and Kalman real-time weight adaptation, a dual-stage optimization strategy is proposed to derive a multiscale detection index with the dominant statistic, the residual statistic, and the module voltage similarity. A time window-based cumulative contribution strategy is constructed for precise cell localization. Finally, the experimental validation on a Li-ion battery pack demonstrates the proposed method’s superior performance: 96.92–99.90% anomaly detection rate, false alarm rate ranging from 0.10% to 7.22%, detection delays of 1–27 s, and 100% accuracy in fault localization. The proposed framework provides a comprehensive solution for safety management of BESSs and is significant for battery life and energy sustainability. Full article

Battery fault detection is crucial for maintaining the safety and reliability of large-scale lithium-ion battery systems, especially in demanding applications like electric vehicles and energy storage power stations. However, existing research primarily addresses either temporal patterns or spatial variations in isolation. This paper presents a comprehensive review of fault detection from a spatio-temporal perspective, with a specific focus on AI-driven methods that integrate temporal dynamics with spatial sensor data. The contributions of this review include an in-depth analysis of advanced techniques such as transfer learning, foundation models, and physics-informed neural networks, emphasizing their potential for modeling complex spatio-temporal dependencies. On the engineering side, this review surveys the practical application of these methods for early fault detection and diagnostics in large-scale battery systems, supported by case studies and real-world deployment examples. The findings of this review provide a unified perspective to guide the development of robust and scalable spatio-temporal fault detection methods for EV batteries, highlighting key challenges, promising solutions, and future research directions. Full article

China is developing renewable energy bases (REBs) in the desert and Gobi regions. However, the intermittency of renewable energy and the temporal mismatch between peak renewable generation and peak load demand severely disrupt the power supply reliability of these REBs. Hydrogen storage technology, characterized by high energy density and long-term storage capability, is an effective method for enhancing the power supply reliability. Therefore, this paper proposes a REB planning model in the desert and Gobi regions considering seasonal hydrogen storage introduction as well as electricity-carbon-hydrogen markets trading. Furthermore, a combination scenario generation method considering extreme scenario optimization is proposed. Among which, the extreme scenarios selected through an iterative selection method based on maximizing scenario divergence contain more incremental information, providing data support for the proposed model. Finally, the simulation was conducted in the desert and Gobi regions of Yinchuan, Ningxia Province, China, primarily verifying that (1) the REB incorporating hydrogen storage can fully leverage hydrogen storage to achieve seasonal and long-term electricity transfer and utilization. The project has a payback period of 10 years, with an internal rate of return of 13.30% and a return on investment of 16.34%, thus showing significant development potential. (2) Compared to the typical battery-involved REB, the hydrogen-involved energy storage facility achieved a 59.39% annual profit, a 10.98% internal rate of return, a 14.93% return on investment, and a 1.51% improvement in power supply reliability by sacrificing a 52.49% increase in construction cost. (3) Compared to REB planning based only on typical scenarios, the power supply reliability of REBs based on the proposed combination scenario generation method improved by 8.58%. Full article

The development of large-scale energy storage technologies is a key element in the transition to sustainable energy systems, where redox flow batteries (RFBs) are emerging as a promising alternative to conventional systems. The available literature reveals a notable lack of systematic studies evaluating the impact of membranes on the performance of IL-incorporating RFBs, despite this component being crucial for regulating ionic conductivity, minimizing the crossover of active species, and ensuring the operational stability of the system. This review provides a critical analysis of 81 articles published between 2015 and 2025, examining the impact of various membrane types on key parameters including conductivity, thermal and mechanical stability, energy efficiency, and power output. The findings reveal that more than 70% of the reviewed studies do not directly address the function of the membrane, underscoring the need for research focused on designing selective and robust materials for non-aqueous conditions. Finally, knowledge gaps are identified, and development prospects are proposed, along with the standardization of characterization protocols, to accelerate the practical implementation of IL-based RFBs in various scenarios. Full article

This paper proposes a novel AC side series arc fault (SAF) identification method based on Generalized Lissajous Trajectory (GLT) learning for low-voltage residential circuits. The method addresses challenges in detecting SAFs—characterized by high concealment, random occurrence, and limitations in existing protection devices—by leveraging the Hilbert transform to map current signals into 2D Generalized Lissajous Trajectories. These trajectories amplify key SAF features (e.g., zero-break distortion and random pulses). A ResNet50-based image recognition model achieves high-precision fault detection under specific load types, with a validation accuracy of up to 99.91% for linear loads and 98.93% for nonlinear loads. The algorithm operates within 1.6 ms, enabling real-time circuit breaker tripping. The proposed method achieves higher recognition accuracy with lower computational cost compared to other image-based methods. In this paper, an adjustable load signal modeling approach is proposed to visualize the current signal using GLT and complete the lightweight identification based on ResNet network, which provides new ideas and methods for series arc fault detection. Full article

The rapid increase in retired lithium-ion batteries (LIBs) from electric vehicles (EVs) highlights the urgent need for accurate and automated end-of-life (EOL) assessment. This study proposes an AI-integrated smart grading system that combines hardware diagnostics and deep learning-based evaluation to classify the residual usability of retired batteries. The system incorporates a bidirectional charger/discharger, a CAN-enabled battery management system (BMS), and a GUI-based human–machine interface (HMI) for synchronized real-time data acquisition and control. Four diagnostic indicators—State of Health (SOH), Direct Current Internal Resistance (DCIR), temperature deviation, and voltage deviation—are processed through a deep neural network (DNN) that outputs categorical grades (A: reusable, B: repurposable, C: recyclable). Experimental validation shows that the proposed AI-assisted model improves grading accuracy by 18% and reduces total testing time by 30% compared to rule-based methods. The integration of adaptive correction models further enhances robustness under varying thermal and aging conditions. Overall, this system provides a scalable framework for automated, explainable, and sustainable battery reuse and recycling, contributing to the circular economy of energy storage. Full article

Modern electrical vehicles (EVs) are equipped with sizable batteries that possess significant potential as energy prosumers. EVs are poised to be transformative assets and pivotal contributors to the virtual power plant (VPP), enhancing the performance and profitability of VPPs. The number of household EVs is increasing yearly, and this poses new challenges to the optimization of VPP operations. The computational cost increases exponentially as the number of decision variables rises with the increasing participation of EVs. This paper explores the role of a large number of EVs as prosumers, interacting with a VPP consisting of a photovoltaic system and battery energy storage system. To accommodate the large quantity of EVs in the modeling, this research adopts the decentralized control structure. It optimizes EV operations by regulating their charging and discharging behavior in response to pricing signals from the VPP. A two-stage optimization framework is proposed for VPP-EV operation using a reinforcement algorithm and gradient-based programming. Action masking for reinforcement learning is explored to eliminate invalid actions, reducing ineffective exploration, thereby accelerating the convergence of the algorithm. The proposed approach is capable of handling a substantial number of EVs and addressing the stochastic characteristics of EV charging and discharging behaviors. Simulation results demonstrate that the VPP-EV operation optimization increases the revenue of the VPP and significantly reduces the electricity costs for EV owners. Through the optimization of EV operations, the charging cost of 1000 EVs participating in the V2G services is reduced by 26.38% compared to those that opt out of the scheme, and VPP revenue increases by 27.83% accordingly. Full article

The storage of different forms of energy is becoming increasingly important in the energy system sector, due to the significant fluctuations that renewable energy sources influence on urban energy systems. Nowadays, these sources have been promoted for the transition towards modern energy systems at different scales, due to their reduced emissions of greenhouse gases. Yet, many doubts remain about their efficacy in urban settlements worldwide. For this reason, to promote the fast implementation of renewable energy technologies around the world, it is of great importance to design and develop free-access and user-friendly tools to help stakeholders in the planning and management of urban energy districts. The present study has proposed an evaluation tool to model decentralised energy storage systems using Urban Building Energy Models, including an optimisation method to size the best capacity in each building of a district. The developed models simulate two storage technologies: battery power banks and heated water tanks. To present the outcomes of the tool, these models have been tested in two scenarios of Portugal, located in a densely populated area and the most isolated region of the country. Among the most important findings of the results are their ability to evaluate the performance of individual buildings by group archetype and total district metrics, using different temporal periods in a single model to identify the buildings taking most advantage of storage technologies. In addition, the optimisation algorithm efficiently estimated the ideal size of each storage technology, reducing the need of unnecessary capacity. Full article

The elevated needs for high-performance energy storage, dictated by electrification, renewable sources integration, and the global increase in interconnected devices, have placed batteries to the forefront of technological research. Additive manufacturing is increasingly recognized as a compelling approach to advance battery research and application by enabling tailored control over design, pore geometry, materials, and integration. This perspective work examines the opportunities and challenges associated with utilizing additive manufacturing as an enabling battery manufacturing technology. Recent advances in the additive fabrication of electrodes, electrolytes, separators, and integrated devices are examined, exhibiting the potential to acheive electrochemical performance, design adaptability, and sustainability. At the same time, key challenges—including materials formulation, reproducibility, economic feasibility, and regulatory uncertainty—are discussed as limiting factors that must be addressed for achieving the expected results. Rather than being viewed as a replacement for conventional gigafactory-scale production, additive manufacturing is positioned as a complementary fabrication technique that can deliver value in niche, distributed, and application-specific contexts. This work concludes by outlining research and policy priorities that could accelerate the maturation of 3D-printed batteries, stressing the importance of hybrid manufacturing, multifunctional printable materials, circular economy integration, and carefully phased timelines for deployment. Moreover, by enabling customized form factors, improved device–user interfaces, and seamless integration into smart, automated environments, additive manufacturing has the potential to significantly enhance user experience across emerging battery applications. In this context, this perspective provides a grounded assessment of how additive fabrication methods may contribute to next-generation battery technologies that not only improve electrochemical performance but also enhance user interaction, reliability, and seamless integration within automated and control-driven systems. Full article

The separable heat pipe, with its highly efficient heat transfer and flexible layout features, has become an innovative solution to the heat dissipation problem of batteries, especially suitable for the directional heat dissipation requirements of high-energy-density battery packs. However, most of the number–value models currently studied examine the flow of refrigerant working medium within the pump as an isentropic or isothermal process and are unable to effectively analyze the heat transfer characteristics of different internal regions. Based on the laws of energy conservation, momentum conservation, and mass conservation, this study establishes a steady-state mathematical model of the pump-driven microchannel-separated heat pipe. The influence of factors—such as the phase state change in the working medium inside the heat exchanger, the heat transfer flow mechanism, the liquid filling rate, the temperature difference, as well as the structural parameters of the microchannel heat exchanger on the steady-state heat transfer and flow performance of the pump-driven microchannel-separated heat pipe—were analyzed. It was found that the influence of liquid filling ratio on heat transfer quantity is reflected in the ratio of change in the sensible heat transfer and latent heat transfer. The sensible heat transfer ratio is higher when the liquid filling is too low or too high, and the two-phase heat transfer is higher when the liquid filling ratio is in the optimal range; the maximum heat transfer quantity can reach 3.79 KW. The decrease in heat transfer coefficient with tube length in the single-phase region is due to temperature and inlet effect, and the decrease in heat transfer coefficient in the two-phase region is due to the change in flow pattern and heat transfer mechanism. This technology has the advantages of long-distance heat transfer, which can adapt to the distributed heat dissipation needs of large-energy-storage power plants and help reduce the overall lifecycle cost. Full article

The maritime industry is under increasing pressure to reduce greenhouse gas emissions, especially in countries such as Saudi Arabia that are actively working to transition to cleaner energy. In this paper, a new hybrid shipboard power system, which incorporates wind turbines, solar photovoltaic (PV) panels, proton-exchange membrane fuel cells (PEMFCs), and a battery energy storage system (BESS) together for propulsion and hotel load services, is proposed. A multi-loop Energy Management System (EMS) based on proportional–integral control (PI) is developed to coordinate the interconnections of the power sources in real time. In contrast to the widely reported model predictive or artificial intelligence optimization schemes, the PI-derived EMS achieves similar power stability and hydrogen utilization efficiency with significantly reduced computational overhead and full marine suitability. By taking advantage of the high solar irradiance and coastal wind resources in Saudi Arabia, the proposed configuration provides continuous near-zero-emission operation. Simulation results show that the PEMFC accounts for about 90% of the total energy demand, the BESS (±0.4 MW, 2 MWh) accounts for about 3%, and the stationary renewables account for about 7%, which reduces the demand for hydro-gas to about 160 kg. The DC-bus voltage is kept within ±5% of its nominal value of 750 V, and the battery state of charge (SOC) is kept within 20% to 80%. Sensitivity analyses show that by varying renewable input by ±20%, diesel consumption is ±5%. These results demonstrate the system’s ability to meet International Maritime Organization (IMO) emission targets by delivering stable near-zero-emission operation, while achieving high hydrogen efficiency and grid stability with minimal computational cost. Consequently, the proposed system presents a realistic, certifiable, and regionally optimized roadmap for next-generation hybrid PEMFC–battery–renewable marine power systems in Saudi Arabian coastal operations. Full article

In this research article, a silicon carbide (SiC) nanocluster has been designed and characterized as an anode electrode for lithium (Li), sodium (Na), potassium (K), beryllium (Be), magnesium (Mg), boron (B), aluminum (Al) and gallium (Ga)-ion batteries through the formation of SiLiC, SiNaC, SiKC, SiBeC, SiMgC, SiBC, SiAlC and SiGaC nanoclusters. A vast study on energy-saving by SiLiC, SiNaC, SiKC, SiBeC, SiMgC, SiBC, SiAlC and SiGaC complexes was probed using computational approaches accompanying density state analysis of charge density differences (CDDs), total density of states (TDOS) and molecular electrostatic potential (ESP) for hybrid clusters of SiLiC, SiNaC, SiKC, SiBeC, SiMgC, SiBC, SiAlC and SiGaC. The functionalization of Li, Na, K, Be, Mg, B, Al and Ga metal/metalloid elements can raise the negative charge distribution of carbon elements as electron acceptors in SiLiC, SiNaC, SiKC, SiBeC, SiMgC, SiBC, SiAlC and SiGaC nanoclusters. Higher Si/C content can increase battery capacity through SiLiC, SiNaC, SiKC, SiBeC, SiMgC, SiBC, SiAlC and SiGaC nanoclusters for energy storage processes and to improve the rate performance by enhancing electrical conductivity. Full article

Sodium-ion batteries (SIB) are gaining attention as a sustainable, cost-effective alternative to lithium-ion technology in electric vehicles (EVs), driven by concerns over lithium’s scarcity, high costs, and environmental impact. This study explores the feasibility of SIBs through a theoretical analysis of recent advancements in chemistry, materials, and electrochemical performance. It compares key factors such as energy density, charge cycles, safety, cost-effectiveness, and supply chain sustainability. While sodium-ion batteries currently offer lower energy density and shorter cycle life, they benefit from abundant raw materials and more sustainable production. Recent breakthroughs in electrode and electrolyte design show promise for improved efficiency and longevity. Sodium-ion technology is not yet a full replacement for Li-ion batteries but presents a viable option for low-cost EVs and stationary storage. Full article