Design and fabrication of electrochemical energy storage systems with both high energy and power densities as well as long cycling life is of great importance. As one of these systems, Battery‐supercapacitor hybrid device (BSH) is typically constructed with a high‐capacity battery‐type electrode and a high‐rate capacitive electrode, which has attracted enormous attention due to its potential applications in future electric vehicles, smart electric grids, and even miniaturized electronic/optoelectronic devices, etc. With proper design, BSH will provide unique advantages such as high performance, cheapness, safety, and environmental friendliness. This review first addresses the fundamental scientific principle, structure, and possible classification of BSHs, and then reviews the recent advances on various existing and emerging BSHs such as Li‐/Na‐ion BSHs, acidic/alkaline BSHs, BSH with redox electrolytes, and BSH with pseudocapacitive electrode, with the focus on materials and electrochemical performances. Furthermore, recent progresses in BSH devices with specific functionalities of flexibility and transparency, etc. will be highlighted. Finally, the future developing trends and directions as well as the challenges will also be discussed; especially, two conceptual BSHs with aqueous high voltage window and integrated 3D electrode/electrolyte architecture will be proposed.
Ephrem Chemali, P. Kollmeyer, Matthias Preindl
et al.
Abstract Accurate State of Charge (SOC) estimation is crucial to ensure the safe and reliable operation of Li-ion batteries, which are increasingly being used in Electric Vehicles (EV), grid-tied load-leveling applications as well as manned and unmanned aerial vehicles to name a few applications. In this paper, a novel approach using Deep Feedforward Neural Networks (DNN) is used for battery SOC estimation where battery measurements are directly mapped to SOC. Training data is generated in the lab by applying drive cycle loads at various ambient temperatures to a Li-ion battery so that the battery is exposed to variable dynamics. The DNN's ability to encode the dependencies in time into the network weights and in the process provide accurate estimates of SOC is presented. Moreover, data recorded at ambient temperatures lying between −20 °C and 25 °C are fed into the DNN during training. Once trained, this single DNN is able to estimate SOC at various ambient temperature conditions. The DNN is validated over many different datasets and achieves a Mean Absolute Error (MAE) of 1.10% over a 25 °C dataset as well as an MAE of 2.17% over a −20 °C dataset.
Abstract Nanogenerators (NGs) are a field that uses Maxwell's displacement current as the driving force for effectively converting mechanical energy into electric power/signal, which have broad applications in energy science, environmental protection, wearable electronics, self-powered sensors, medical science, robotics and artificial intelligence. NGs are usually based on three effects: piezoelectricity, triboelectricity (contact electrification), and pyroelectricity. In this paper, a formal theory for NGs is presented starting from Maxwell's equations. Besides the general expression for displacement vector D = eE used for deriving classical electromagnetic dynamics, we added an additional term Ps in D, which represents the polarization created by the electrostatic surface charges owing to piezoelectricity and/or triboelectricity as a result of mechanical triggering in NG. In contrast to P that is resulted from the electric field induced medium polarization and vanishes if E = 0, Ps remains even when there is no external electric field. We reformulated the Maxwell equations that include both the medium polarizations due to electric field (P) and non-electric field (such as strain) (Ps) induced polarization terms, from which, the output power, electromagnetic behavior and current transport equation for a NG are systematically derived. A general solution is presented for the modified Maxwell equations, and analytical solutions about the output potential are provided for a few cases. The displacement current arising from e∂E/∂t is responsible for the electromagnetic waves, while the newly added term ∂Ps/∂t is the application of Maxwell's equations in energy and sensors. This work sets the first principle theory for quantifying the performance and electromagnetic behavior of a nanogenerator in general.
Daniel Ospina, Paula Mirazo, Richard P. Allan
et al.
Abstract
Non-Technical Summary
This review highlights 10 recent advances in climate change research with high policy relevance, spanning diverse topics: (1) the global temperature jump of 2023–2024; (2) sea surface warming and marine heatwaves; (3) land carbon sinks; (4) interactions between climate change and biodiversity loss; (5) accelerated groundwater decline; (6) global dengue incidence; (7) income and labour productivity loss; (8) strategic considerations for scaling carbon dioxide removal (CDR); (9) integrity of carbon credit markets; and (10) policy mixes for climate change mitigation.
Technical Summary
Interdisciplinary understanding is vital for delivering sound climate policy advice. However, navigating the ever-growing and increasingly diverse scholarly literature on climate change is challenging for any individual researcher. This annual synthesis highlights and explains recent advances across a variety of fields of climate change research. This year, the 10 insights focus on: (1) the record-warmth of 2023/2024 and the elevated Earth energy imbalance; (2) acceleration of ocean warming and intensifying marine heatwaves; (3) northern land carbon sinks under strain; (4) reinforcing feedback between biodiversity loss and climate change; (5) accelerated depletion of groundwater; (6) global dengue incidence; (7) global income losses and labour productivity declines; (8) strategic scaling of CDR; (9) integrity challenges in carbon credit markets and emerging responses; and (10) effective policy mixes for emissions reductions. The insights have been written to be accessible to researchers from different fields, serving as entry-points to specific topics, as well as providing an overview of the evolving landscape of climate change research. In the final section, the insights are used to develop overarching policy-relevant messages. This paper provides the basis for a science-policy report that was shared with all Party delegations ahead of COP30 in Belém, Brazil.
Social Media Summary
Highlights of climate change research in 2024–2025: 10insightsclimate.science
Samuel Amaro, João F.P. Fernandes, P.J. Costa Branco
Pumps as Turbines (PATs) provide efficient, decentralized energy generation for remote off-grid areas, typically coupled with induction generators (IGs) for cost-effective robustness. However, using typical capacitors for their excitation limits performance under variable hydraulic and electrical conditions. This study explores field-oriented control (FOC)-based control algorithms to replace capacitor banks and improve the performance of the off-grid PAT-IG system under varying hydraulic conditions. Our main contributions are focused on analyzing system efficiency, along with the electromagnetic behavior of the induction generator and the hydraulic-mechanical dynamics of the PAT, under torque- and mechanical-power-controlled FOC strategies. In addition, flux control has been shown to be critical for maximizing the system’s efficiency. The analysis of the off-grid PAT-IG system is evaluated using dynamic non-linear simulations. The simulation results revealed that FOC techniques with optimal flux control allow for maximizing the efficiency of the system for a wide range of hydraulic and electric variables. With capacitors, the PAT-IG shows a highly variable efficiency with a maximum value of around 40% and several zones where the machine is not capable of operating. However, using torque and mechanical power FOC with flux optimization, the system is capable of operating in a wider range with very stable efficiency for different operational points. However, only mechanical power-controlled FOC can achieve almost constant electric power generation in the presence of hydraulic pressure variation. Therefore, from an electric energy generation point of view, the latter technique should be chosen instead of torque-controlled FOC. These findings highlight the need for advanced control strategies in off-grid PAT-IG systems and support their viability as a sustainable component in off-grid applications.
Little Pradhan, Abhijit Kshirsagar, D. Venkatramanan
et al.
Cascaded H-bridge (CHB) multilevel inverters are well-suited for medium-voltage charging stations because of their inherent modularity, scalability, and efficient voltage conversion capability. However, conventional level-shifted PWM (LSPWM) schemes often lead to uneven distribution of active and reactive power among individual modules. This imbalance produces nonuniform semiconductor losses, increases thermal stress, and accelerates premature failures in overstressed modules. Alternative methods, such as space-vector modulation and switching-angle adjustment, can mitigate these issues, but their computational complexity becomes prohibitive for higher-level CHB topologies. Carrier-reassignment PWM strategies, including First-In-First-Out (FIFO), provide simpler implementations but still fail to achieve complete power and loss balancing. This article contributes to the state-of-the-art in two key ways. First, it extends carrier-reassignment PWM, previously demonstrated only for 9-level CHBs, to a 17-level CHB inverter, introducing two new reassignment strategies: Type-A and Type-B. The Type-A scheme enables highly uniform real-power sharing under a unity power factor (PF). At the same time, the Type-B approach achieves balanced loss distribution across the full PF range and effectively eliminates circulating power at zero PF, surpassing existing rotation-based methods. Second, the article proposes a comprehensive validation framework that integrates analytical loss modeling of CoolSiC™ devices with hardware-in-the-loop (HIL) experiments, employing an OP4510 digital simulator and a PED-Board controller. Experimental results confirm that the proposed schemes substantially enhance both power and loss distribution, while also reducing current total harmonic distortion compared to conventional approaches. Overall, the proposed methods provide a practical pathway toward more reliable and efficient CHB converters for electric vehicle charging and medium-voltage applications.
ObjectivesWhen a virtual synchronous generator (VSG) is connected to a medium and low voltage distribution network, the line impedance exhibits resistive and inductive characteristics, resulting in strong coupling in the VSG output power. This coupling can cause power oscillations and even deviations in the internal power control, affecting power quality and transmission stability. To address these issues, an improved power decoupling strategy based on virtual capacitance is proposed.MethodsBy simplifying the VSG grid-connected model and conducting small-signal modeling, the power coupling mechanism is analyzed to identify key factors contributing to coupling. Based on this analysis, the concept of virtual capacitance is introduced, and an improved control method utilizing virtual capacitance is proposed. Finally, simulations are conducted on the MATLAB/Simulink platform to verify the effectiveness of the proposed strategy.ResultsThe capacitive characteristics of virtual capacitance not only enhance the system’s operational stability, but also correct the output voltage reference, effectively mitigating oscillations exacerbated by power coupling.ConclusionsThe proposed control strategy reduces the interference caused by active power command changes or load switching on reactive power, suppressing strong power coupling during the dynamic process of the system.
Applications of electric power, Production of electric energy or power. Powerplants. Central stations
Abstract Accurate and efficient calculation of a transformer's magnetic field is fundamental for the rapid calculation of its losses, temperature rise, and structural forces. However, existing numerical methods for calculating the harmonic magnetic field of a product‐level transformer are time‐consuming and fail to meet the rapid requirements of digital operations and maintenance. To address this, this paper first utilises the harmonic field method to obtain the snapshot matrix of the transformer's magnetic field. Subsequently, a response surface model of the magnetic field is constructed using intrinsic quadrature theory and radial basis functions in the augmented form. To enhance the efficiency of constructing the reduced‐order model, an adaptive Latin hypercube sampling method, integrating the additive rule and leave‐one‐out cross‐validation, is introduced, significantly improving the efficiency of sample space construction. The effectiveness of the proposed method is validated by applying the proper orthogonal decomposition‐radial basis function including linear polynomial (POD‐RBFLP) method to calculate the harmonic magnetic field of a three‐phase power transformer in reduced order. The results are compared with those from COMSOL calculations, showing that the reduced‐order model maintains the calculation error within a reasonable range, thereby confirming the accuracy of the proposed method. Additionally, the reduced‐order model demonstrates a significant advantage in computation time compared to COMSOL simulations, enabling the calculation of the transformer's magnetic field in seconds.
ABSTRACT To address the challenges in real‐time 3D temperature field analysis for intelligent power systems, we propose a fast calculation method based on point cloud U‐net++ neural network. Taking a 35 kV oil‐immersed transformer as an example, initially, we input key temperature‐influencing factors into our algorithm. These input features are randomly combined in a limited range according to a specific step. The sets of 3D temperature are computed by Fluent on the Jinan Shanhe supercomputing platform. And the three‐dimensional mathematical model is then converted into point clouds. Finally, we determined the optimal hyperparameters and proceeded with parameter training, evaluation and debugging. The results demonstrate that the method proposed can reduce single calculation time to 0.04 s with the vast majority of the error in the region of 0K or so, significantly improving the efficiency of the calculation. Meanwhile, the U‐net++ neural network also achieves significantly higher accuracy than the U‐net network. To validate the algorithm's effectiveness, we establish a platform for assessing the temperature increase. The experimental results indicate that the temperature rise trend from U‐net++ neural network calculations aligns closely with the experimental data, and the temperature difference is within only 4K.
Juha J. Pyrhonen, Ilya Petrov, Daniil Zadorozhniuk
et al.
In electric vehicle (EV) traction, energy conversion by electric motors must be material and energy efficient to reduce environmental burden caused by the manufacture and operation of the systems and to help in efficient transition towards net zero future. The scarcity of key materials like rare earths and to some extent copper must be addressed in the design. Novel traction motors need a different approach compared to traditional industrial motor designs. Here, we focus on innovative design and optimization strategies to enhance material and energy efficiency and meet the escalating demand for sustainable transportation solutions. We are developing a prototype motor that achieves a continuous specific power of 7 kW/kg, significantly exceeding current automotive standards. This will be achieved by elevated operational speed of the traction motor, integrating advanced materials, and innovative cooling techniques such as direct liquid cooling (DLC) using hollow hairpin conductors, and insulating them with polyether-ether-ketone (PEEK) extrusion and expandable mainwall insulation material. The approach reduces reliance on rare earth permanent magnet (PM) materials by 60% compared to existing motors, aligning with global sustainability objectives. Through simulation, modeling, and practical case studies, the research demonstrates the feasibility of these innovations in real-world applications, highlighting potential advancements in EV propulsion systems. The article not only represents the insights into the latest developments in the design of electric motors for EV but also delineates the journey towards the creation of such a motor with considering accompanying electromagnetic and mechanical challenges.
Abstract Currently, the digital transformation of the power grid is underway, and the intelligent health management technology for power transformers is rapidly advancing. However, there are issues in the operation and maintenance process, such as weak information correlation and low decision‐making efficiency. Knowledge graphs have been applied in other industrial fields, such as spacecraft maintenance, to significantly improve knowledge query efficiency. However, there is a lack of literature on knowledge graph construction in the field of power transformer operation and maintenance. Additionally, there is limited publicly available data and difficulties in effectively mining operation and maintenance knowledge in this field. A method for constructing a knowledge graph for power transformer operation and maintenance based on ALBERT is proposed. Firstly, publicly available literature in the field of power transformers is collected, and a sample enhancement method using regular matching is used to enrich the semi‐structured corpora, such as power system accident investigation reports, to construct a training dataset for power transformer operation and maintenance. Then, the ALBERT‐BiLSTM‐CRF deep learning algorithm is applied to extract power transformer operation and maintenance entities from the relevant literature and accident investigation reports, and this method is compared with traditional deep learning algorithms to demonstrate its superiority. Subsequently, the ALBERT‐BiLSTM‐Attention deep learning algorithm, which incorporates ALBERT and attention mechanism, is utilised to extract relationships between power transformer operation and maintenance entities. Compared to other deep learning algorithms, this algorithm demonstrates better performance in the domain‐specific texts of power transformer operation and maintenance. Finally, the Neo4j graph database is used to visualise and present the knowledge graph, enabling decision support based on the power transformer operation and maintenance knowledge graph.
Angel Recalde, Ricardo Cajo, Washington Velasquez
et al.
This paper provides a comprehensive review of machine learning strategies and optimization formulations employed in energy management systems (EMS) tailored for plug-in hybrid electric vehicles (PHEVs). EMS stands as a pivotal component facilitating optimized power distribution, predictive and adaptive control strategies, component health monitoring, and energy harvesting, thereby enabling the maximal exploitation of resources through optimal operation. Recent advancements have introduced innovative solutions such as Model Predictive Control (MPC), machine learning-based techniques, real-time optimization algorithms, hybrid optimization approaches, and the integration of fuzzy logic with neural networks, significantly enhancing the efficiency and performance of EMS. Additionally, multi-objective optimization, stochastic and robust optimization methods, and emerging quantum computing approaches are pushing the boundaries of EMS capabilities. Remarkable advancements have been made in data-driven modeling, decision-making, and real-time adjustments, propelling machine learning and optimization to the forefront of enhanced control systems for vehicular applications. However, despite these strides, there remain unexplored research avenues and challenges awaiting investigation. This review synthesizes existing knowledge, identifies gaps, and underscores the importance of continued inquiry to address unanswered research questions, thereby propelling the field toward further advancements in PHEV EMS design and implementation.
The world is grappling with dual crises of energy depletion and environmental degradation, as escalating global energy demands strain the sustainability of existing systems. While traditional energy harvesting technologies such as wind, solar, and hydropower have progressed, challenges in energy storage and system stability persist, underscoring the urgent need for more efficient and sustainable alternatives. Emerging water-based energy harvesting technologies that harness the dynamic regulation of electrical double layers (EDLs) at solid-liquid interfaces offer significant advantages, including enhanced energy conversion efficiency and flexible application potential. These systems are particularly well-suited to meet the growing demand for distributed energy in the Internet of Things (IoT), where adaptable and scalable energy solutions are essential. Key nanogenerator technologies utilizing dynamic EDL regulation are classified into five major types: solid-liquid triboelectric nanogenerators (S-L TENGs), triboiontronic nanogenerators (TINGs), hydrovoltaic technology, moisture-enabled electric generators (MEGs), and osmotic power sources. This review provides a comprehensive analysis of their operating principles, output characteristics, and typical applications. Furthermore, it addresses the main challenges and bottlenecks these technologies face and outlines future research and development opportunities, advancing the field of water-based energy harvesting.
Off-grid renewable power to ammonia (ReP2A) systems present a promising pathway toward carbon neutrality in both the energy and chemical industries. However, due to chemical safety requirements, the limited flexibility of ammonia synthesis poses a challenge when attempting to align with the variable hydrogen flow produced from renewable power. This necessitates the optimal sizing of equipment capacity for effective and coordinated production across the system. Additionally, an ReP2A system may involve multiple stakeholders with varying degrees of operational flexibility, complicating the planning problem. This paper first examines the multistakeholder sizing equilibrium (MSSE) of the ReP2A system. First, we propose an MSSE model that accounts for individual planning decisions and the competing economic interests of the stakeholders of power generation, hydrogen production, and ammonia synthesis. We then construct an equivalent optimization problem based on Karush-Kuhn-Tucker (KKT) conditions to determine the equilibrium. Following this, we decompose the problem in the temporal dimension and solve it via multicut generalized Benders decomposition (GBD) to address long-term balancing issues. Case studies based on a realistic project reveal that the equilibrium does not naturally balance the interests of all stakeholders due to their heterogeneous characteristics. Our findings suggest that benefit transfer or re-arrangement ensure mutual benefits and the successful implementation of ReP2A projects.
The operating point of a power system may change due to slow enough variations of the power injections. Rotating machines in the bulk system can absorb smooth changes in the dynamic states of the system. In this context, we present a novel reservoir computing (RC) method for estimating power system quasi-steady states. By exploiting the behavior of an RC-based recurrent neural network, the proposed method can capture the inherent nonlinearities in the power flow equations. Our approach is compared with traditional methods, including least squares, Kalman filtering, and particle filtering. We demonstrate the estimation performance for all the methods under normal operation and sudden load change. Extensive experiments tested on the standard IEEE 14-bus and 300-bus cases corroborate the merit of the proposed approach.