Superconducting technology applications in electric machines have long been pursued due to their significant advantages of higher efficiency and power density over conventional technology. However, in spite of many successful technology demonstrations, commercial adoption has been slow, presumably because the threshold for value versus cost and technology risk has not yet been crossed. One likely path for disruptive superconducting technology in commercial products could be in applications where its advantages become key enablers for systems which are not practical with conventional technology. To help systems engineers assess the viability of such future solutions, we present a technology roadmap for superconducting machines. The timeline considered was ten years to attain a Technology Readiness Level of 6+, with systems demonstrated in a relevant environment. Future projections, by definition, are based on the judgment of specialists, and can be subjective. Attempts have been made to obtain input from a broad set of organizations for an inclusive opinion. This document was generated through a series of teleconferences and in-person meetings, including meetings at the 2015 IEEE PES General meeting in Denver, CO, the 2015 ECCE in Montreal, Canada, and a final workshop in April 2016 at the University of Illinois, Urbana-Champaign that brought together a broad group of technical experts spanning the industry, government and academia.
Summary Superconducting materials hold great potential to bring radical changes for electric power and high-field magnet technology, enabling high-efficiency electric power generation, high-capacity loss-less electric power transmission, small lightweight electrical equipment, high-speed maglev transportation, ultra-strong magnetic field generation for high-resolution magnetic resonance imaging (MRI) systems, nuclear magnetic resonance (NMR) systems, future advanced high energy particle accelerators, nuclear fusion reactors, and so on. The performance, economy, and operating parameters (temperatures and magnetic fields) of these applications strongly depend on the electromagnetic and mechanical properties, as well as the manufacturing and material cost of superconductors. This perspective examines the basic properties relevant to practical applications and key issues of wire fabrication for practical superconducting materials, and describes their challenges and current state in practical applications. Finally, future perspectives for their opportunities and development in the applications of superconducting power and magnetic technologies are considered.
With its remarkable electro-thermal properties such as the highest known thermal conductivity (~22 W cm−1∙K−1 at RT of any material, high hole mobility (>2000 cm2 V−1 s−1), high critical electric field (>10 MV cm−1), and large band gap (5.47 eV), diamond has overwhelming advantages over silicon and other wide bandgap semiconductors (WBGs) for ultra-high-voltage and high-temperature (HT) applications (>3 kV and >450 K, respectively). However, despite their tremendous potential, fabricated devices based on this material have not yet delivered the expected high performance. The main reason behind this is the absence of shallow donor and acceptor species. The second reason is the lack of consistent physical models and design approaches specific to diamond-based devices that could significantly accelerate their development. The third reason is that the best performances of diamond devices are expected only when the highest electric field in reverse bias can be achieved, something that has not been widely obtained yet. In this context, HT operation and unique device structures based on the two-dimensional hole gas (2DHG) formation represent two alternatives that could alleviate the issue of the incomplete ionization of dopant species. Nevertheless, ultra-HT operations and device parallelization could result in severe thermal management issues and affect the overall stability and long-term reliability. In addition, problems connected to the reproducibility and long-term stability of 2DHG-based devices still need to be resolved. This review paper aims at addressing these issues by providing the power device research community with a detailed set of physical models, device designs and challenges associated with all the aspects of the diamond power device value chain, from the definition of figures of merit, the material growth and processing conditions, to packaging solutions and targeted applications. Finally, the paper will conclude with suggestions on how to design power converters with diamond devices and will provide the roadmap of diamond device development for power electronics.
Copper–aluminum layered composites offer a promising combination of high conductivity, light weight, and cost-effectiveness, making them attractive for applications in electric vehicles, electronics, and power transmission. However, achieving reliable interfacial bonding while avoiding excessive work hardening and brittle intermetallic formation remains a significant challenge. In this study, a Cu18150/Al1060/Cu18150 trilayer composite was fabricated through a three-stage high-temperature oxygen-free rolling process. Subsequently, the produced composite was subjected to annealing treatments to systematically investigate the effects of rolling passes, annealing temperature/time on interfacial evolution and mechanical behavior. Results indicate that rolling passes primarily influence interfacial topography and defect distribution. Fewer passes lead to wavy, mechanically bonded interfaces, while more passes improve flatness but reduce intermetallic continuity. Annealing temperature critically governs diffusion kinetics; temperatures up to 400 °C promote the formation of a uniform Al<sub>2</sub>Cu layer, whereas 450 °C accelerates the growth of brittle Al<sub>4</sub>Cu<sub>9</sub>, thickening the intermetallic layer to 18 μm and compromising toughness. Annealing duration further modulates diffusion mechanisms, with short-term (0.5 h) treatments favoring defect-assisted diffusion, resulting in a porous, rapidly thickened layer. In contrast, longer annealing (≥1 h) shifts toward lattice diffusion, which densifies the interface but risks excessive brittle phase formation if prolonged. Mechanical performance evolves accordingly; as-rolled strength increases with the number of rolling passes, but at the expense of ductility. Annealing transforms bonding from a mechanical to a metallurgical condition, shifting fracture from delamination to collaborative failure. The identified optimal process, single-pass rolling followed by annealing at 420 °C for 1 h, yields a balanced interfacial structure of Al<sub>2</sub>Cu, AlCu, and Al<sub>4</sub>Cu<sub>9</sub> phases, achieving a tensile strength of 258.9 MPa and an elongation of 28.2%, thereby satisfying the target performance criteria (≥220 MPa and ≥20%).
Shreeram V. Kulkarni, Sandeep Gupta, G. Arjun
et al.
Abstract Accurate estimation of SOC is crucial to ensure electric vehicle (EV) battery safety, reliability, and performance. Conventional Coulomb Counting is one of the most widely used methods due to its simplicity and real-time capability. However, the method’s accuracy deteriorates over the long term of operation, due to numerical integration errors, sensor noise, and unmodeled capacity degradation. This paper proposes a trapezoidal numerical integration-based SOC estimation technique that compensates for capacity fade to lower cumulative drift at a relatively low cost. To assess and compare the conventional rectangular method of Euler with the proposed method, a synthetic drive cycle of 240 min with a class interval for discharge and regenerative charging phases has been simulated. Reference SOC is obtained from ideal integration; the error metrics and performance indicators, including MAE, SOC drift, and ΔSOC distributions, are evaluated. The improved method consistently exhibits lower drift and error spread, especially during current polarity transitions. The proposed advancement provides a lightweight alternative to heavy model-based estimators and shows suitability for low-power battery management system applications in real EVs.
Remaining useful lifetime prediction and extension of Si power devices have been studied extensively. Silicon carbide (SiC) power devices have been developed and commercialized. Specifically, SiC mosfets have been utilized for the next generation high-voltage, high-power converters with smaller size and higher efficiency, covering various mainstream applications, including photovoltaic systems, electric vehicles, solid-state transformers, and more electric ships and airplanes. However, the SiC-based devices have different failure modes and mechanisms compared with Si counterparts. Therefore, a comprehensive review is critical to develop accurate lifetime prediction and extension strategies for SiC power converter systems. The SiC power device component-level failure modes and mechanisms are first investigated. Different accelerated lifetime tests and component-level lifetime models are then compared. Power converter system-level offline lifetime modeling techniques and software tools are further summarized. Besides, the SiC power converter condition monitoring strategies and health indicators are surveyed. The online measurement challenges are also studied. Furthermore, the system-level lifetime extension strategies are reviewed. By integrating device physics, statistical modeling, reliability engineering, and mechanical engineering with power electronics, this article is intended to provide a comprehensive overview, address existing challenges, and unfold new research opportunities regarding the SiC power converter real-time lifetime prediction and extension.
Modern civilization is related to the increased use of electric energy for industry production, human mobility, and comfortable living. Highly efficient and reliable power electronic systems, which convert and process electric energy from one form to the other, are critical for smart grid and renewable energy systems. The power semiconductor device, as the cornerstone technology in a power electronics system, plays a pivotal role in determining the system efficiency, size, and cost. Starting from the invention and commercialization of silicon bipolar junction transistor 60 years ago, a whole array of silicon power semiconductor devices have been developed and commercialized. These devices enable power electronics systems to reach ultrahigh efficiency and high-power capacity needed for various smart grid and renewable energy system applications such as photovoltaic (PV), wind, energy storage, electric vehicle (EV), flexible ac transmission system (FACTS), and high voltage dc (HVDC) transmission. In the last two decades, newer generations of power semiconductor devices based on wide bandgap (WBG) materials, such as SiC and GaN, were developed and commercialized further pushing the boundary of power semiconductor devices to higher voltages, higher frequencies, and higher temperatures. This paper reviews some of the major power semiconductor devices technologies and their potential impacts and roadmaps.
A two-stage robust optimization model of power system considering source-load uncertainty is proposed，to address the serious lack of system scheduling flexibility caused by the source-load uncertainty in new energy power systems. According to the characteristics of source-load uncertainty，the K-means method and robust optimization theory are combined to quantify the flexibility demand of the power system at multiple time scales. Firstly，the robust dispatch model is established，and the flexible regulation potentials of thermal power units，pumped storage and other resources are fully exploited.The flexible transformation of thermal power units and pumped storage pumping status are included in the first stage of the model，and the output of the flexible resources is taken as the second stage of the decision variables. The optimization objective of the model is to minimize the cost of retrofitting，carbon emission and operating costs. The two-stage robust model is transformed into relatively independent main problems and sub-problems，and the column constraint generation （C&CG） algorithm and strong dyadic theory are adopted to iterate repeatedly to approximate the optimal solution. Finally，the proposed optimal scheduling strategy is verified through examples，so that the proposed optimal scheduling strategy can integrate all kinds of resources based on meeting the demand for flexibility，which achieve the balance of economy，environmental protection，and flexibility in the system，and improve the ability to resist the risk of uncertainty in the source load.
ABSTRACT In this paper, a novel six‐phase rotor‐permanent magnet axial field modular fault‐tolerant flux‐switching machine (RPM‐AFMFTFSM) is proposed. The separated stator core and rotor cells provide effective electromagnetic isolation for the armature windings, and this leads to enhanced fault‐tolerant operating capability. The segmented permanent magnet (PM) is integrated in the rotor, by which the magnetic saturation of the stator iron core is alleviated and the PM eddy current loss is reduced. The stator‐slots and rotor pole‐pairs (Ps/Pr) combination of the proposed machine is optimised, and the cogging torque is reduced. A comparative study between the RPM‐AFMFTFSM and conventional stator permanent magnet axial field flux‐switching machine (SPM‐AFFSM) is carried out by 3‐D finite element analysis (FEA) method. The advantage of the proposed machine with respect to the overload capability, flux‐weakening capacity and antidemagnetisation ability are revealed. The fault‐tolerant performance under single‐phase and two‐phase open‐circuited conditions is analysed. The full‐bridge inverters are employed on the six‐phase armature windings to achieve the reduced amplitude of the fault‐tolerant current and copper loss by adopting the round rotating magnetomotive force reconfiguration control strategy. Finally, a prototype of the RPM‐AFMFTFSM is manufactured and the FEA predicted results are validated by experimental measurements.
Abstract This paper proposes a novel asymmetric permanent magnet assisted synchronous reluctance motor (APMA‐SynRM) used for electric vehicles (EVs), which adopts the magnetic isolation flux barrier to enhance the effect of magnetic field shifting. Firstly, the magnetic field shifting principle and magnetic circuit model of APMA‐SynRM are analysed. Then, a new design method of APMA‐SynRM that makes the reluctance torque and permanent magnet (PM) torque reach the peak value at same current angle is proposed. According to the design method's analysis, the condition that the structural and magnetic property parameters of APMA‐SynRM should meet is obtained, which provides significant reference for the APMA‐SynRM's design. After that, an APMA‐SynRM scheme is designed to verify the design method's accuracy and effectiveness. Finally, the electromagnetic and mechanical strength performances of APMA‐SynRM and corresponding permanent magnet assisted synchronous reluctance motor (PMA‐SynRM) are studied, which demonstrates the feasibility of APMA‐SynRM's application in EVs.
During the operation of maglev trains approaching stations, the electric energy generated by the linear generators propels the trains but is insufficient to meet the power demands of onboard equipment. Traditional contact power supply methods have shown deficiencies in various aspects, such as high installation and maintenance costs, as well as the safety risks associated with exposed live conductors. In contrast, wireless power transfer (WPT) technology eliminates the need for physical cable connections, allowing maglev trains to operate without mechanical contact and enhancing the safety, economic efficiency, and environmental adaptability of their auxiliary power supply system. This paper focuses on the optimized design for the magnetic coupling mechanism and resonant compensation circuit, addressing specific requirements of auxiliary WPT systems in maglev train applications including high power demands and efficiency requirements. A finite element model of a magnetic coupling mechanism with a single-transmitter multiple-receiver (STMR) configuration was established. The operational characteristics of three resonant compensation topologies (S/S, LCC/LCC, LCC/S) for WPT systems were compared and analyzed. A global multi-objective optimization design strategy was introduced based on the concept ofPareto optimal solutions. Furthermore, an 8.5 kW auxiliary WPT system prototype was built for verification. The experimental results demonstrated the proposed optimization scheme in meeting the design requirements of WPT systems for maglev trains, with an energy transfer efficiency of up to 91.9%.
ObjectivesWith the continuous growth of demand-side response resources, traditional energy scheduling models struggle to meet the system requirements of high penetration levels of renewable energy. To achieve the rational allocation of multiple energy sources within a community, this study proposes an energy trading strategy based on demand-side response from users, aiming to optimize energy scheduling in smart community.MethodsFor a residential community with multiple buildings, this study coordinates distributed photovoltaics, energy storage systems, and flexible loads. A two-stage scheduling optimization model is established using the Stackelberg game framework based on pricing interactions between community operators and user load aggregators.ResultsSimulation results show that, compared to the traditional heat-determined power strategy, the proposed model reduces operational costs by 40.22% and increases photovoltaic utilization by 22.57%. Compared to the conventional cost-optimal operation strategy, the proposed model results in a 29.66% reduction in operational costs and a 6.78% increase in photovoltaic utilization.ConclusionsThe proposed strategy demonstrates excellent performance in achieving equitable benefit distribution, mitigating power fluctuations, flexibly meeting peak-load demands, enhancing renewable energy integration, and ensuring grid operational security.
Applications of electric power, Production of electric energy or power. Powerplants. Central stations
Dhanadhya Trupti, Kadam Swaraj, Prasad Shashikant
et al.
Electric vehicles (EVs) stand out as more efficient in conserving energy, reducing emissions, and safeguarding the environment compared to their fuel-powered counterparts. Consequently, as they find broader applications in the transportation industry, their significance continues to grow. The impending reality of widespread EV adoption is evident as their usage increases daily. In the shift towards the electronic revolution within the automotive sector, the manner in which EVs are charged becomes a pivotal concern. This comprehensive review examines recent advancements and persistent challenges in EV battery charging technologies. The paper analyzes various charging methodologies, including conductive, inductive, and battery swapping systems, evaluating their technical characteristics, implementation challenges, and impact on charging infrastructure. Key developments in fast-charging protocols, wireless power transfer efficiency, and smart grid integration are discussed. Critical challenges addressed include charging time optimization, infrastructure scalability, standardization issues, and grid stability concerns. The review also explores emerging technologies such as dynamic wireless charging and along with their potential impact on future EV adoption. Finally, the paper identifies research gaps and suggests future directions for improving EV charging technologies. This systematic review provides valuable insights for researchers, industry practitioners, and policymakers working towards advancing sustainable transportation solutions.
Due to the rising penetration of distributed generators into the current microgrids, reactive power management has become a crucial concern in terms of voltage stability and resilience of smart grids. In this regard, reactive power forecasting (RPF) is an essential tool for maintaining the reactive power management and planning of active electric distribution systems in which power flow is bidirectional. Machine learning (ML)-based algorithms are frequently applied to electric load forecasting owing to the fact that these methods achieve more accurate results in the short-term horizon. RPF is one of the challenging implementations of electric load forecasting and it can be characterised as a nonlinear problem with a variety of explanatory variables such as active and lagging reactive power values. In this paper, a real-time short-term RPF using ML-based algorithms including long short-term memory (LSTM) networks, random forest (RF), and extreme gradient boosted decision trees (XGBoost) were employed for an electric distribution system located in the North of England, UK. The study also incorporated convolutional neural network (CNN), gated recurrent unit (GRU) networks, and light gradient boosting machine (LightGBM) for benchmarking with the main selected methods. The experimental results demonstrated that LightGBM outperformed other models by achieving the highest accuracy with an R2 of 95.37% and the lowest root mean squared scaled error (RMSSE) of 0.541 while maintaining the shortest computation time of 0.396 s. These findings highlighted the potential of ML-based RPF techniques for improving voltage stability, optimising reactive power compensation, and enhancing energy efficiency in modern smart grids. To the best of our knowledge, there is a lack in the current literature for real-time applications of RPF and this paper is considered to fill this deficiency to create a path for aspiring researchers in the field.
Human grasps can be roughly categorized into two types: power grasps and precision grasps. Precision grasping enables tool use and is believed to have influenced human evolution. Today's multi-fingered robotic hands are effective in power grasps, but for tasks requiring precision, parallel grippers are still more widely adopted. This contrast highlights a key limitation in current robotic hand design: the difficulty of achieving both stable power grasps and precise, fine-grained manipulation within a single, versatile system. In this work, we bridge this gap by jointly optimizing the control and hardware design of a multi-fingered dexterous hand, enabling both power and precision manipulation. Rather than redesigning the entire hand, we introduce a lightweight fingertip geometry modification, represent it as a contact plane, and jointly optimize its parameters along with the corresponding control. Our control strategy dynamically switches between power and precision manipulation and simplifies precision control into parallel thumb-index motions, which proves robust for sim-to-real transfer. On the design side, we leverage large-scale simulation to optimize the fingertip geometry using a differentiable neural-physics surrogate model. We validate our approach through extensive experiments in both sim-to-real and real-to-real settings. Our method achieves an 82.5% zero-shot success rate on unseen objects in sim-to-real precision grasping, and a 93.3% success rate in challenging real-world tasks involving bread pinching. These results demonstrate that our co-design framework can significantly enhance the fine-grained manipulation ability of multi-fingered hands without reducing their ability for power grasps. Our project page is at https://jianglongye.com/power-to-precision
Abstract Managing the high‐rate‐power transients of Electric Vehicles (EVs) in a drive cycle is of great importance from the battery health and drive range aspects. This can be achieved by high power‐density storage, such as a high‐speed Flywheel Energy Storage System (FESS). It is shown that a variable‐mass flywheel can effectively utilise the FESS useable capacity in most transients close to optimal. Novel variable capacities FESS is proposed by introducing Dual‐Inertia FESS (DIFESS) for EVs. The feasibility of the proposed concept is evaluated by deriving the size of a Single‐Inertia FESS (SIFESS) for a battery EV, which runs the well‐known Urban Dynamometer Driving Schedule. The sizing framework consists of an Energy Management System using the constrained Pontryagin's minimum principle and a proposed sizing algorithm. Then, by splitting the derived SIFESS inertia into two separate inertias, the appropriate engaging control of inertias is determined for some driving cycles including, the Artemis Urban, Braunschweig City, and Worldwide Harmonised Light‐duty Vehicles Test Cycle. The dual inertias suitable sizes are derived using a proposed algorithm, which targets maximising the FESS useable capacity. The results show that compared to the SIFESS, the DIFESS can employ the FESS's useable capacity more effectively.