Showing papers on "Voltage published in 2021"

Ultra-high-voltage Ni-rich layered cathodes in practical Li metal batteries enabled by a sulfonamide-based electrolyte

Abstract: By increasing the charging voltage, a cell specific energy of >400 W h kg−1 is achievable with LiNi0.8Mn0.1Co0.1O2 in Li metal batteries. However, stable cycling of high-nickel cathodes at ultra-high voltages is extremely challenging. Here we report that a rationally designed sulfonamide-based electrolyte enables stable cycling of commercial LiNi0.8Co0.1Mn0.1O2 with a cut-off voltage up to 4.7 V in Li metal batteries. In contrast to commercial carbonate electrolytes, the electrolyte not only suppresses side reactions, stress-corrosion cracking, transition-metal dissolution and impedance growth on the cathode side, but also enables highly reversible Li metal stripping and plating leading to a compact morphology and low pulverization. Our lithium-metal battery delivers a specific capacity >230 mA h g−1 and an average Coulombic efficiency >99.65% over 100 cycles. Even under harsh testing conditions, the 4.7 V lithium-metal battery can retain >88% capacity for 90 cycles, advancing practical lithium-metal batteries. Charging at high voltages in principle makes batteries energy dense, but this is often achieved at the cost of the cycling stability. Here the authors design a sulfonamide-based electrolyte to enable a Li metal battery with a state-of-the-art cathode at an ultra-high voltage of 4.7 V while maintaining cyclability.

PVTv2: Improved Baselines with Pyramid Vision Transformer

Abstract: Transformer recently has shown encouraging progresses in computer vision. In this work, we present new baselines by improving the original Pyramid Vision Transformer (abbreviated as PVTv1) by adding three designs, including (1) overlapping patch embedding, (2) convolutional feed-forward networks, and (3) linear complexity attention layers. With these modifications, our PVTv2 significantly improves PVTv1 on three tasks e.g., classification, detection, and segmentation. Moreover, PVTv2 achieves comparable or better performances than recent works such as Swin Transformer. We hope this work will facilitate state-of-the-art Transformer researches in computer vision. Code is available at this https URL .

Battery Fault Diagnosis for Electric Vehicles Based on Voltage Abnormality by Combining the Long Short-Term Memory Neural Network and the Equivalent Circuit Model

Abstract: Battery fault diagnosis is essential for ensuring safe and reliable operation of electric vehicles. In this article, a novel battery fault diagnosis method is presented by combining the long short-term memory recurrent neural network and the equivalent circuit model. The modified adaptive boosting method is utilized to improve diagnosis accuracy, and a prejudging model is employed to reduce computational time and improve diagnosis reliability. Considering the influence of the driver behavior on battery systems, the proposed scheme is able to achieve potential failure risk assessment and accordingly to issue early thermal runaway warning. A large volume of real-world operation data is acquired from the National Monitoring and Management Center for New Energy Vehicles in China to examine its robustness, reliability, and superiority. The verification results show that the proposed method can achieve accurate fault diagnosis for potential battery cell failure and precise locating of thermal runaway cells.

Task-Adaptive Attention for Image Captioning

Abstract: Attention mechanisms are now widely used in image captioning models. However, most attention models only focus on visual features. When generating syntax related words, little visual information is needed. In this case, these attention models could mislead the word generation. In this paper, we propose Task-Adaptive Attention module for image captioning, which can alleviate this misleading problem and learn implicit non-visual clues which can be helpful for the generation of non-visual words. We further introduce a diversity regularization to enhance the expression ability of the Task-Adaptive Attention module. Extensive experiments on the MSCOCO captioning dataset demonstrate that by plugging our Task-Adaptive Attention module into a vanilla Transformer-based image captioning model, performance improvement can be achieved.

Bilayer of polyelectrolyte films for spontaneous power generation in air up to an integrated 1,000 V output.

Abstract: Environmentally adaptive power generation is attractive for the development of next-generation energy sources. Here we develop a heterogeneous moisture-enabled electric generator (HMEG) based on a bilayer of polyelectrolyte films. Through the spontaneous adsorption of water molecules in air and induced diffusion of oppositely charged ions, one single HMEG unit can produce a high voltage of ~0.95 V at low (25%) relative humidity (RH), and even jump to 1.38 V at 85% RH. A sequentially aligned stacking strategy is created for large-scale integration of HMEG units, to offer a voltage of more than 1,000 V under ambient conditions (25% RH, 25 °C). Using origami assembly, a small section of folded HMEGs renders an output of up to 43 V cm−3. Such integration devices supply sufficient power to illuminate a lamp bulb of 10 W, to drive a dynamic electronic ink screen and to control the gate voltage for a self-powered field effect transistor. A power generator exhibits enhanced output due to a dual-charge-carrier design. The voltage produced is constant yet competitive even under low relative humidity.

An Overview of Condition Monitoring Techniques for Capacitors in DC-Link Applications

Abstract: Capacitors are widely used in dc links of power electronic converters to balance power, suppress voltage ripple, and store short-term energy. Condition monitoring (CM) of dc-link capacitors has great significance in enhancing the reliability of power converter systems. Over the past few years, many efforts have been made to realize CM of dc-link capacitors. This article gives an overview and a comprehensive comparative evaluation of them with emphasis on the application objectives, implementation methods, and monitoring accuracy when being used. First, the design procedure for the CM of capacitors is introduced. Second, the main capacitor parameters estimation principles are summarized. According to these principles, various possible CM methods are derived in a step-by-step manner. On this basis, a comprehensive review and comparison of CM schemes for different types of dc-link applications are provided. Finally, application recommendations and future research trends are presented.

A Novel Consistency Evaluation Method for Series-Connected Battery Systems Based on Real-World Operation Data

Abstract: Unmanaged cell inconsistency may cause accelerated battery degradation or even thermal runaway accidents in electric vehicles (EVs). Accurate cell inconsistency evaluation is a prerequisite for efficient battery health management to maintain safe and reliable operation and is also vital for battery second-life utilization. This article presents a cell inconsistency evaluation model for series-connected battery systems based on real-world EV operation data. The open-circuit voltage (OCV), internal resistance, and charging voltage curve are extracted as consistency indicators (CIs) from a large volume of electric taxis’ operation data. The Thevenin equivalent circuit model is adopted to delineate battery dynamics, and an adaptive forgetting factor recursive least-squares method is proposed to reduce the fluctuation phenomenon in model parameter identification. With a modified robust regression method, the evolution characteristics of the three CIs are analyzed. The Mahalanobis distance in combination with the density-based spatial clustering of applications with noise is employed to comprehensively evaluate the multiparameter inconsistency state of a battery system based on the CIs. The results show that the proposed method can effectively assess cell inconsistency with high robustness and is competent for real-world applications.

MXene/Co3O4 composite based formaldehyde sensor driven by ZnO/MXene nanowire arrays piezoelectric nanogenerator

Abstract: Formaldehyde is widespread in daily life and industrial production, and the detection of formaldehyde is an important measure related to human health Currently, self-powered high-performance gas sensors have aroused great interests In this work, MXene/Co3O4 composite-based formaldehyde (HCHO) sensor working at room temperature (RT) driven by ZnO/MXene nanowire (NW) arrays piezoelectric nanogenerator (PENG) was reported A series of characterizations were performed to verify the synthesis of the sensing materials With HCHO concentration increasing from 001 ppm to 10 ppm, the MXene/Co3O4 composite sensor showed obvious response The piezoelectric output voltage based on ZnO/MXene NW arrays showed great self-powered ability to drive the MXene/Co3O4 composite sensor The humidity influence and the flexibility of the gas sensor were investigated for potential application In addition, the flexible PENG can be used to collect human motion energy as wearable devices The possible gas sensing mechanism was attributed to the synergistic interfacial interactions between MXene and Co3O4

Two-Stage Volt/Var Control in Active Distribution Networks With Multi-Agent Deep Reinforcement Learning Method

Abstract: The high penetration of intermittent renewable energy resources in active distribution networks (ADN) results in a great challenge for the conventional Volt-Var control (VVC). This article proposes a two-stage deep reinforcement learning (DRL)-based real-time VVC method to mitigate fast voltage violation while minimizing the network power loss. In the first stage, on-load tap changer (OLTC) and capacitor banks (CBs) are dispatched hourly based on the optimal power flow method. The optimization problem is formulated as a mixed-integer second-order cone programming (MISOCP) which can be effectively solved. In the second stage, the reactive power of photovoltaics (PVs) is regulated dynamically to mitigate fast voltage fluctuation based on the well-learned control strategy and local measurements. The real-time VVC problem is formulated and solved using a multi-agent deep deterministic policy gradient (MADDPG) method, which features offline centralized training and online decentralized application. Rather than using the critic network to evaluate the output of the actor-network, the gradient of the action-value function to action is derived analytically based on the voltage sensitivity method. The proposed approach is tested on the IEEE 33-bus distribution system and comparative simulation results show the enhanced control effect in mitigating voltage violations.

Six-Switch Step-Up Common-Grounded Five-Level Inverter With Switched-Capacitor Cell for Transformerless Grid-Tied PV Applications

Abstract: Photovoltaic string inverters with transformerless grid-connected architecture are the most commonly used solar converters owing to their appliance-friendly and cost-effective benefits. A novel circuit configuration for these converters is presented in this article, which seeks to address the shortcomings of most of the conventional topologies such as the problem of leakage current, voltage ratio transformations, and power quality. The proposed structure is based on the series–parallel switching conversion of the switched-capacitor (SC) cell and is comprised of only six unidirectional power switches with a common-grounded (CG) feature. Through the use of the SC cell and the CG connection of active and passive used elements, not only is the number of output voltage level enhanced by up to five but also a two times voltage boosting feature with a single-stage operation as well as elimination of the leakage current is acquired. Herein, to inject a tightly controlled current into the grid, a peak current controller approach has been used which can handle both the active and reactive power supports modes. Theoretical analysis, design guidelines, comparative study, and some experimental results are also given to corroborate the feasibility and accurate performance of the proposed topology.

A Novel Generalized Common-Ground Switched-Capacitor Multilevel Inverter Suitable for Transformerless Grid-Connected Applications

Abstract: Recent research on common-ground switched-capacitor transformerless (CGSC-TL) inverters shows some intriguing features, such as integrated voltage boosting ability, possible multilevel output voltage generation, and nullification of the leakage current issue. However, the number of output voltage levels and also the overall voltage boosting ratio of most of the existing CGSC-TL inverters are limited to five and two, respectively. This article presents a generalized circuit configuration of such converters capable of higher voltage gain and output voltage levels generation. A basic five-level (5L) CGSC-TL inverter is first proposed using eight power switches and two self-balanced dc-link capacitors. A generalized extension of the circuit for any output voltage levels and voltage gain is then presented while keeping all the traits of the proposed basic 5L-CGSC-TL inverter. The circuit descriptions, control strategy, design guidelines, comparative study, and the relevant simulation and experimental results for the proposed 5L-CGSC-TL inverters and its seven-level derived topology are presented to validate the effectiveness and feasibility of this proposal.

A Novel Modified Switched Inductor Boost Converter With Reduced Switch Voltage Stress

Abstract: Recently, switched inductor (SI) and switched capacitor techniques in dc–dc converter are recommended to achieve high voltage by using the principle of parallel charging and series discharging of reactive elements. It is noteworthy that four diodes, one high-voltage rating switch, and two inductors are required to design classical SI boost converter (SIBC). Moreover, in classical SIBC, the switch voltage stress is equal to the output voltage. In this article, modified SIBC (mSIBC) is proposed with reduced voltage stress across active switches. The proposed mSIBC configuration in this article is transformerless and simply derived by replacing the one diode of the classical SI structure with an active switch. As a result, mSIBC required low-voltage rating active switches, since the total output voltage is shared into two active switches. Moreover, the proposed mSIBC is low in cost, provides higher efficiency, and requires the same number of components compared with the classical SIBC. The continuous conduction mode and discontinuous conduction mode analysis, the effect of nonidealities on voltage gain, design methodology, and comparison are presented in detail. The operation and performance of the designed 500-W mSIBC are experimentally validated under different perturbations.

Low-Noise Magnetic Coil System for Recording 3-D Eye Movements

Abstract: Vestibular and oculomotor research often requires measurement of 3-D eye orientation and movement with high spatial and temporal precision and accuracy. We describe the design, implementation, validation, and use of a new magnetic coil system optimized for recording 3-D eye movements using small scleral coils in animals. Like older systems, the system design uses off-the-shelf components to drive three mutually orthogonal alternating magnetic fields at different frequencies. The scleral coil voltage induced by those fields is decomposed into three signals, each related to the coil’s orientation relative to the axis of one field component. Unlike older systems based on analog demodulation and filtering, this system uses a field-programmable gate array (FPGA) to oversample each induced scleral coil voltage (at 25 Msamples/s), demodulate in the digital domain, and average over 25 ksamples per data point to generate 1-ksamples/s output in real time. Noise floor is <0.036° peak-to-peak and linearity error is <0.1° during 345° rotations in all three dimensions. This FPGA-based design, which is both reprogrammable and freely available upon request, delivers sufficient performance to record eye movements at high spatial and temporal precision and accuracy using coils small enough for use with small animals.

SOC Estimation of Li-ion Batteries With Learning Rate-Optimized Deep Fully Convolutional Network

Abstract: In this letter, we train deep learning (DL) models to estimate the state-of-charge (SOC) of lithium-ion (Li-ion) battery directly from voltage, current, and battery temperature values. The deep fully convolutional network model is proposed for its novel architecture with learning rate optimization strategies. The proposed model is capable of estimating SOC at constant and varying ambient temperature on different drive cycles without having to be retrained. The model also outperformed other commonly used DL models such as the LSTM, GRU, and CNN on an open source Li-ion battery dataset. The model achieves 0.85% root mean squared error (RMSE) and 0.7% mean absolute error (MAE) at 25 °C and 2.0% RMSE and 1.55% MAE at varying ambient temperature (–20–25 °C).

A Review of Modulation and Control Techniques for Multilevel Inverters in Traction Applications

Abstract: Traction inverter has been the subject of many studies due to its essential role in the proper performance of the drive system. With the recent trend in increasing the input voltage in battery-powered electric vehicles, multilevel inverters have been proposed in the literature as a promising substitute for conventional two-level traction inverters. A critical aspect of utilizing multilevel structures is employing proper control and modulation techniques. The control system structure must be capable of handling a number of key issues, like capacitor voltage balancing and equal power loss sharing, which arise in multilevel topologies. This paper presents a review of the present-day traction drive systems in the industry, control and modulation techniques for multilevel structures in the inverters, as well as the principal challenges that need to be addressed in the control stage of the multilevel traction inverter. A comparison has been made between different methods based on the most important criteria and requirements of the traction drive system. Finally, future trends in this application are presented and some suggestions have been made for the next generation of traction drives.

On the feature selection for battery state of health estimation based on charging-discharging profiles

Abstract: Correctly evaluating the health status of the battery is of great significance for ensuring the safety of electric vehicles, and avoiding potential failures of electric vehicles. Recently, the data-driven methods have raised interest in evaluating battery the battery state of health (SOH) based on the statistical theory. However, the accuracy of the battery state of health estimation algorithms is greatly affected by the model input selection. Because of the limitation for battery data type, it is meaningful to extract the useful data information from the raw data. In this work, we extract health indicators from the battery current, voltage, temperature data based on the laboratory measured experimental data, which can inform model input choices, thus improving the accuracy in battery health estimation. Then, grey relation analysis is used to quantify the correlation between health indicators and battery capacity degradation, and using this quantified result as the basis for the selection of model variables for battery modeling. According to the correlation degree value which calculated by grey relation analysis, it shows that most health indicators are more related to the battery heath. The value of correlation degree for most features are above 90%, and the lowest value is 69%. Finally, the performance of the estimated model based on these health indicator is evaluated.

Soft-Switching Solid-State Transformer With Reduced Conduction Loss

Abstract: Solid-state transformers (SSTs) are a promising solution photovoltaic (PV), wind, traction, data center, battery energy storage system (BESS), and fast charging electric vehicle (EV) applications. The traditional SSTs are typically three-stage, i.e., hard-switching cascaded multilevel rectifiers and inverters with dual active bridge (DAB) converters, which leads to bulky passives, low efficiency, and high electromagnetic interference (EMI). This article proposes a new soft-switching solid-state transformer (S4T). The S4T has full-range zero-voltage switching (ZVS), electrolytic capacitor-less dc link, and controlled dv/dt , which reduces EMI. The S4T comprises two reverse-blocking current-source inverter (CSI) bridges, auxiliary branches for ZVS, and transformer magnetizing inductor as a reduced dc link with 60% ripple. Compared with the prior S4T, an effective change on the leakage inductance diode is made to reduce the number of the devices on the main power path by 20% for significant conduction loss saving and retain the same functionality of damping the resonance between the leakage and resonant capacitors and recycling trapped leakage energy. The conduction loss saving is crucial, being the dominating loss mechanism in SSTs. Importantly, the proposed single-stage SST not only holds the potential for high power density and high efficiency but also has full functionality, e.g., multiport dc loads integration, voltage regulation, and reactive power compensation, unlike the traditional single-stage matrix SST. The S4T can achieve single-stage isolated bidirectional dc–dc, ac–dc, dc–ac, or ac–ac conversion. It can also be configured input-series output-parallel (ISOP) in a modular way for medium-voltage (MV) grids. Hence, the S4T is a promising candidate for the SST. The full functionality, e.g., voltage buck–boost, multiport, etc., and the universality of the S4T for the dc–dc, dc–ac, and ac–ac conversion are verified through the simulations and experiments of two-port and three-port MV prototypes based on 3.3 kV SiC mosfet s in dc–dc, dc–ac, and ac–ac modes at 2 kV.

Isolated and Non-Isolated DC-to-DC Converters for Medium Voltage DC Networks: A Review

Abstract: MVDC technology is a promising solution to avoid installation of new AC networks. MVDC can provide optimum integration of large-scale renewable energy sources, the interconnection of different voltage levels of DC and AC grids with the ancillary services. The development in MVDC depends significantly on the DC-DC converters. Such converters support the modern trends of utilising medium-frequency transformers in power networks. Research on isolated converters technology is in its infancy and limited by the conversion ratio and component ratings. Besides, there is no standards exist covering specific aspects of isolated converter product. Thus, a review of such converters is needed. This work presents, for the first time, a review of the DC-DC power converter families in MVDC grids including the leading families which are isolated and non-isolated converters, as well as other subfamilies comparing the specifications and characteristics. Also, the applications of these converters are provided by focusing on the essential requirements for each application.

Optimal location, selection, and operation of battery energy storage systems and renewable distributed generation in medium–low voltage distribution networks

Abstract: This paper presents a methodology for the optimal location, selection, and operation of battery energy storage systems (BESSs) and renewable distributed generators (DGs) in medium–low voltage distribution systems. A mixed-integer non-linear programming model is presented to formulate the problem, and a planning-operation decomposition methodology is proposed to solve it. The proposed methodology is separated into two problems (planning and operation problems). The planning problem is related to the location and selection of these devices, and the operation problem is responsible for finding the optimal BESS operating scheme. For solving the planning problem is used a simulated annealing algorithm with a defined neighborhood structure that uses a sensitivity analysis based on the Zbus matrix. A novel decomposition method, which guarantees near-global-optimal solutions with low computational effort, is proposed for solving the operation problem. The effectiveness and accuracy of the proposed decomposition method is validated and tested on an 11-node test system from the specialized literature, and the robustness of the proposed method is assessed and tested on a modified version of an IEEE 135-node test system. The proposed planning-operation decomposition methodology is tested on a real medium–low voltage distribution system of 230 nodes. To verify the efficiency of the proposed methodology, four cases are compared: (I) without BESS and DGs, (II) with DGs, (III) with BESS, and (IV) with BESS and DGs. The numerical results demonstrate the effectiveness and robustness of the proposed methodology.

Resilient Control and Analysis for DC Microgrid System Under DoS and Impulsive FDI Attacks

Abstract: This paper proposes a resilient controller for DC microgrid to achieve current sharing and voltage restoration under discrete-time false data injection (FDI) and denial-of-service (DoS) attacks. Switching and impulsive signals are used to model the dynamic system of DC microgrid under DoS and FDI. To deal with the cyber attacks, a combined error of current and voltage is proposed and a switching secondary controller is designed. Based on the stability analysis method on hybrid systems, we establish a sufficient condition for selecting control parameters in relation to the average dwell time of FDI attack and the normal communication rate under DoS attack. Furthermore, an adaptive gain based control scheme is proposed to relax the requirement on knowledge of the cyber attacks in control parameter design. The utility of the results is illustrated through case studies on a tested DC microgrid.