Capacitance Model Research Articles

Comprehensive modeling and formulation of split DC link capacitors balancing problem in Three-Phase Three-Level bidirectional AC/DC converters operating with arbitrary power factor

Three-phase three-level AC/DC and DC/AC converters have demonstrated several advantages over their classical two-level counterparts such as better efficiency, enhanced total harmonic distortion, lower dv/dt and lesser overall chip area. T-type and neutral point clamped (NPC) topologies dominate the family of three-phase three-level converters, widely used in 10kVA – 100kVA power range. However, control of three-level converters is more complicated than of their classical two-level counterparts due to necessity of adopting an additional voltage loop, aimed to balance partial split DC link voltages. Moreover, the latter contain multiples of triple-base-frequency harmonic ripple even under balanced AC-side operation (this does not happen in two-level converters), which should be minimized by the balancing loop mentioned above. The nature of partial DC link ripple formation was analyzed in-depth for unity power factor operation, yet hardly extended to non-unity power factor cases. Moreover, inaccurate conclusions were drawn in some of the related studies. Consequently, this paper focuses on comprehensive modeling and formulation of split DC link capacitors balancing problem in three-phase three-level bidirectional AC/DC converters operating with arbitrary power factor. Analytical expression for dynamics of partial DC link voltages difference is derived and studied in depth. The relation between zero-sequence component of modulation signals and neutral point current, tending to unbalance partial DC link voltages is explicitly established for arbitrary power factor values. Practical cases of restricted and unrestricted zero-sequence component of modulation signals are presented. Analytical predictions are shown to be fully supported by simulations based on both switching-cycle-averaged and full switched converter models. Experimental results obtained employing a 10kVA T-type converter prototype operating with restricted zero-sequence component of modulation signals under different power factor values are also given to further validate the presented methodology.

Optimization of the Relay Coil Compensation Capacitor for the Three-Coil Wireless Power Transmission System

The wireless power transmission (WPT) system, which eliminates the limitation of physical connection and improves the convenience of power transmission, has gradually become a research focus in recent years. However, in the current three-coil WPT system, the power repeater is composed of a coupling coil and a compensation capacitor, and its tuning conditions will affect the power transmission ability of the WPT system. However, the exact impact of compensation capacitor is not clear, which leads to the design of power repeaters being based on experience or trial and error methods, making it difficult to achieve the optimization of the WPT system. Therefore, this paper takes transmission power and transmission efficiency as optimization objectives and obtains the optimal relay coil compensation capacitor model of the three-coil WPT system. The result found that the transmission efficiency of the three-coil WPT system after optimizing the compensation capacitor is 3% higher than before, and the anti-migration ability is enhanced. Meanwhile, in view of the dynamic changes in load and mutual inductance that may occur during the use of the three-coil WPT system, a compensation capacitor design and a relay coil compensation capacitor circuit are proposed, respectively, and the parameter setting scheme of the circuit is proposed. The innovative scheme proposed in this paper can effectively improve the transmission efficiency and stability of the three-coil WPT system.

Label-Free Neuropeptide Detection beyond the Debye Length Limit.

Biosensors with high selectivity, high sensitivity, and real-time detection capabilities are of significant interest for diagnostic applications as well as human health and performance monitoring. Graphene field-effect transistor (GFET) based biosensors are suitable for integration into wearable sensor technology and can potentially demonstrate the sensitivity and selectivity necessary for real-time detection and monitoring of biomarkers. Previously reported DC-mode GFET biosensors showed a high sensitivity for sensing biomarkers in solutions with a low salt concentration. However, due to Debye length screening, the sensitivity of the DC-mode GFET biosensors decreases significantly during operation in a physiological fluid such as sweat or interstitial fluid. To overcome the Debye screening length limitation, we report here alternating current (AC) mode heterodyne-based GFET biosensors for sensing neuropeptide-Y (NPY), a key stress biomarker, in artificial sweat at physiologically relevant ionic concentrations. Our AC-mode GFET biosensors show a record ultralow detection limit of 2 × 10-18 M with an extensive dynamic range of 10 orders of magnitude in sensor response to target NPY concentration. The sensors were characterized for various carrier frequencies (ranging from 30 kHz to 2 MHz) of the applied AC voltages and various salt concentrations (10, 50, and 100 mM). Contrary to DC-mode sensing, the AC-mode sensor response increases with an increase in salt concentration in the electrolyte. The sensor response can be further enhanced by tuning the carrier frequency of the applied AC voltage. The optimum response frequency of our sensor is approximately 400-600 kHz for salt concentrations of 50 and 100 mM, respectively. The salt-concentration- and frequency-dependent sensor response can be explained by an electrolyte-gated capacitance model.

Grey-box model for model predictive control of buildings

Model predictive control (MPC) can improve energy efficiency and demand-side flexibility in buildings. Developing a grey-box model suitable for MPC is not straightforward, especially in buildings combining, not only ventilation and usual internal loads, but also Thermally Activated Building Structures (TABS) and large glass façades with external shading. To address these complexities, this paper presents a reduced order grey-box approach, considering all these elements. Various single zone model structures are compared, combining resistance–capacitance model, with finite difference or finite volume methods for modelling the TABS. The performance of these various model structures is evaluated using experimental data from a well-equipped living laboratory building. Additionally, the influence of technical parameters on the model's performance is investigated.The best model variant, with an enhanced glass façade model, achieves an accuracy of 0.25 °C of Mean Absolute Error over a year of simulation, on the 24 h zone temperature forecast compared to the measurement. This model has a small number of parameters (8), which are estimated with the least square non-linear method. The stability of the parameter values is analysed. The parameter identification requires only a small historical dataset of 1–2 weeks for startup and 2–4 weeks for training. This provides an adaptive model, in the sense that it is updated regularly (every day or week) based on recent measurement data. This data-driven evolving model is suitable across a wide range of applications involving data-driven Model Predictive Control (MPC) for buildings.

A scalable solution recipe for a Ag-based neuromorphic device

Integration and scalability have posed significant problems in the advancement of brain-inspired intelligent systems. Here, we report a self-formed Ag device fabricated through a chemical dewetting process using an Ag organic precursor, which offers easy processing, scalability, and flexibility to address the above issues to a certain extent. The conditions of spin coating, precursor dilution, and use of solvents were varied to obtain different dewetted structures (broadly classified as bimodal and nearly unimodal). A microscopic study is performed to obtain insight into the dewetting mechanism. The electrical behavior of selected bimodal and nearly unimodal devices is related to the statistical analysis of their microscopic structures. A capacitance model is proposed to relate the threshold voltage (Vth) obtained electrically to the various microscopic parameters. Synaptic functionalities such as short-term potentiation (STP) and long-term potentiation (LTP) were emulated in a representative nearly unimodal and bimodal device, with the bimodal device showing a better performance. One of the cognitive behaviors, associative learning, was emulated in a bimodal device. Scalability is demonstrated by fabricating more than 1000 devices, with 96% exhibiting switching behavior. A flexible device is also fabricated, demonstrating synaptic functionalities (STP and LTP).

Dielectric relaxations without colossal permittivity in Mg and Nb co-doped BaTiO3 ceramics

Magnesium (Mg) and Niobium (Nb) co-doped BaTiO3 ceramics are prepared using a solid-state reaction method under an air atmosphere, and their crystal structures, microstructures, valence states and dielectric properties are studied. The ceramics adopt a cubic perovskite structure, and the lattice parameter increases with doping content owing to the larger ionic radii of dopants. The complex permittivity and electric modulus as a function of frequency and temperature are analyzed. Dielectric relaxations without colossal permittivity are observed. The relaxation processes are thermally activated and their variations with temperature are analyzed. The activation energy values for the relaxation processes are determined by the Arrhenius relationship and increase with doping content. By analyzing the electric modulus, the activation values for high-frequency relaxation are found to be associated with the electrical properties of grain, and the contribution of grain boundary could be neglected based on internal barrier layer capacitor (IBLC) model. The electron hopping between the different Ti valences, which is confirmed by X-ray photoelectron spectroscopy, is supposed to play an important role in high-frequency relaxation. The permittivity plateaus are related to the inhomogeneous structures of grain and grain boundary, and the low-frequency permittivity plateau is attributed to the space-charge polarization at grain boundaries. By comparing with Cu/Nb co-doped BaTiO3, the absence of colossal permittivity is ascribed to high resistivity of the samples. Charge density and migration as well as the insulating grain boundaries are critical for the formation of space charge and a high permittivity.

Model and characterization for dynamic non-uniform pixel capacitance in electrowetting displays

Electrowetting display (EWD) is a new reflective display device that has the advantages of fast response speed, low power consumption, and video playback. The pixel capacitance of EWDs changes with the flow of internal liquid during the pixel-on process, which affects the pixel response speed. To characterize the change in pixel capacitance and analyze the relationship between pixel capacitance and pixel response speed, we established a dynamic capacitance model during the pixel-on process, derived the integral equation of pixel capacitance, and demonstrated the validity of the model. Meanwhile, we also investigated the effects of the initial angle formed between the oil and the pixel wall, and the relative permittivity of the oil and the hydrophobic insulating layer on the pixel capacitance, respectively. Results showed that the pixel capacitance increased as the initial angle and the relative permittivity of the oil and hydrophobic insulation layers increased. In addition, when the initial angle is 0. 4π and the aperture ratio exceeded 30%, the time required for the pixel to reach the corresponding aperture ratio was the shortest. Moreover, the smaller the relative permittivity of hydrophobic insulating layer, the shorter time required for the pixel to reach the corresponding aperture ratio.

Constructing multiple heterogeneous shell on the CaCu3Ti4O12 core for better dielectric performances

A core-shell synthesis is proposed in this study to improve the dielectric loss and the resistivity of CaCu3Ti4O12 (CCTO), which exhibits a colossal permittivity, make it very interesting for capacitor applications. However, the grain boundaries of this ceramic material play a key role for the good dielectric properties. To control these boundaries can be performed by producing shells around the CCTO grains with different oxide mixtures. The design of core-shell structure is a possible approach to control the thickness and homogeneity of the shell, which will improve the properties of this material. In this work, as-prepared CCTO particles, synthetized by solid-state reaction, were successful coated with double or triple shell (SiO2, Al2O3, TiO2) using conventional sol–gel processing derivative from Stöber method. This coating with several binary oxides is made for the first time and has an innovative character in the improvement of properties for an application The CCTO coated with shell composed of SiO2 + Al2O3 + TiO2 mixture showed the best improvement with a colossal permittivity about 7.104, low loss tangent close to 0.05 and high resistivity equal to 109 Ω cm at 1 kHz. Indeed, a clear increasing of resistivity and decreasing of dielectric loss were observed for these samples compared with the resistivity of CCTO (≈105 Ω cm). Moreover, simulations of impedance curves give the capacitance and resistivity of the grain boundaries, which confirm by their linear behavior that the global properties of this material depend on the behavior of the grain boundaries in agreement with the Internal Bayer Layer Capacitors model.

A Scalable Knowledge-Based Neural Network Model for GaN HEMTs With Accurate Trapping and Self-Heating Effects Characterization

In this article, a scalable knowledge-based neural network (KBNN) large-signal model of gallium nitride (GaN) high-electron-mobility transistors (HEMTs) with accurate trapping and self-heating effects characterization is developed. An improved empirical drain current model is proposed and added to the neural network as prior knowledge, thereby establishing the drain current model, including self-heating effect. A new empirical equation is proposed to model the buffer-related trapping effect more accurately. Taking Angelov capacitance models as prior knowledge, the KBNN capacitance models are completed. Moreover, the scaling characteristics of the proposed KBNN model are studied. The developed model has been fully verified by different sizes of GaN HEMTs. Good agreement between the model simulation results and the measurement data, including current–voltage ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$I$</tex-math> </inline-formula> – <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$V$</tex-math> </inline-formula> ), <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$S$</tex-math> </inline-formula> -parameters, power characteristics, and load-pull data, confirms the effectiveness of the proposed model. The proposed scalable KBNN model is fast and accurate and would be useful for accurate large-signal modeling of large gate periphery GaN HEMTs for high-power radio frequency (RF) applications.

Analytic modelling of quantum capacitance and carrier concentration for [formula omitted]-borophene FET based gas sensor

In this work, we investigate the physical and electronic properties of β12 - borophene FET-based gas sensor using a theoretical quantum capacitance model based on tight-binding approach. We study the impact of adsorbed NH3, NO, NO2 and CO gas molecule on its density of states, carrier concentration, quantum capacitance and I–V characteristics. We found a remarkable variation in the energy band structure and the density of states (DOS) of the β12 - borophene in the presence of the adsorbed gas molecule. The appearance of non-identical Van-Hove singularities in the DOS in the presence of adsorbed gas molecules strongly indicates the high sensitivity of β12 - borophene. We found a significant increase in the carrier concentration for NH3 gas while it decreases for all other gases. Moreover, a drastic change in quantum capacitance and current–voltage relation is also observed in adsorbed gases. The different properties of the given gas molecules are compared with the pristine borophene and found to exhibit distinct wrinkles in each case, thereby indicating the strong selectivity of our proposed gas sensor. Though β12 - borophene is found to be highly sensitive for all studied gases, the NO gas is found to be most sensitive compared to the others.

Parameter sensitivity analysis and parameter identifiability analysis of electrochemical model under wide discharge rate

The increasing demand for electric vehicles, grid stability, and high pulse power loads has driven the development of new high-power lithium-ion batteries (LIBs) that are capable of discharging at extremely high C-rates. Accurately identifying physical parameters in electrochemical models is crucial for the rational design and optimization of these new high-power LIBs. Since high-rate discharge can significantly reduce discharge time, it is essential to investigate the impact of double-layer capacitors. In this study, we present a P2D coupled double-layer capacitor model and conduct a comprehensive analysis of the sensitivity of all 32 physical parameters to terminal voltage during constant current discharge over a wide rate range of 1C to 40C. This is the first time such an analysis has been performed. We classified the parameters into three groups based on their normalized parameter sensitivity index: high sensitivity, sensitive, and insensitive, and identified 14 physical parameters that can be recognized under constant current discharge conditions. We used a genetic algorithm to identify these parameters, achieving a root mean square error below 13 mV and an average absolute error below 10 mV between the simulation results of the model and the experimental data. This work provides guidance and inspiration for the parameter identification of high-power LIBs electrochemical models.

Mathematical modelling of the partial differential equations in microelectromechanical systems (MEMS) and its applications

This paper presents a model of a doubly clamped electrically actuated microbeam, which is a commonly used structure in microelectromechanical systems (MEMS). The model is based on Euler–Bernoulli beam theory and includes the effect of electrostatic forces on the beam’s deflection. The electric field is modeled using the parallel plate capacitor model, and the deflection of the beam is calculated using the Galerkin method. The behavior of a microbeam subjected to the van der Waals force, which is a weak intermolecular force that arises from the interaction between the beam and a nearby surface. The microbeam is modeled using the Euler–Bernoulli beam theory, and the van der Waals force is modeled using the Lennard–Jones potential. At the last we study the model of MEMS based on multi-walled carbon nanotubes (MWCNTs). MWCNTs have unique mechanical, thermal, and electrical properties, which make them ideal for use in MEMS applications. The approximate solution of the developed models is found by using homotopy perturbation based Aboodh transformation (HPATM). HPATM is a mathematical method used to solve nonlinear equations by converting them into linear forms. This approach involves introducing a small parameter and applying perturbation theory to obtain a solution in a series form. The method’s accuracy is defined based on the existing literature because its solution matched the variation iteration-based Laplace method. Also, we compared its results with the finite difference method. The validity of the stability analysis is further established by examining the stability in the vicinity of the fixed points. Sketches are made of the phase portraits close to the equilibrium points.

Photodetectors based on chemical vapor deposition or liquid processed multi-wall carbon nanotubes

Carbon nanotubes (CNTs) have attracted great interest for applications due to their unique mechanical, electronic and optoelectronic properties. The advantages of the multiple wall carbon nanotubes for optoelectronic devices like photodetectors and photodiodes are the large effective photo-collector surface as well as the possibility to tune the band gap and absorbance by controlling the growth parameters. In this work, we demonstrate two types of hybrid Multi-Wall CNTs/Si3N4/n-Si photodetectors based on ordered (produced with Chemical Vapor Deposition – CVD) and solution processed MWCNTs and evaluate their performance in UV and visual spectrum (275 nm, 655 nm). Device parameters were obtained by comparison of I–V characteristics to Schottky barrier models and C–V characteristics to standard MIS capacitor models. The role of silicon nitride layer was also examined by comparing devices with different nitride layer thickness. Focusing on the UV part of the spectrum, the solution processed device was photosensitive in the voltage range 3V–6V giving its best performance (External Quantum Efficiency - E.Q.E. 85%@275 nm) at 5V and illumination of 5 μW. The CVD processed device showed lower EQE for the same bias voltage range. Both devices showed a low performance in optical part of the spectrum compared to the corresponding one in the UV part of the spectrum. The mean optical responsivity values obtained at the UV part of the spectrum in both cases indicate that the Visible light blind MWCNT/Si3N4/n-Si system have a potential use in UV photodetector applications. In particular, solution processed devices may pave the way for cheap production of UV sensor systems.

Fragility-based lot-sizing in veterinary pharmaceutical plants under demand uncertainty

We study a production lot-sizing problem inspired by a veterinary pharmaceutical plant in which demands are uncertain. First, we develop a deterministic capacitated lot-sizing model for the production of animal pesticides, performed in three machine-specific stages. Second, we propose a traditional robust optimisation formulation following the popular budget-of-uncertainty approach. Third, we derive a novel fragility-based approach that circumvents well-known issues with traditional robust optimisation approaches, such as the estimation of budgets of uncertainty, the over-conservatism of robust solutions and the sensitivity of solutions to the decision maker's risk attitude. The fragility-based approach is grounded in the idea of minimising violations, over the full uncertainty support, from a user-specified cost target. It avoids the estimation of budgets of uncertainty and produces less conservative solutions via explicit modelling of constraint violation. We demonstrate the effectiveness of our approach on instances built upon real data provided by our industrial partner, a major player in the Brazilian veterinary pharmaceutical sector. The results show that our fragility-based approach reduces average total costs across all instances and maintains greater model stability under different target estimations. It also preserves cost savings when bottlenecks are introduced in production and when inventory costs and capacities are varied.

Research on key technologies of large-scale wind-solar hybrid grid energy storage capacity big data configuration optimization

Due to the uncertainty and randomness of large-scale wind and light, the output power of the power grid has great fluctuations. If it is directly connected to the grid, it will affect the main grid. In addition, when the grid switches between on-grid/off-grid operation modes, there will be power shortages, shocks and oscillations. The scientific and reasonable configuration of energy storage system capacity big data can reduce the load power shortage rate, improve the utilization rate of renewable energy, and ensure the reliable operation of the power grid. For this reason, the key technology of large-scale wind-solar hybrid grid energy storage capacity big data configuration optimization is studied. A large-scale wind-solar hybrid grid energy storage structure is proposed, and the working characteristics of photovoltaic power generation and wind power generation are analyzed, and the probability model of photovoltaic power generation, wind power generation and load, as well as the charging and discharging model of battery and super capacitor are established accordingly. On this basis, the optimization objective function is set, the constraints are determined, and the large-scale wind-solar hybrid grid energy storage capacity big data configuration optimization model is constructed. And the PSO algorithm is used to solve the model to realize the big data configuration optimization of large-scale wind-solar hybrid grid energy storage capacity. The research results show that the proposed method of large-scale wind-solar hybrid grid energy storage system has good power supply reliability and economy, and can effectively improve the utilization rate of renewable energy.

Modeling of inversion layer capacitance of III-V double gate MOSFETs using a neural network-based regression technique

Modeling of inversion layer capacitance of III-V double gate MOSFETs using a neural network-based regression technique

Lifetime prediction and reliability analysis for aluminum electrolytic capacitors in EV charging module based on mission profiles

The charging modules of Electric vehicles (EVs) always run in a complex and variable state. As the weakness in the reliable operation of charging modules, the accurate lifetime prediction of aluminum electrolytic capacitors (Al-caps) is important for the later maintenance and reliability design. The hotspot temperature calculation method and lifetime model limit the accuracy of aluminum electrolytic capacitors lifetime prediction methods, which cannot meet the increasing requirements for reliability. In order to solve the problems above, this paper has proposed a hotspot temperature calculation method based on the ripple current with frequency characteristics and the cooling conditions on the heat generation and thermal conductivity of the capacitors. Furthermore, the lifetime model under reference voltage has been constructed with 3D surface fitting toolbox, which describes the trends of capacitor lifetime with ambient temperature and hotspot temperature under the constant voltage condition. Considering the variation of voltage, the multiple lifetime model of capacitor is established with a voltage correction coefficient. With the proposed method, it can be realized about the real-time lifetime prediction of capacitors under multiple operating profiles such as ripple current, thermal dissipation conditions, ambient temperature and operating voltage. Finally, the effectiveness of the proposed method is verified with the annual profiles of a 30 kW EV charging module.

A fast thermal simulation and dynamic feedback control framework for lithium-ion batteries

The temperature has a significant effect on the lifespan and the safety of the batteries which are one of the core components of electric vehicles. A well-designed battery thermal management system is required to adjust the temperature of the batteries within an appropriate range for thermal safety, and further minimize the energy consumption for energy savings. However, these two goals form a tradeoff. Typically, increased energy consumption for cooling results in safer batteries thermally. To examine this tradeoff, firstly, a linear electro-thermal model is built to evaluate the thermal dynamics of the operating battery-cooling module. The linear model accounts for the heat generation based on internal resistance, and the heat transfer based on a resistance–capacitance model. Such linear model reduces the computation of the model simulation to dynamically catch up with the change of temperatures and to implement control strategies on the cooling system in a short time. Based on this model, a model predictive control (MPC) framework is proposed to design thermal control methods, which considers both thermal safety and energy saving. The weight of these two goals can be adjusted to explore the tradeoff between them. Finally, we investigate the performance of the MPC framework by applying the Urban Dynamometer Driving Schedule (UDDS) to the battery module. The simulation results show that the electro-thermal model is effective for battery thermal dynamics evaluation. Additionally, the tradeoff between the two goals is demonstrated by the simulation.

A Non-Arrhenius Model for Mechanism Consistency Checking in Accelerated Degradation Tests

Degradation models are central to the lifetime prediction of electromagnetic relays. Coefficients of degradation models under accelerated degradation test (ADTs) can be obtained experimentally, and it is customary to map these coefficients back to those describing the actual degradation by the so-called Arrhenius model. However, for some components, such as springs in electromagnetic relays, the Arrhenius model is only appropriate over a certain ADT temperature range, which implies inaccurate mapping outside that range. On this point, an error function model (EFM) is proposed to overcome the shortcomings of the Arrhenius model. EFM is derived from the average vibration energy of the crystal, which is further related to temperature alongside some constants. The empirical part of the paper compares the proposed EFM to the Arrhenius model for the ADT of 28-V–2-A electromagnetic relays. The results show that EFM is superior in describing the temperature characteristics of coefficients in the degradation model. Through mechanism consistency checking, EFM is also shown to be a better option than the Arrhenius model. Moving beyond the case of electromagnetic relays, EFM is thought to have better applicability in the degradation models of capacitors and rubbers.

Exploring the acid–base surface properties of vivianite crystals in aqueous solution

The acid–base surface properties of vivianite crystals highly affect the adsorption capacity of vivianite for heavy metals and organic matters in aqueous solution. Therefore, the aim of the study was to innovatively explore and quantify the acid–base surface characterization of vivianite crystals in aqueous solution. It was found that the two surface active adsorption sites (≡POH and ≡FeOH) and five surface species thereof (i.e., ≡PO, ≡POH, ≡FeOH, ≡FeO−, and ≡FeOH2+) underwent surface reaction on vivianite crystals. Moreover, the active surface site and point of zero charge of vivianite crystals were 18 sites/nm2 and pH 5.9. Importantly, constant capacitance model was successfully employed to model the titration data, from which the distribution of surface species as function of pH and surface reaction constants were also accurately derived. The finding herein can be applied for plausible modeling molecular‐scale adsorption of the impurities on vivianite surface.