Discharge Current Profiles Research Articles

Performance and plasma diagnostics of the Air-breathing Microwave Plasma CAThode (AMPCAT) coupled to a cylindrical Hall thruster

The Air-breathing Microwave Plasma CAThode (AMPCAT) has been developed for air-breathing electric propulsion in very-low Earth orbit. In this study, the standalone AMPCAT plasma characteristics are analyzed by means of several diagnostic tools and operation on xenon is compared to a conventional hollow cathode. A transition of AMPCAT extracted current from a lower (<0.1 A) to higher-current (>0.5 A) mode, triggered by increasing the negative cathode bias voltage, is accompanied by a significant rise in internal electron density and external electron temperature. The AMPCAT is coupled with a cylindrical Hall thruster in the 100–300 W power-level running on 0.5–0.7 mg/s of xenon, and the thrust is directly measured for cathode operation with both xenon and air. Stable thruster operation is demonstrated for the AMPCAT running on both propellants. For xenon, the performance is compared to a hollow cathode, which reveals matching discharge current profiles but a significantly higher thrust for the AMPCAT at low discharge voltages, approximately two times higher at 200 V. Langmuir probe measurements highlight a 30–40 V lower plasma potential in the cathode vicinity for the AMPCAT with xenon compared to both the hollow cathode and AMPCAT with air. This indicates a significantly improved coupling of cathode electrons to the thruster discharge, yielding an increased degree of ionization. Faraday probe and Wien filter results show that a larger current utilization efficiency drives the observed performance difference at low discharge voltages, rather than a significant change in ion acceleration or plume divergence.

Syllogism of Li-FePO4 Battery Cell Voltage Parameter Guess Under Aperiodic Dynamic Current Profile by Some Data-Driven Techniques: A Error-Based Statistical Comparison Between Decision Tree, Support Vector, Bee Colony, and Neural Network

The various procedures are used in the literature for defining battery parameter change such as direct measurement methods, model-based methods, and data-driven methods, which contain the algorithms used in this paper also. The main aim of this study is to present a powerful and highly correct way of parameter forecasting of the A123 Systems 26650 cylindrical type Li-FePO4 battery cell. A few of the goal of this paper is to show the guessing performance of the artificial bee colony algorithm, which has a very limited number of applications on the battery parameter of literature, under the non-periodic dynamic charge/ discharge current profile. Then, a comparison has been made between artificial bee colony, artificial neural networks, support vector machine, and decision tree algorithms used in the paper. The load-connected terminal voltage is defined by considering the 100%-60% state of charge range in the primary usage areas of the batteries. A statistical comparison has been made by considering the absolute errors, squared errors, and the regression values information regarding the results presented by the methods. Consequently, the regression values that give information about the consistency of the confidence interval and results, of the bee colony, neural network, support vector, and decision tree methods have been determined as 99.92%, 99.75%, 96.00% and 95.79%, respectively. Moreover, mean squared errors of the methods has been calculated as 0.00202%, 0.00648%, 0.00998%, and 0.11%, respectively. As a new generation algorithm, artificial bee colony, which gave the most successful results according to the results obtained in the study, has been compared with two different methods selected from the existing literature, eXtreme Gradient Boosting and Smoothed eXtreme Gradient Boosting.

Capacity estimation of lithium-ion batteries based on adaptive empirical wavelet transform and long short-term memory neural network

To ensure the durability and safety of electric vehicles (EVs), it is vital to monitor the capacity deterioration of lithium-ion batteries (LIBs). However, due to complex physicochemical interactions and temperature effects, the capacity of LIBs cannot be directly measured. This work presents a novel generalized approach for estimating the capacity of LIBs based on the adaptive empirical wavelet transform (EWT) and long-short-term memory neural network (LSTM). The adaptive EWT is a potent tool for analyzing the charging-discharging terminal voltage signal with non-stationary and transient phenomena of the LIBs in the time-frequency domain. Specifically, the measured voltage signals of the LIBs are decomposed into nine multi-resolution modes to display the high and low-frequency components. Then, 13 statistical features are extracted to investigate the correlation between the capacity degradation and the extracted features. Afterwards, the LSTM model is developed to estimate the capacity of the LIBs. The proposed approach has been validated using two datasets: (1) NASA's randomized dataset with 24 LIBs cycled under generally varying operational conditions and (2) Stanford University's dataset with 10 LIBs cycled with EV discharge current profile. Compared with the state-of-the-art, the proposed method accurately estimates LIB capacity with an average root mean square error of 1.26 % and a maximum error of 2.74 %.

Key Modes of Ignition and Maintenance of Corona Discharge in Air

Theoretical and experimental studies of various modes of corona discharge operation in atmospheric pressure air are presented in this short review. The original results of modeling negative corona discharges are presented, taking into account the non-stationary plasma-chemical kinetics of charged particles in air plasma. The space–time evolution of the discharge in needle-to-plane geometry is investigated and analyzed. Several stages of discharge development are revealed from the moment of initiation of a low-negative current corona to the quasi-stationary mode of a glow discharge. Experimental data of the authors are presented. Modern technology and diagnostic equipment with a wide variation of the main parameters (the shape and polarity of the applied voltage, the type of gap, etc.) was used. The measurement of the optical characteristics of the plasma glow was carried out with high spatial resolution. Corona discharge current pulse profiles in the air at atmospheric pressure have been recorded with subnanosecond time resolution. With a positive polarity of the pin electrode and high voltage, a transition from a spherical streamer initiating a corona discharge to a cylindrical streamer is shown. The author’s results are rigorously evaluated through a critical comparison with findings from other research groups.

Discharge plasma formation in square capillary with gas supply channels

A comprehensive model of processes in a discharge capillary is required in order to obtain nominal parameters of a preformed plasma channel suitable for the laser wake-field acceleration. We present three-dimensional magnetohydrodynamics simulations of a hydrogen gas filling process and discharge plasma formation in a short square shaped capillary with gas supply channels. Time evolution of the gas pressure and the plasma density in the capillary channel for a chosen discharge current profile is analyzed. Performed simulations provide distributions of the electric current, the magnetic field, and the electron density along the whole channel, taking into account gas supply areas as well as areas outside of the capillary. Obtained results show that the presence of gas supplies leads to the inhomogeneous plasma density distribution along the capillary channel which has to be taken into account for generating an optimal laser-driven electron beam.

Forecasting Methods of Battery Charge and Discharge Current Profile for LEO Satellites

The orbital characteristics of low Earth orbit (LEO) satellite systems prevent continuous monitoring because ground access time is limited. For this reason, the development of simulators for predicting satellite states for the entire orbit is required. Power-related prediction is one of the important LEO satellite simulations because it is directly related to the lifespan and mission of the satellite. Accurate predictions of the charge and discharge current of a power system’s battery are essential for fault management design, mission design, and expansion of LEO satellites. However, it is difficult to accurately predict the battery power demand and charging of LEO satellites because they have nonlinear characteristics that depend on the satellite’s attitude, season, orbit, mission, and operating period. Therefore, this paper proposes a novel battery charge and discharge current prediction technique using the bidirectional long short-term memory (Bi-LSTM) model for the development of a LEO satellite power simulator. The prediction performance is demonstrated by applying the proposed technique to the KOM-SAT-3A and KOMSAT-5 satellites operating in real orbits. As a result, the prediction accuracy of the proposed Bi-LSTM shows root mean square error (RMSE) within 2.3 A, and the prediction error well outperforms the most recent the probability-based SARIMA model.

Concise characterization of cold atmospheric pressure helium plasma jet

This article provides a concise methodology for the development of a cold atmospheric pressure plasma jet and its characterization. To optimize the plasma jet parameters for biological and industrial applications, it is highly necessary to thoroughly understand its characteristics. The major emphasis of this work is to utilize simple and advanced diagnostics systematically with low complexity in the post-data analysis and to obtain in situ information of plasma jet parameters. The detailed optimization methods and the effect of the applied voltage and gas flow rate to achieve the stable plasma jet of the desired dimensions are discussed. In addition, the effects of the gas flow rate on the discharge current profiles and filament behavior are provided. Moreover, optical techniques, such as optical emission spectroscopy and time-resolved fast imaging, are used for the characterization of plasma parameters, i.e., Texc and ne, in a simple way. The gas temperature along the length of the plasma jet is estimated using a K-type thermocouple. The discussed simple characterization techniques and range of parameters of our designed plasma source will be useful for the development and optimization of plasma jet sources for various biological and industrial applications. Furthermore, we have also discussed various applications where we can use the discoursed diagnostics for the system development as well as for characterization. As the characterization of cold atmospheric pressure plasma jets is a multiphysics study, this concise characterization report on the cold atmospheric pressure plasma aims to provide necessary information for early researchers.

Particle-Filtering-Based Prognostics for the State of Maximum Power Available in Lithium-Ion Batteries at Electromobility Applications

Nowadays, electric vehicles such as cars and bicycles are increasing their popularity due to the rising environmental consciousness. The autonomy required by these means of transport has marked a significant and steady growth in the development of battery technologies. In this sense, it is crucial to estimate and prognosticate critical parameters of battery packs such as the State of Charge (SOC), the State of Maximum Power Available (SoMPA), and the Failure Time. All these indicators are relevant to determine if both the energy stored in the battery of electric vehicles and power specifications are sufficient to successfully complete a required route, avoiding battery preventive disconnection before arrival. In this regard, this paper presents a novel approach to estimate and prognosticate the SOC and SoMPA of Lithium-Ion batteries in the context of electromobility applications. The proposed method uses the formulation of an optimization problem to find an analytical relationship between the SOC and the SoMPA; whereas the battery pack is modeled in terms of both the polarization resistance and the SOC. Particle filtering algorithms are used to compute online estimates and prognostic results, while the characterization of the usage profile of the battery bank is achieved using probability-based models (Markov chains). The problem of battery monitoring for an electric bicycle is used as a case study to validate the proposed scheme, when driven in flat and sloped routes to generate different usage profiles. It is demonstrated that the proposed methodology allows to successfully prognosticate both SOC and SoMPA when the future discharge current profile is characterized in terms of probability-based models.

Influence of catalyst packing configuration on the discharge characteristics of dielectric barrier discharge reactors: A numerical investigation

A two-dimensional numerical fluid model is developed for studying the influence of packing configurations on dielectric barrier discharge (DBD) characteristics. Discharge current profiles and time averaged electric field strength, electron number density, and electron temperature distributions are compared for the three DBD configurations, plain DBD with no packing, partially packed DBD, and fully packed DBD. The results show that a strong change in discharge behaviour occurs when a DBD is fully packed as compared to partial packing or no packing. While the average electric field strength and electron temperature of a fully packed DBD are higher relative to the other DBD configurations, the average electron density is substantially lower and may impede the DBD reactor performance under certain operating conditions. Possible scenarios of the synergistic effect of the combination of plasma with catalysis are also discussed.

An extended single particle model-based parameter identification scheme for lithium-ion cells

The accurate modeling and parameter identification of lithium-ion battery are of great significance in real-time control and high-performance operation for advanced battery management system (BMS) in electrified vehicles (EVs). However, it is difficult to obtain the information about the interior state inside battery, because it cannot be directly measured by some electric devices. In order to accurately identify the key state parameters of lithium-ion cell applied to electric ground vehicles, an extended single particle model of lithium-ion cell with electrolyte dynamics behaviors is first built up based on the porous electrode theory and concentration theory in this article. Compared with the conventional single particle cell model, the parameter description of the solid electrolyte interface film is incorporated into this model, and the coupled effects of temperature-dependent and electrolyte-dependent electrochemical parameters on the cell discharge are also taken into consideration. Based on this extended single particle cell model, a simplified parameter sensitivity analysis method and a comprehensive parameter identification scheme for lithium-ion cell are proposed herein, in which a sensitivity analysis of the capacity to a subset of electrochemical parameters that are hypothesized to evolve throughout the battery's life, is conducted to determine the highly sensitive parameters to be identified under some particular operation scenarios, and further to solve the parameter optimization problem using the genetic algorithm. Based on this method, the test data under the working condition of 1 C discharge rate at 23℃ are employed to evaluate the identified parameters of lithium-ion battery cell with a peak value of voltage error less than 3.8%. Afterwards, the effectiveness and feasibility of the proposed parameter identification scheme are validated by the comparative study of the simulated output voltage and the experimental output voltage under the same input current profile. Specifically, the 0.05 C discharge and HPPC (hybrid pulse power characterization) current profile are used to verify the evaluated parameters under the 1 C discharge condition, and the maximum relative errors of voltage with 0.05 C galvanostatic discharge profile at 23 and 45℃ are 3.4% and 2.6% by using our proposed SPMe_SEI model, and 5.7% and 4.0% by using the traditional SPMe model, respectively. Moreover, the maximum relative errors of voltage with HPPC discharge profile at 23 and 45℃ are 1.9% and 1.5% by using our proposed SPMe_SEI model, and 2.1% and 1.8% by using the traditional SPMe model, respectively. It is concluded that the proposed parameter identification scheme for a lithium-ion cell model can provide a solid theory foundation for facilitating the estimation of state-of-health in BMS application.

State of Charge and State of Health Estimation for Lithium Batteries Using Recurrent Neural Networks

This paper presents an application of dynamically driven recurrent networks (DDRNs) in online electric vehicle (EV) battery analysis. In this paper, a nonlinear autoregressive with exogenous inputs (NARX) architecture of the DDRN is designed for both state of charge (SOC) and state of health (SOH) estimation. Unlike other techniques, this estimation strategy is subject to the global feedback theorem (GFT) which increases both computational intelligence and robustness while maintaining reasonable simplicity. The proposed technique requires no model or knowledge of battery's internal parameters, but rather uses the battery's voltage, charge/discharge currents, and ambient temperature variations to accurately estimate battery's SOC and SOH simultaneously. The presented method is evaluated experimentally using two different batteries namely lithium iron phosphate ( $\text{LiFePO}_4$ ) and lithium titanate ( $\text{LTO}$ ) both subject to dynamic charge and discharge current profiles and change in ambient temperature. Results highlight the robustness of this method to battery's nonlinear dynamic nature, hysteresis, aging, dynamic current profile, and parametric uncertainties. The simplicity and robustness of this method make it suitable and effective for EVs’ battery management system (BMS).

Impact of discharge current profile on the lasing efficiency of 46.9 nm capillary discharge soft x-ray laser

The very important role played by the discharge current profile on the lasing efficiency of 46.9 nm capillary discharge soft x-ray laser has been experimentally established. The discharge current profile was varied either by making the current faster and keeping its peak value fixed or by increasing its peak value and keeping the duration fixed. When the dI/dt value was increased from 7.5 × 1011 to 9.2 × 1011 A s−1 by making the current faster, the soft x-ray laser efficiency was found to be enhanced significantly. This was reflected by a substantial increase (~48%) in the gain-coefficient of the x-ray laser from 0.69 cm−1 to 1.02 cm−1 respectively. The effect of current amplitude on the gain-coefficient was also studied by measuring the gain-coefficient for different current amplitudes while keeping the duration fixed. It was found that there exists an optimum value of discharge current amplitude at which the gain-coefficient becomes maximum. Using such optimization, the gain-coefficient could be further enhanced to 1.33 cm−1 which is ~93% higher than the earlier value of 0.69 cm−1.

Robust Adaptive Sliding-Mode Observer Using RBF Neural Network for Lithium-Ion Battery State of Charge Estimation in Electric Vehicles

This paper presents a robust sliding-mode observer (RSMO) for state-of-charge (SOC) estimation of a lithium-polymer battery (LiPB) in electric vehicles (EVs). A radial basis function (RBF) neural network (NN) is employed to adaptively learn an upper bound of system uncertainty. The switching gain of the RSMO is adjusted based on the learned upper bound to achieve asymptotic error convergence of the SOC estimation. A battery equivalent circuit model (BECM) is constructed for battery modeling, and its BECM is identified in real time by using a forgetting-factor recursive least squares (FFRLS) algorithm. The experiments under the discharge current profiles based on EV driving cycles are conducted on the LiPB to validate the effectiveness and accuracy of the proposed framework for the SOC estimation.

The Charge Release and Its Mechanism for Pb(In1/2Nb1/2)–Pb(Mg1/3Nb2/3)–PbTiO3 Ferroelectric Crystals Under One‐Dimensional Shock Wave Compression

The charge release and related mechanisms for Pb(In1/2Nb1/2)–Pb(Mg1/3Nb2/3)–PbTiO3 (PIN–PMN–PT) ferroelectric crystals under one‐dimensional shock wave compression were investigated using discharge current profile measurement, by which the piezoelectric stress coefficient e31 and the phase transition (from tetragonal to orthorhombic phase) pressure were obtained, being −2.9 C/m2 and 2.3 GPa, respectively. Based on experiment results and thermodynamics analysis, it was found that the one‐dimensional shock compression favored ferroelectric phase, being different from the effect of hydrostatic pressure, which favored paraelectric phase. This phenomenon can be attributed to the crystal anisotropy and electromechanical coupling effects as one‐dimensional shock compression is applied to PIN–PMN–PT ferroelectric crystals.

Understanding the discharge activity across GFRP material due to salt deposit under transient voltages by adopting OES and LIBS technique

In the present study, breakdown characteristics of a glass fiber reinforced plastic (GFRP) with different salt deposit densities (SDD) under standard lightning impulse (LI)/ switching impulse (SI) voltages are studied. Optical emission during discharge process and the discharge current are measured simultaneously. It is observed that flashover voltage (FOV) reduces with increase in salt deposit density on GFRP material under LI/SI (irrespective of the polarity of applied voltage) voltages. It is also observed that irrespective of level of SDD, the FOV is less with SIV compared with LIV. FOV under negative SIV is less compared with positive SIV. Optical emission corresponding to Na I emission at 589 nm in discharge plasma follows the discharge current profile and also the lifetime of Na I emission is high under switching impulse compared with lightning impulse voltages. Na I emission in the discharge plasma sustains for a longer period in case of negative switching impulse voltages. Plasma temperature is estimated using emission lines and it is observed that the local temperature during discharge process is high under negative switching impulse voltage. With increase in salt deposit density, the optical emission spectrum of GFRP material in addition of Na I line at 589 nm is observed. Laser induced breakdown spectroscopy (LIBS) technique is proposed and demonstrated for the detection of a salt deposit on a GFRP material from a standoff distance of 1 m. In addition, temporal and spatial profile of laser induced plasma on the polluted GFRP material is used to quantify the salt deposit. Laser fluence less than 5 J/cm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> is ideally suited to rank the severity of a salt deposit on GFRP material at an incident laser wavelength of 1064 nm.

A novel approach for state of charge estimation based on adaptive switching gain sliding mode observer in electric vehicles

In this paper, a novel approach for battery state of charge (SOC) estimation in electric vehicles (EVs) based on an adaptive switching gain sliding mode observer (ASGSMO) has been presented. To design the ASGSMO for the SOC estimation, the state equations based on a battery equivalent circuit model (BECM) are derived to represent dynamic behaviours of a battery. Comparing with a conventional sliding mode observer, the ASGSMO has a capability of minimising chattering levels in the SOC estimation by using the self-adjusted switching gain while maintaining the characteristics of being able to compensate modelling errors caused by the parameter variations of the BECM. Lyapunov stability theory is adopted to prove the error convergence of the ASGSMO for the SOC estimation. The lithium-polymer battery (LiPB) is utilised to conduct experiments for determining the parameters of the BECM and verifying the effectiveness of the proposed ASGSMO in various discharge current profiles including EV driving conditions in both city and suburban.

Sliding Mode Observer for State of Charge Estimation Based on Battery Equivalent Circuit in Electric Vehicles

The sliding mode observer (SMO) for battery state of charge (SOC) estimation based on the improved battery equivalent circuit is presented. The convergence of the SMO is proved by Lyapunov stability theory. The advantage of the SMO is that the modelling errors caused by the variation parameters of circuit model can be compensated. Three sets of the testing data under different discharge current profiles generated by DUALFOIL battery simulation program are used to extract the circuit model parameters and to verify the effectiveness of the proposed SMO for the SOC estimation. It shows that the proposed SOC observer can provide robust tracking performance and accurate SOC estimation in electric vehicle driving conditions.

Sliding mode observer for state of charge estimation based on battery equivalent circuit in electric vehicles

The sliding mode observer (SMO) for battery state of charge (SOC) estimation based on the improved battery equivalent circuit is presented. The convergence of the SMO is proved by Lyapunov stability theory. The advantage of the SMO is that the modelling errors caused by the variation parameters of circuit model can be compensated. Three sets of the testing data under different discharge current profiles generated by DUALFOIL battery simulation program are used to extract the circuit model parameters and to verify the effectiveness of the proposed SMO for the SOC estimation. It shows that the proposed SOC observer can provide robust tracking performance and accurate SOC estimation in electric vehicle driving conditions.

Modelling of the reactive sputtering process with non-uniform discharge current density and different temperature conditions

The majority of current models of the reactive magnetron sputtering assume a uniform shape of the discharge current density and the same temperature near the target and the substrate. However, in the real experimental set-up, the presence of the magnetic field causes high density plasma to form in front of the cathode in the shape of a toroid. Consequently, the discharge current density is laterally non-uniform. In addition to this, the heating of the background gas by sputtered particles, which is usually referred to as the gas rarefaction, plays an important role. This paper presents an extended model of the reactive magnetron sputtering that assumes the non-uniform discharge current density and which accommodates the gas rarefaction effect. It is devoted mainly to the study of the behaviour of the reactive sputtering rather that to the prediction of the coating properties. Outputs of this model are compared with those that assume uniform discharge current density and uniform temperature profile in the deposition chamber. Particular attention is paid to the modelling of the radial variation of the target composition near transitions from the metallic to the compound mode and vice versa. A study of the target utilization in the metallic and compound mode is performed for two different discharge current density profiles corresponding to typical two pole and multipole magnetics available on the market now. Different shapes of the discharge current density were tested. Finally, hysteresis curves are plotted for various temperature conditions in the reactor.

Relevance of the structure of time‐resolved spectral output to light‐tissue interaction using intense pulsed light (IPL)

High quality IPLs can offer simple, safe and effective treatments for long-term hair removal, removal of benign vascular and pigmented skin abnormalities, skin rejuvenation and acne treatments. Significant differences in clinical outcome have been recorded among different free-discharge and constant current IPLs despite identical settings. We investigated the differences in optical output of 19 IPLs in normal clinical use in the UK to evaluate spectral output, energy density values and pulse structure and propose a correlation between light-tissue interaction and spectral output as measured by time-resolved photo-spectrometry. Using a fast spectrometer, generating 1,000 full spectral scans per second, time resolved spectral data of IPL outputs was captured with a resolution of 0.035 nm. IPL spectral outputs were calculated and graphically modelled using MathCAD software for comparison. Several IPLs, which professed matching of pulse durations to the thermal relaxation times of specific follicular or vascular targets were shown to have effective pulse durations that were vastly shorter than those claimed. Some IPLs claiming 'square pulse' characteristics failed to show constant spectral output across the duration of the pulse or sub-pulses. This study provides a suitable method to determine accurately key parameters of the emitted light pulses from IPLs and confirms the direct correlation between the electrical discharge current profile and the output energy profile. The differences measured between first generation free discharge systems and modern square pulse systems may have important clinical consequences in terms of different light-tissue interactions and hence clinical efficacy and safety. IPL manufacturers should provide time-resolved spectroscopy graphs to users.