Single-particle Model Research Articles

Nuclear Level Density in the X(888, 274) System

Bevelacqua Resources, 7531 Flint Crossing Circle SE, Owens Cross Roads, AL 35763 USA Bevelresou@aol.com Level densities for the X(788, 274) system are calculated using a single particle methodology incorporating the Rost interaction and pairing interaction of Blomqvist and Wahlborn. Energy level fluctuations are smoothed utilizing the constant temperature, power law, and equidistant model functional forms. The constant temperature relationship is used to model the density and total number of energy levels. The total number of levels is also compared with the predictions of the power law and equidistant models. Systematics of level densities for theoretical superheavy nuclei are discussed. Key Words: Nuclear Level Density, Single Particle Model, Constant Temperature Model, Power Law Model, Equidistant Model, Superheavy Nuclei.

An electrochemical-thermal coupling model for lithium-ion battery state-of-charge estimation with improve dual particle filter framework

Lithium-ion battery state-of-charge (SOC) estimation plays an indispensable role in the battery management system functionalities. To achieve SOC estimation with high accuracy, a precise battery model is required. Among various battery models, electrochemical model (EM) stands out for its ability to provide insights into the electrochemical mechanisms of the battery. However, the model parameters will vary with the battery running and aging, besides, the model-based methods cannot well handle the model errors. Therefore, to ensure estimation accuracy, selecting a more insightful battery model and a more effective estimation algorithm becomes crucial. This paper proposes an electrochemical model-based SOC estimation method with online parameter adjustment and an optimized algorithm. Firstly, a single particle model (SPM) with electrolyte and thermal dynamics is introduced and simplified by an approximate solution. Secondly, the particle swarm optimization (PSO) method is used to solve the problem of the particle filter (PF) algorithm, such as particle degradation and particle diversity decrease. Finally, during SOC estimating, a double-scale dual particle filter (D-PF) is utilized to adjust parameters as battery aging and temperature changes. The battery tests are applied under different working conditions and temperatures. Experimental results show that the error of SOC estimation reduces significantly compared to the previous electrochemical model-based method, and the proposed method has a higher accuracy and effectiveness.

A New Battery SOC/SOH/eSOH Estimation Method Using a PBM and Interconnected SPKFs: Part I. SOC and Internal Variable Estimation

Battery management systems (BMSs) are required to estimate many non-measurable values that describe the actual operating condition of batteries; such as the state of charge (SOC) or the state of health (SOH). In order to improve this evaluation, many physical states and parameters can be estimated using physics-based models (PBMs). These estimates could be used to improve the control and prognosis of batteries. In a series of papers we propose a new method to estimate the internal physical states, the SOC, the SOH and the electrode-specific state of health (eSOH) parameters of a lithium-ion battery, using interconnected sigma-point Kalman filters (SPKFs) and a single-particle model with electrolyte dynamics (SPMe). This first paper focuses on state estimation for non-aged cells. To begin, we describe and validate our electrochemical model against a high-fidelity P2D model. The interconnected SPKF algorithm is then described and the observability of our system is analyzed, showing that the interconnected estimator approach improves an observability measure of the system. Finally, the results of the estimator are discussed, comparing the estimated variables with the truth values under initialization, measurement and modeling uncertainties. The results show that the algorithm can estimate the internal battery states with high accuracy.

Decay Characteristics of Neutron Excess Potassium Nuclei

In neutron star mergers, neutron excess nuclei and the r-process are important factors governing the production of heavy nuclear systems. A single-particle model evaluation of potassium nuclei suggests that the heaviest Z = 19 nucleus will have mass 69 with filling of the 1g9/2 neutron shell. A = 55 – 69 potassium isotopes have limited experimental half-life data, but the model predicts beta decay half-lives in the range of 0.315 – 9.23 ms. Based on previous calculations for Z = 9 - 18, 20, 26, and 30 systems and comparisons to the 55K – 59K, 61K, and 63K calculations, summarized in the Japanese Nuclear Data Compilation, the single-particle model results likely overestimate the half-lives of A = 55 – 69 neutron excess potassium nuclei.

Lithium-Plating Suppressed and Deep Deterministic Policy Gradient-Based Energy Management Strategy

Lithium plating and the formation of lithium dendrite are the main causes of battery internal short circuit and thermal runaway. This paper proposes a deep reinforcement learning-based energy management framework for power-split hybrid electric vehicles (HEVs) which effectively suppresses lithium plating inside batteries. First, single particle model with electrolyte (SPMe) and lithium-plating model are integrated into deep deterministic policy gradient based energy management strategy (DDPG-EMS) for the first time. Second, hybrid particle swarm optimization grey wolf optimizer (HPSOGWO) algorithm is applied to the parameter identification of the two models for the first time to ensure the optimal trade-off between convergence speed and identification accuracy. Third, the superiority of the control strategy is achieved by balancing among fuel consumption, lithium plating occurrence, and SOC sustainability effect under different initial cell temperatures, and verified by comparison with deep Q-network (DQN). The training and validation results both show that the proposed strategy with lithium plating considered not only slows down battery degradation by redressing the charging current in time before the negative overpotential drops to zero, but also reduces the fuel consumption cost as well as total cost.

Noise-induced collective actuation in active solids.

Collective actuation describes the spontaneous synchronized oscillations taking place in active solids when the elasto-active feedback, which generically couples the reorientation of the active forces and the elastic stress, is large enough. In the absence of noise, collective actuation takes the form of a strong condensation of the dynamics on a specific pair of modes and their generalized harmonics. Here we report experiments conducted with centimetric active elastic structures, where collective oscillation takes place along the single lowest energy mode of the system, gapped from the other modes because of the system's geometry. Combining the numerical and theoretical analysis of an agent-based model, we demonstrate that this form of collective actuation is noise-induced. The effect of the noise is first analyzed in a single-particle toy model that reveals the interplay between the noise and the specific structure of the phase space. We then show that in the continuous limit, any finite amount of noise turns this new form of transition to collective actuation into a bona fide supercritical Hopf bifurcation.

Optimization of the Bulk Refractive Index Sensitivity of Silver NanoPrisms

AbstractThe sensitivity and optical properties of silver nanoprisms (triangular plates with round‐truncated corners) are investigated in this paper. Results of boundary element method simulations are compared with experimental results and literature data. Based on electron microscopy images of the synthesized nanoprisms, a single‐particle model is set up for simulations with three running parameters: edge length, thickness, and roundness (defined as the radius of the circumscribed circle divided by the edge length). These geometric parameters can be optimized during chemical synthesis to create sensors with improved sensitivity. The effect of biomolecular layers is also investigated. As a novel approach to improve the agreement between the simulated and experimentally measured extinction spectra, the single‐particle model is extended to consider the variation of the prisms' parameters in the form of distributions. The resulting extinction cross‐section spectra correspond well with the experimental data. The calculated bulk refractive index sensitivity is 670 nm/RIU (RIU stands for refractive index unit) for the single particle model (length = 150 nm, thickness = 10 nm, and roundness = 0.1), while (690 ± 5) nm/RIU for the extended model. The presented model and obtained relations between sensitivity and geometry can be effectively used to design and optimize the fabrication technologies for silver nanoprism‐based sensing applications.

A novel order-reduced thermal-coupling electrochemical model for lithium-ion batteries

Although the single-particle model enhanced with electrolyte dynamics (SPMe) is simplified from the pseudo-two-dimensional (P2D) electrochemical model for lithium-ion batteries, it is difficult to solve the partial differential equations of solid–liquid phases in real-time applications. Moreover, working temperatures have a heavy impact on the battery behavior. Hence, a thermal-coupling SPMe is constructed. Herein, a lumped thermal model is established to estimate battery temperatures. The order of the SPMe model is reduced by using both transfer functions and truncation techniques and merged with Arrhenius equations for thermal effects. The polarization voltage drop is then modified through the use of test data because its original model is unreliable theoretically. Finally, the coupling-model parameters are extracted using genetic algorithms. Experimental results demonstrate that the proposed model produces average errors of about 42 mV under 15 constant current conditions and 15 mV under nine dynamic conditions, respectively. This new electrochemical-thermal coupling model is reliable and expected to be used for onboard applications.

Single-Particle Model of Li-ion Battery – Model Calibration and Validation against Real Data in an Electric Vehicular Application

This paper investigates the problem of calibration and validation of a battery electrochemical model as a mandatory step towards accurate estimation of battery important variables, like state of charge (SoC) and state of health (SoH). Here, the Single Particle Model (SPM) is considered, which mathematically describes the battery internal governing phenomena by means of parabolic partial differential equations (PDEs), but whose parameters are notoriously difficult to measure or estimate. After suitable approximation of this model through a linear finite-dimensional model, a systematic procedure of SPM calibration is here proposed and validated against real data issued from battery cycling in an electric vehicular application, i.e., under standard driving cycle scenarios. In a novel approach of SoC estimation, the suitably calibrated SPM, together with measures of voltage and current, allow to analytically connect the internal spatially distributed ions’ concentrations to the equilibrium concentration, which, at its turn, is an image of battery SoC. Results suggest that SPM can reliably predict the battery internal ions’ concentrations and be further used for SoC accurate estimation.

Online monitoring of the burning characteristics of single pulverized coal particle in O2/N2 and O2/CO2 environments

The objective of this study is to compare the ignition and burning characteristics such as burnout times of volatiles and char, and ignition mechanism of single pul?verized coal particle burning in air (O2/N2) and oxy (O2/CO2) environments. An entrained flow reactor with photo detector has been employed for this study. This technique involves online monitoring of radiation emission from an individual coal particle. Individual particles of sub-bituminous or lignite coal particle with size in the ranges of 106-125 micron and 180-212 micron particles have been injected into an air (O2/N2) or oxy (O2/CO2) environment inside an entrained flow reactor. The oxygen concentration in the ambient gas is varied between 10%-50% by volume. The volatile and char burnout times have been obtained from time histories estimated from the radiation emitted by the particle. The results show an obvious increase in the burning rates of volatiles and char with increasing oxygen concentration. Further, when compared to N2 atmosphere, the burning rate and radiation intensity are found to be lower in CO2 atmosphere, especially at lower oxygen concentrations. An indication of the possible ignition mechanism has been outlined from the percentages of single and double peaks observed in the radiation emission histories. The complete experimental investigation shows that the burning rates of both volatiles and char are predominantly affected by the oxygen concentration, particle size and gas temperature. The theoretical results from a single particle model has been used to validate the trends and the combustion durations obtained from the present experimental study and is published as a separate manuscript.

Inverse problem in the conditioning of Markov processes on trajectory observables: what canonical conditionings can connect two given Markov generators?

In the field of large deviations for stochastic dynamics, the canonical conditioning of a given Markov process with respect to a given time-local trajectory observable over a large time-window has attracted a lot of interest recently. In the present paper, we analyze the following inverse problem: when two Markov generators are given, is it possible to connect them via some canonical conditioning and to construct the corresponding time-local trajectory observable? We focus on continuous-time Markov processes and obtain the following necessary and sufficient conditions: (i) for continuous-time Markov jump processes, the two generators should involve the same possible elementary jumps in configuration space, i.e. only the values of the corresponding rates can differ; (ii) for diffusion processes, the two Fokker–Planck generators should involve the same diffusion coefficients, i.e. only the two forces can differ. In both settings, we then construct explicitly the various time-local trajectory observables that can be used to connect the two given generators via canonical conditioning. This general framework is illustrated with various applications involving a single particle or many-body spin models. In particular, we describe several examples to show how non-equilibrium Markov processes with non-vanishing steady currents can be interpreted as the canonical conditionings of detailed-balance processes with respect to explicit time-local trajectory observables.

Asymptotic normalization coefficient for the study of the mirror nuclei 7Li and 7Be

We present a theoretical study of the nuclear structure of the mirror nuclei 7Li and 7Be by using the asymptotic normalization coefficient (ANC) method through the transfer reactions 6Li(d,p)7Li and 6Li(3He,d)7Be. For these reactions, the elastic scattering and the transfer angular distributions were calculated considering cluster structures for the residual nucleus and the transfer particle. With the use of the Fresco code, we found the optical model parameters which reproduce the experimental data for the elastic and the transfer angular distributions and we got the ANC for the single particle model and the spectroscopic factor on the ground and the first excited states of each nuclei. This methodology allows to calculate the nuclear ANC which describes the peripheral behavior of both nuclei on their bound states.

System Identification for Battery State Prediction and Lifespan Estimation

In this paper, a nonlinear system Identification method, wavelet-network-based Nonlinear Auto-Regressive Exogenous (NLARX) approach, is employed for battery state estimation and lifespan estimation. More specifically, three key battery parameters and health metrics, including temperature, voltage and State of Charge (SOC), are estimated and these parameters are essential for condition or state monitoring. Further, State of Health (SOH), crucial for forecasting the battery remaining useful life, is also predicted. Two open datasets are used to train and validated the performance of the proposed method. For temperature and voltage forecasting, the NLARX model outperforms the existing Thermal Single Particle Model with electrolyte (TSPMe) for prediction horizons under 600 seconds. In SOC estimations, the NLARX method produces consistent 15-second ahead prediction results even only using a small percentage of training data, while the SOH estimation, the proposed metho provides precise SOH variation prediction for 400 post cycles with less than 10% of the batterys life for training. Extensive results demonstrates that the NLARX model’s promise for the precise prediction of key battery parameters and health metrics and it can be used as a useful tool for battery fault detection and remaining useful life prediction.

Structural Analysis of Membrane-associated Forms of Helicobacter pylori VacA Toxin

Helicobacter pylori colonizes the stomach in about half of the human population, leading to an increased risk of peptic ulcer disease and gastric cancer. H. pylori secretes an 88 kDa VacA toxin that contributes to pathogenesis. VacA assembles into oligomeric complexes in solution and forms anion-selective channels in cell membranes. Cryo-electron microscopy (cryo-EM) analyses of VacA oligomers in solution provided insights into VacA oligomerization but failed to reveal the structure of the hydrophobic N-terminal region predicted to be a pore-forming domain. In this study, we incubated VacA with liposomes and used single particle cryo-EM to analyze detergent-extracted VacA oligomers. A 3D structure of detergent-solubilized VacA hexamers revealed the presence of six α-helices extending from the center of the oligomers, a feature not observed in previous studies of water-soluble VacA oligomers. Cryo-electron tomography analysis and 2D averages of VacA associated with liposomes confirmed that central regions of the membrane-associated VacA oligomers can insert into the lipid bilayer. However, insertion is heterogenous, with some membrane-associated oligomers appearing only partially inserted and others sitting on top of the bilayer. These studies indicate that VacA undergoes a conformational change when contacting the membrane and reveal an α-helical region positioned to extend into the membrane. Although the reported VacA 3D structure does not represent a selective anion channel, our combined single particle 3D analysis, cryo-electron tomography, and modeling allow us to propose a model for the structural organization of the VacA N-terminus in the context of a hexamer as it inserts into the membrane.

Electrochemical Model-Based State-Space Approach for Real-Time Parameter Estimation of Lithium-Ion Batteries

Lithium-ion batteries are widely used in various applications, including electric vehicles and renewable energy storage systems, due to their high energy density, long cycle life, and low self-discharge rate. Accurate estimation of battery state of charge (SOC) and state of health (SOH) is crucial for their safe and efficient operation.In this study, we propose a model-based state-space solver for real-time monitoring of lithium-ion batteries under various operating conditions. The state-space model simplifies a single-particle model and enables simulations considering the current state of the battery using a Kalman filter. The model includes a one-dimensional Single Particle Model with Electrolyte (SPMe) and uses eigenfunction expansion to solve the mass conservation equation for the electrolyte phase. The final form of the state-space model for the solid and electrolyte phase includes a voltage equation using lithium-ion concentration. The proposed solver was validated by comparing it with the SPMe solver, and the results agree well.To achieve on-line updating of the battery model parameters, we propose a multi-scale parameter adaptive method based on dual Kalman filters for estimating the SPMe model parameters. The fast time-varying parameters are separated from the slowly time-varying parameters, and then each group is estimated by a different Kalman filter with a different time constant. This method enables the estimation of both the SOC and all parameters, including the SPMe model parameters. Additionally, we provide a parameter adjustment method for dual extended Kalman filters when estimating multiple parameters.The proposed method reduces the influence of the initial state error on the algorithm and improves its robustness. Experimental results demonstrate that the accuracy of the algorithm is improved by separating the fast and slow parameters and estimating them with different Kalman filters. The proposed solver can be used with Kalman filters and the like in an onboard environment due to its fast calculation speed. Figure 1

Evolution of microstructure and mechanical properties of cold spray additive manufactured aluminum deposit on copper substrate

The microstructure and mechanical properties of cold sprayed aluminum coatings on copper substrates were studied in this paper. The impact and deposition mechanisms of single and multiple particles during cold spraying were investigated using finite element modeling. The results of single particle model show that the maximum equivalent plastic strain is located at the contact surface between particle and substrate, decreasing from the jet site to the center of particles, and the equivalent plastic strain at the top of particles is almost zero. The results of multi-particle model show that the regions with large equivalent plastic strain include the contact surface between particle and substrate and the region between particles. The experimental results show that particle deposition has a significant impact on compaction during the spraying process. When the spraying distance is set to a fixed value of 30 mm, and the gas pressure is 2.2 MPa, 2.6 MPa, 3.0 MPa and 3.4 MPa, the coating porosity is always stable at about 1 %. The bonding strength of coating increases with the increase of gas pressure. When the gas pressure increases from 2.2 MPa to 3.4 MPa, the coating bonding strength has increased by 142.0 %, and the maximum value of bonding strength between the coating and the substrate is 16.9 MPa. When the gas pressure is 3.4 MPa with the gas preheating temperature of 300 °C, and the spraying distance is 20 mm, 30 mm and 40 mm, the coating porosity is 1.74 %, 0.95 % and 1.21 %, respectively. The maximum value of bonding strength between the coating and the substrate is 21.3 MPa under the spraying distance of 30 mm.

Beyond Battery State of Charge Estimation: Observer for Electrode-Level State and Cyclable Lithium With Electrolyte Dynamics

This paper presents a provably convergent battery estimation scheme based on a single particle model with electrolyte dynamics (SPMe), by proposing a systematic methodology to estimate critical information such as electrode-level states, electrolyte dynamics, and cyclable lithium. Electrode-level state estimation suffers from weak observability originating from two standalone electrode dynamics, which is then aggravated by the addition of electrolyte dynamics. This lack of observability can be alleviated by exploiting lithium inventory conservation enabled by Kalman decomposition, allowing one to separate out the unobservable subspace. Assuming the knowledge of cyclable lithium, a nonlinear state observer with provable convergence can be constructed for the SPMe model, using voltage and current measurements. To relax this strong assumption, a sensitivity-based parameter estimation scheme is also deployed to track cyclable lithium – a crucial physical variable for capacity fade. Ultimately, the estimation framework can perform finer monitoring and diagnosis of battery charge and health down to the level of individual electrode and the electrolyte. Experimental validation demonstrates <1% estimation error for cyclable lithium inventory. Solid phase lithium concentration estimates, especially in the negative electrode, can be sensitive to disturbances in cyclable lithium.

Comparative Analysis of Numerical Methods for Lithium-Ion Battery Electrochemical Modeling

Electrochemical models provide insights into the battery internal states and have become powerful tools for battery design and management. These models consist of partial differential equations (PDEs) that are solved numerically. In this paper, we compare two spatial discretization methods commonly used to numerically solve the governing PDEs in the context of Lithium ion batteries, namely finite difference method (FDM) and finite volume method (FVM) in terms of model accuracy and mass conservation guarantee. First, we provide the mathematical details to carry out the spatial discretization for both FDM and FVM to solve the battery single particle model (SPM). SPM parameters are identified from experimental data, and sensitivity analysis is conducted to study parameter identifiability under different current input profiles, followed by model accuracy and mass conservation analysis of the two numerical schemes. Leveraging the third order Hermite extrapolation approach, an enhanced FVM scheme is proposed in this paper to improve the model accuracy of standard FVM which relies on linear extrapolation. This paper shows that the FVM scheme with Hermite extrapolation leads to accurate and robust control-oriented battery model while guaranteeing mass conservation and high accuracy.

A computational analysis of effects of electrode thickness on the energy density of lithium-ion batteries

Elevated energy density is a prime requirement for many lithium-ion battery (LIB) applications, including electric vehicles (EVs). At the cell level, the enhanced energy density of LIBs is achievable by designing thicker electrodes, which decreases the weight of the inactive materials. This work proposed a reduced order electrochemical model (ROEM), which is an extended single particle model (extended SPM), by incorporating the existing order reduction techniques of Pseudo-Two-Dimensional (P2D) model. The proposed model was used to predict the energy density values of a nickel cobalt manganese (NCM) cell with respect to the cathode thickness. The validation of the model was performed by using MATLAB at 0.05C, 1C, 2C, 3C and 5C rates by comparing the simulation results with the experimental data obtained from the Stanford Energy Control Laboratory. The verified model was then used to simulate the effect of varying thicknesses of the cathode on energy density of the NCM cell at C/5, C/2, 1C, and 2C rates of discharge. The simulation results illustrate that increasing the cathode thickness has a profound positive impact on the energy density of the cell. The prediction of the model demonstrates a total of around 20 % increase in energy density for the increment of cathode thickness from 75 μm to 600 μm. The proposed model can replicate energy density for thicker electrodes up to 2C rate reasonably correctly. However, the incorporation of thermal characteristics and other influential parameters of LIB cells into the proposed model can yield a better prediction of energy density of thicker electrodes at higher C-rates, which is recommended for further study.

Decay Characteristics of Neutron Excess Argon Nuclei

Bevelacqua Resources, 7531 Flint Crossing Circle SE, Owens Cross Roads, AL 35763 USA bevelresou@aol.com In neutron star mergers, neutron excess nuclei and the r-process are important factors governing the production of heavier nuclear systems. A single-particle model evaluation of argon nuclei suggests that the heaviest Z = 18 nucleus will have mass 69 with filling of the 3s1/2 neutron shell. A = 51 – 69 argon isotopes have limited experimental half-life data, but the model predicts beta decay half-lives in the range of 0.473 – 20.3 ms. Based on previous calculations for Z = 9 - 17, 20, 26, and 30 systems and comparisons to the 51Ar - 56Ar, 58Ar, and 60Ar calculations, summarized in the Japanese Nuclear Data Compilation, the single-particle model results likely overestimate the half-lives of A = 51 – 69 neutron excess argon nuclei.