Current Profile Research Articles

A Comparative Study of Numerical Methods for Lithium-Ion Battery Electrochemical Modeling

Lithium-ion battery (LIB) with high specific energy density and long cycle life is one widely used energy storage technology. In order to ensure longevity and safety of the battery system, a battery management system (BMS) that relies on battery model is required. With the development of BMS technique, more attention has been given to electrochemical models that provide insights into the battery internal states. In general, battery electrochemical models consist of partial differential equations (PDEs) describing the thermodynamic and electrochemical process inside the cell, and those PDEs can only be solved numerically due to their complexity.Existing battery simulation software utilize numerical methods such as finite difference method (FDM), finite volume method (FVM), and finite element method (FEM) to solve PDEs in battery models. However, there are little discussions for the selection of a specific numerical methods. In our recent study [1], we compare two spatial discretization methods commonly used to numerically solve the governing PDEs of LIB electrochemical models, namely FDM and FVM, in terms of model accuracy and mass conservation guarantee. First, we provide the mathematical details of the spatial discretization process for both FDM and FVM to solve the battery single particle model (SPM), this result has not been shown in any publication before. Second, we propose a new Hermite extrapolation based FVM scheme leading to higher accuracy when compared to the normally used linear extrapolation based FVM scheme. Then, SPM parameters are identified using experimental data, and a comprehensive parameter sensitivity analysis is conducted under different current input profiles to study parameter identifiability. Finally, model accuracy and mass conservation analysis of the FDM and FVM schemes are presented. Our study shows that the FVM scheme with Hermite extrapolation leads to accurate and robust control-oriented battery model while guaranteeing mass conservation and high accuracy. Also, the proposed new FVM scheme can be extended to solve other battery models, such as SPM model with electrolyte and Doyle-Fuller-Newman model. Moreover, we provide findings of mass conservation analysis for FDM and FVM schemes, which we hope can facilitate BMS model selection.

The Voltage-Based Implementation of the Tunneling Mechanism in Aging Models for Lithium-Ion Batteries

Lithium-ion batteries (LIBs) have become one of the most crucial power sources for electronics and electric vehicles due to high energy density, long cycle life, environmental friendliness, etc. Precise explanation and prediction of the aging behavior of LIBs is quickly becoming a hotspot. A solid electrolyte interface (SEI) growth is regarded as the dominant factor of capacity losses in LIBs. However, the previous SEI growth and aging models were based on energy levels. That means the energy barrier related to the Fermi level of SEI and electrode was used as the input parameter. It is not straightforward to implement, especially for industrial applications. Furthermore, the state-of-charge (SoC) and the energy barrier were usually assumed to be constant during (dis)charging, contradicting common sense and affecting the calculations' accuracy.The present work proposes and experimentally validates an advanced voltage-based aging model. The voltage of the anode electrode is adopted as an input parameter in this model. It is easier to introduce dependence of aging on the voltage/SoC of the electrode with this model. It is found that deep delithiation of the anodes and lower SoC of LIBs will mitigate electron tunneling, lower SEI growth, and reduce calendar aging. Commonly used anode materials, such as graphite, Li4Ti5O12, and blended Si/C, are presented as interesting cases to investigate the effects of different electrode materials on the tunneling current profiles during (dis)charging for the first time. The model is instructive and can predict aging in LIBs with various electrode materials. This model and related analyses are beneficial for further optimizing LIB's electronic and electrode properties and as interesting limiting cases for testing battery modeling software.

Diagnosing NMC Battery Aging Modes Using Digital Twin

Traditional lithium-ion battery (LiB) modeling does not provide sufficient information to accurately verify battery performance under real-time dynamic operating conditions, particularly when considering various aging modes and mechanisms. This paper proposes a lithium-ion battery digital twin that can capture real-time data and integrate the strong coupling between SEI layer growth, anode crack propagation, drying up of Electrolytes, and lithium plating. It can be utilized to predict the degradation behavior, voltage-current profiles in dynamic aging conditions, and analyze the lithium inventory loss (LLI) of Nickel-Manganese-Cobalt-Oxide (NMC) lithium-ion batteries.Commercially available 18650 cylindrical NMC Samsung battery cells were investigated. The cathode and anode materials are LiNi0.5Co0.2Mn0.3O2 and synthetic graphite, respectively. Four dynamic aging test cases using 1C, 1.3C, MC (multistep charging), and 2C, were designed and performed at 25 °C. The reference performance test (RPT) was repeated every 100 full equivalent cycles (FECs) until the cells reached 20 % capacity fade. Figure 1 shows the real-time voltage and current profile captured and predicted by the digital twin.The model takes into account LLI induced by SEI growth and lithium plating, aligning with the LLI calibrated from the differential voltage (DV) endpoint shift (Figure 1h). Based on the model results, SEI growth is found to be the primary contributor to capacity loss before 500 FECs. SEI-induced degradation is minimal under the MC protocol, with the capacity loss varying by less than 5.22% across four test cases (Figure 1e). After 500 FECs, anode cracking becomes a non-linear accelerated aging factor. The crack propagation stabilizes in the initial 200 FECs, followed by unstable accelerated growth leading to capacity loss. Subsequently, a rapid acceleration in capacity loss occurs after 700 FECs (Figure 1f). The growth of anode cracks under the MC aging protocol has a much slower effect on capacity loss compared to the 2C and 1.3C before 500 FECs, as strongly confirmed by the post-mortem analysis. In addition, the lithium plating phenomenon triggers two orders of magnitude less capacity loss than SEI growth and cracking at 25 °C. The MC protocol demonstrates an effective reduction in lithium plating capacity loss compared to the other three traditional charging protocols (Figure 1g). This digital twin can represent a firm physical foundation for future physics-informed machine learning development to predict LiBs degradation behavior.Figure 1. Real-time voltage and current captured and predicted by our designed digital twin under (a) 1C-WLTC, (b)1.3C-WLTC, (c) MC-WLTC, and (d) 2C-WLTC aging profiles. Additionally, (f) illustrates capacity loss density for the four protocols attributed to (e) SEI film growth, (f) anode crack propagation, (g) Li plating, and (h) LLI prediction by the digital twin and DV measurement. Figure 1

Integration of Distributed Generations and electric vehicles in the distribution system

Power quality challenges are a major problem these days because of the significant increase in load demand and the existence of various reasons for disruptions in the distribution system. A voltage sag or a decrease in the magnitude of the line voltage can be brought on by rapid fluctuations in load or distribution system issues. Enhancing the grid current profile, reducing grid voltage sag, and improving other performances can all be achieved through the combination of electric vehicles with distribution generation systems. In this paper, the integration of Distributed Generation with Electric Vehicles in the distribution system has been done to enhance system performances such as true power loss index, imaginary power loss index, voltage deviation index, short circuit current index, cost function, and environmental impact reduction index for 16 bus distribution system in constant impedance, constant current, and constant power load models, or ZIP load models through proper location, sizing, coordinated controlling, and types of Distributed Generation and Electric Vehicle pairs for both charging and discharging modes by adopting Hybrid Genetic Algorithm - Monte Carlo Simulation optimization methodologies. This study's primary goal is to determine the optimal distribution generation and electric vehicle pairing to improve the aforementioned parameters, which will assist researchers in their future planning. Researchers, industry professionals, scientists, and individuals working with Distributed Generation and Electric vehicles in the smart grid will find this statement to be of great use.

Assessing uncertainties of a remote sensing-based discharge reflectance model for applications to large rivers of the Congo Basin

ABSTRACT Adequate monitoring of river discharge is crucial for effective water resource management. However, this objective remains difficult to achieve in the context of large and ungauged river basins. This study assesses the performance of remote sensing applications for discharge monitoring in the lower reach of the Congo River, where daily discharge information is required to support many water resource operations. The approach is based on the use of Moderate Resolution Imaging Spectroradiometer (MODIS) remote sensing imagery to produce a daily time series of a ratio of reflectance values (C/M) for discharge monitoring. The validation of the approach is performed based on three-year water level data collected at the outlet gauging site and limited in situ Acoustic Doppler Current Profiler (ADCP) cross-section measurements for high and low flow seasons. The simulated discharge closely matches the observed values and falls within acceptable ranges, with errors below 10% and Nash-Sutcliffe coefficients ranging from 0.65 to 0.76 for ADCP and gauging station, respectively.

Optimization of the cruising speed for high-speed trains to reduce energy consumed by motion resistances

As one of the effective measures to reduce energy consumption of high-speed railway, train operation control has gained much attention because of its cost-effectiveness. Conventional approaches to eco-driving generally rely on single-mass train models and lack a thorough investigation into the vehicle cruising process, which can overlook real-world operation conditions and hinder further improvements in energy efficiency. To address these issues, this paper proposes a comprehensive framework for optimizing the cruising speed of high-speed trains (HSTs) based on track conditions to reduce energy consumed by motion resistances (ECMR). Firstly, a multibody dynamics simulation model of the HST is developed and validated to accurately evaluate ECMR under a range of operation scenarios. Simulation results show that the impact of speed on ECMR varies considerably according to track characteristics. Subsequently, a neural network is constructed to derive the regressive relationship between operation conditions and ECMR. On this basis, an optimization model for the cruising speed under different track conditions is formulated to minimize the ECMR of a railway segment with a given operation time. Finally, the effectiveness of the model is validated through case studies, which demonstrate that ECMR can be reduced by up to 27.85% compared to traditional control strategies. The proposed optimization method can be integrated with current train speed profiles to further enhance energy efficiency. This framework has broad applicability for addressing engineering problems related to train operation optimization.

Potential of Pan-Tuberculosis Treatment to Drive Emergence of Novel Resistance.

New tuberculosis (TB) drugs with little existing antimicrobial resistance enable a pan-TB treatment regimen, intended for universal use without prior drug-susceptibility testing. However, widespread use of such a regimen could contribute to an increasing prevalence of antimicrobial resistance, potentially rendering the pan-TB regimen ineffective or driving clinically problematic patterns of resistance. We developed a model of multiple sequential TB patient cohorts to compare treatment outcomes between continued use of current standards of care (guided by rifampin-susceptibility testing) and a hypothetical pan-TB approach. A pan-TB regimen that met current target profiles was likely to initially outperform the standard of care; however, a rising prevalence of transmitted resistance to component drugs could make underperformance likely among subsequent cohorts. Although the pan-TB approach led to an increased prevalence of resistance to novel drugs, it was unlikely to cause accumulation of concurrent resistance to novel drugs and current first-line drugs.

Living Conditions of the Gond Community in Veerdandi Village of Adilabad District in Telangana

The Gond community, one of India’s most prominent tribal groups, boasts a rich history and cultural heritage that dates back to the 9th century AD. Their vibrant art, traditional practices, and festivals were renowned, and they once ruled several prominent kingdoms in the hill Gondwana region. However, the British colonial period disrupted their traditional rule, leading to the current socioeconomic profile of Veerdandi village in Telangana’s Adilabad district, revealing significant challenges. Predominantly male (80%), with a large segment in the 25-35 age group (36.7%), the village faces problems such as low literacy rates (55%), lack of education and health facilities, and heavy reliance on agriculture and labour. Modern agricultural technology is limited despite infrastructure such as roads and emergency transport. Demographic and socioeconomic conditions highlight significant challenges in health, education, and living standards. 56.7% of households don’t possess sanitation facilities in their houses and 78.3% are suffering from nutritional deficiency. This underscores the urgent need for government schemes to address the multifaceted problems faced by tribal communities in Telangana. The Pearson Chi-Square value is 18.400. The p-value (.000) was statistically significant in the government support schemes and in preventing tribes’ malnutrition, health issues, and poverty in the study.

Effect of driving current profile on acceleration efficiency of electromagnetic railgun

Effect of driving current profile on acceleration efficiency of electromagnetic railgun

Advanced State of Charge Estimation Using Deep Neural Network, Gated Recurrent Unit, and Long Short-Term Memory Models for Lithium-Ion Batteries under Aging and Temperature Conditions

Accurate estimation of the state of charge (SoC) of lithium-ion batteries is crucial for battery management systems, particularly in electric vehicle (EV) applications where real-time monitoring ensures safe and robust operation. This study introduces three advanced algorithms to estimate the SoC: deep neural network (DNN), gated recurrent unit (GRU), and long short-term memory (LSTM). The DNN, GRU, and LSTM models are trained and validated using laboratory data from a lithium-ion 18650 battery and simulation data from Matlab/Simulink for a LiCoO2 battery cell. These models are designed to account for varying temperatures during charge/discharge cycles and the effects of battery aging due to cycling. This paper is the first to estimate the SoC by a deep neural network using a variable current profile that provides the SoC curve during both the charge and discharge phases. The DNN model is implemented in Matlab/Simulink, featuring customizable activation functions, multiple hidden layers, and a variable number of neurons per layer, thus providing flexibility and robustness in the SoC estimation. This approach uniquely integrates temperature and aging effects into the input features, setting it apart from existing methodologies that typically focus only on voltage, current, and temperature. The performance of the DNN model is benchmarked against the GRU and LSTM models, demonstrating superior accuracy with a maximum error of less than 2.5%. This study highlights the effectiveness of the DNN algorithm in providing a reliable SoC estimation under diverse operating conditions, showcasing its potential for enhancing battery management in EV applications.

Sleep, chronotype, social jetlag, and mental health in resident physicians: a cross-sectional study.

Social jetlag, the difference between imposed and endogenous sleep schedules, may be detrimental to resident physicians' health. The current profiles of sleep habits, particularly the differences between workdays and free days, are unknown in that population. This cross-sectional study of Quebec resident physicians aimed at assessing sleep habits on workdays and free days, and predictors of social jetlag. Residents were recruited via their residency programs and social media to complete an online questionnaire. Measures included means of sleep duration and timing, chronotype, sleep debt, sleep disturbances, and social jetlag. A range of socio-demographic variables, lifestyle characteristics, and mental health indicators were examined as predictors of severe social jet lag using logistic regressions. A total of 492 residents were included in the study (mean [SD] age, 27.6 [3.6] years; 330 women [67.1%]). The mean sleep duration was 7.15h (95%CI, 7.02-7.28h) on workdays and 8.36h (95%CI, 8.18-8.54h) on free days. The mean sleep debt was 1.59h (95% CI, 1.37-1.81h), and mean social jetlag was 1.37h (95% CI, 1.28-1.47h), with 31.9% (95% CI, 25.0%-39.6%) of residents experiencing ≥2 hours of sleep debt, and 21.8% (95% CI, 16.5%-28.3%) experiencing severe social jetlag. The prevalence of sleep disturbances was 51.7% (95% CI, 44.4%-58.8%). Severe social jetlag was associated with earlier stage of training, later chronotype, decreased physical activity, increased sleep debt, and depressive symptoms. Many residents experience severe social jetlag, chronic sleep deprivation and sleep disturbances. Importantly, severe social jetlag was associated with depressive symptoms, suggesting a potential intervention target for promoting resident mental health.

Multiplex array analysis of circulating cytokines and chemokines in COVID-19 patients during the first wave of the SARS-CoV-2 pandemic in Milan, Italy

BackgroundThe systemic inflammatory syndrome called “cytokine storm” has been described in COVID-19 pathogenesis, contributing to disease severity. The analysis of cytokine and chemokine levels in the blood of 21 SARS-CoV-2 positive patients throughout the phases of the pandemic has been studied to understand immune response dysregulation and identify potential disease biomarkers for new treatments. The present work reports the cytokine and chemokine levels in sera from a small cohort of individuals primarily infected with SARS-CoV-2 during the first wave of the COVID-19 pandemic in Milan (Italy).ResultsAmong the 27 cytokines and chemokines investigated, a significant higher expression of Interleukin-9 (IL-9), IP-10 (CXCL10), MCP-1 (CCL2) and RANTES (CCL-5) in infected patients compared to uninfected subjects was observed. When the change in cytokine/chemokine levels was monitored over time, from the hospitalization day to discharge, only IL-6 and IP-10 showed a significant decrease. Consistent with these findings, a significant negative correlation was observed between IP-10 and anti-Spike IgG antibodies in infected individuals. In contrast, IL-17 was positively correlated with the production of IgG against SARS-CoV-2.ConclusionsThe cytokine storm and the modulation of cytokine levels by SARS-CoV-2 infection are hallmarks of COVID-19. The current global immunity profile largely stems from widespread vaccination campaigns and previous infection exposures. Consequently, the immunological features and dynamic cytokine profiles of non-vaccinated and primarily-infected subjects reported here provide novel insights into the inflammatory immune landscape in the context of SARS-CoV-2 infection, and offer valuable knowledge for addressing future viral infections and the development of novel treatments.

Similarities and contrasts: Comparing U.S. and Canadian paths to net-zero

Canada and the United States (US) have both committed to reaching net zero emissions by 2050 but neither have implemented policy sufficient to reach this target. Knowledge of the technical steps to deep decarbonization is needed alongside an understanding of how each country might be similarly and uniquely impacted by a transition to net zero emissions, contingent on specific technology advancements or policy decisions. We use the computable general equilibrium model, gTech, to simulate sixteen net zero scenarios for Canada and the US varying by technology and policy assumptions as part of the energy modelling forum 37 (EMF37) study. We find that both economies similarly continue to grow in all scenarios out to 2050 with the rate of growth largely determined by assumptions on negative emissions technology. Sectoral impacts differ between countries as a result of current emissions and GDP profiles in combination with assumed net zero scenario policy and technology advancements. In the US, we find that efficient use of electricity is a slightly more important predictor of economic outcomes, while Canada's economy is marginally more responsive to cost and performance improvements in carbon capture technologies.

Human-Exoskeleton Coupling Simulation for Lifting Tasks with Shoulder, Spine, and Knee-Joint Powered Exoskeletons.

In this study, we introduce a two-dimensional (2D) human skeletal model coupled with knee, spine, and shoulder exoskeletons. The primary purpose of this model is to predict the optimal lifting motion and provide torque support from the exoskeleton through the utilization of inverse dynamics optimization. The kinematics and dynamics of the human model are expressed using the Denavit-Hartenberg (DH) representation. The lifting optimization formulation integrates the electromechanical dynamics of the DC motors in the exoskeletons of the knee, spine, and shoulder. The design variables for this study include human joint angle profiles and exoskeleton motor current profiles. The optimization objective is to minimize the squared normalized human joint torques, subject to physical and task-specific lifting constraints. We solve this optimization problem using the gradient-based optimizer SNOPT. Our results include a comparison of predicted human joint angle profiles, joint torque profiles, and ground reaction force (GRF) profiles between lifting tasks with and without exoskeleton assistance. We also explore various combinations of exoskeletons for the knee, spine, and shoulder. By resolving the lifting optimization problems, we designed the optimal torques for the exoskeletons located at the knee, spine, and shoulder. It was found that the support from the exoskeletons substantially lowers the torque levels in human joints. Additionally, we conducted experiments only on the knee exoskeleton. Experimental data indicated that using the knee exoskeleton decreases the muscle activation peaks by 35.00%, 10.03%, 22.12%, 30.14%, 16.77%, and 25.71% for muscles of the erector spinae, latissimus dorsi, vastus medialis, vastus lateralis, rectus femoris, and biceps femoris, respectively.

Insights from a topo-bathymetric and oceanographic dataset for coastal flooding studies: the French Flooding Prevention Action Program of Saint-Malo

Abstract. The French Flooding Prevention Action Program of Saint-Malo, France, requires the assessment of coastal flooding risks and the development of a local flood warning system. The first prerequisite is knowledge of the topography and bathymetry of the bay of Saint-Malo; the acquisition of new multibeam bathymetric data was performed in 2018 and 2019 to increase the resolution of the existing topo-bathymetric datasets and to produce two high-resolution (20 and 5 m) topo-bathymetric digital terrain models. Second, the hydrodynamics associated with coastal flooding were investigated through a dense and extensive oceanographic field experiment conducted during winter 2018–2019 using a network of 22 moorings with 37 sensors: the network included 2 directional buoys, 2 pressure tide gauges, 18 wave pressure gauges, 4 single-point current meters, 7 current profilers, and 4 acoustic wave current profilers from mid-depth (25 m) up to the upper beach and the dike system. The oceanographic dataset thus provides an extended overview of the hydrodynamics and wave processes in the bay of Saint-Malo from the coast up to over-flooding and over-topping areas. This dataset helps to identify the physical drivers of the coastal flooding and provides a quantification of their respective contributions. In particular, the wave processes at the foot of the protection structures can be observed: in this macro-tidal environment, during high spring tides, short and infragravity waves propagate up to the protection structures, while the wave set-up remains negligible, and over-topping by sea packs can occur. The combination of high-resolution topo-bathymetric and oceanographic datasets allows the construction, calibration and validation of a wave and hydrodynamic coupled model that is used to investigate flooding processes more deeply and might be integrated into a future local warning system by means of Saint-Malo inter-communality. The topo-bathymetric and oceanographic datasets are available freely at https://doi.org/10.17183/MNT_COTIER_GNB_PAPI_SM_20m_WGS84, https://doi.org/10.17183/MNT_COTIER_PORT_SM_PAPI_SM_5m_WGS84 and https://doi.org/10.17183/CAMPAGNE_OCEANO_STMALO (Shom, 2020a, b, 2021).

Optimal Fast-Charging Strategy for Cylindrical Li-Ion Cells at Different Temperatures

Ensuring efficiency and safety is critical when developing charging strategies for lithium-ion batteries. This paper introduces a novel method to optimize fast charging for cylindrical Li-ion NMC 3Ah cells, enhancing both their charging efficiency and thermal safety. Using Model Predictive Control (MPC), this study presents a cost function that estimates the thermal safety boundary of Li-ion batteries, emphasizing the relationship between the temperature gradient and the state of charge (SoC) at different temperatures. The charging control framework combines an equivalent circuit model (ECM) with minimal electro-thermal equations to estimate battery state and temperature. Optimization results indicate that at ambient temperatures, the optimal charging allows the cell’s temperature to self-regulate within a safe operating range, requiring only one additional minute to reach 80% SoC compared to a typical fast-charging protocol (high current profile). Validation through numerical simulations and real experimental data from an NMC 3Ah cylindrical cell demonstrates that the simple approach adheres to the battery’s electrical and thermal limitations during the charging process.

3D electromagnetic assessment of bended CORC® cables

Conductor on round core (CORC®) cables have emerged as a leading contender in high-temperature superconducting (HTS) cable designs, offering exceptional performance with current densities surpassing 300 A/mm2 and the ability to withstand high axial tensile and compressive strain. Despite their remarkable properties, optimizing CORC® cables remains a challenge, particularly in accurately estimating their AC losses under real-world conditions, which necessitates advanced numerical modeling techniques. Building upon recent advancements in simulating straight CORC® cables, where Bean’s-like current profiles were observed across the actual thickness of wound superconducting tapes, we introduce a tailored computational approach to enhance the processing speed of three-dimensional (3D) finite element models of wound HTS tapes. This tailored approach is specifically designed to address the complexities of bent CORC® cables, which exhibit helicoidal winding and are subjected to varying mechanical strain. We focus on analyzing their electromagnetic performance by transitioning from idealized straight-former designs to more realistic scenarios where cable-formers are bent to accommodate flexible cable routing or coil configurations. Our simulations consider a typical cable design comprising three 4 mm-wide SuperPower tapes (SCS4050) with a twist pitch of 40 mm. We demonstrate the capability to accurately model the full electromagnetic behavior of bent CORC® cables without the reduction of degrees of freedom, providing valuable insights into their performance under bending conditions. Our findings contribute to the ongoing optimization of CORC® cable designs for a wide range of practical applications in high-current and high-magnetic field environments.

Convective heat transfer with Hall current using magnetized non-Newtonian Carreau fluid model on the cilia-attenuated flow

PurposeCilia serves numerous biological functions in the human body. Malfunctioning of nonmotile or motile cilia will have different kinds of consequences for human health. More specifically, the directed and rhythmic beat of motile cilia facilitates the unidirectional flow of fluids that are crucial in both homeostasis and the development of ciliated tissues. In cilia-dependent hydrodynamic flows, tapering geometries look a lot like the structure of biological pathways and vessels, like airways and lymphatic vessels. In this paper, the Carreau fluid model through the cilia-assisted tapered channel (asymmetric) under the influence of induced magnetic field and convective heat transfer is investigated.Design/methodology/approachLubrication theory is a key player in the mathematical formulation of momentum, magnetic field and energy equations. The formulated nonlinear and coupled differential equations are solved with the aid of the homotopy perturbation method (HPM). The graphical results are illustrated with the help of the computational software “Mathematica.”FindingsThe impact of diverse emerging physical parameters on velocity, induced magnetic field, pressure rise, current density and temperature profiles is presented graphically. It is observed that the cilia length parameter supported the velocity and current density profiles, while the Hartman number and Weissenberg number were opposed. A promising effect of emerging parameters on streamlines is also perceived.Originality/valueThe study provides novel aspects of cilia-driven induced magnetohydrodynamics flow of Carreau fluid under the influence of induced magnetic field and convective heat transfer through the asymmetric tapered channel.

Impact of various DIII-D diagnostics on the accuracy of neural network surrogates for kinetic EFIT reconstructions

Kinetic equilibrium reconstructions make use of profile information such as particle density and temperature measurements in addition to magnetics data to compute a self-consistent equilibrium. They are used in a multitude of physics-based modeling. This work develops a multi-layer perceptron (MLP) neural network (NN) model as a surrogate for kinetic Equilibrium Fitting (EFITs) and trains on the 2019 DIII-D discharge campaign database of kinetic equilibrium reconstructions. We investigate the impact of including various diagnostic data and machine actuator controls as input into the NN. When giving various categories of data as input into NN models that have been trained using those same categories of data, the predictions on multiple equilibrium reconstruction solutions (poloidal magnetic flux, global scalars, pressure profile, current profile) are highly accurate. When comparing different models with different diagnostics as input, the magnetics-only model outputs accurate kinetic profiles and the inclusion of additional data does not significantly impact the accuracy. When the NN is tasked with inferring only a single target such as the EFIT pressure profile or EFIT current profile, we see a large increase in the accuracy of the prediction of the kinetic profiles as more data is included. These results indicate that certain MLP NN configurations can be reasonably robust to different burning-plasma-relevant diagnostics depending on the accuracy requirements for equilibrium reconstruction tasks.

Electrochemical Circuit Model Based State of Health Prognostics for Evaluation of Reusability of Lithium-Ion Batteries from Electric Vehicle.

For the purpose of predicting the state of health of already used lithium-ion batteries from 85 kWh electric vehicles, a simplified equivalent circuit model is utilized to estimate the electrochemical time constant from constant current discharge profiles. The grading process among as-obtained LIB cells is classified into three level types according to the remaining capacity and direct current resistance. Theoretically, the logarithmic equation describing cycling behavior is derived and utilized in the prediction of the state of health of the used cells. After the selection of the electrochemical time constant obtained from the best-fitting results in constant current discharge data, the suitable cycle number until the 20th cycle was selected for the prediction of the state of health after the 250th cycling data, which revealed that a narrow error range below 5% was for high and medium battery grades. Also, this error range became abruptly wider in lowest grade batteries, indicating that our proposed model for cycling behavior was highly useful in the prediction of the future state of health of the used batteries.