RC Model Research Articles

An Enhanced Electro-Thermal Coupled Model With Lithium Plating Detection for Lithium-Ion Battery at Low Temperatures

Lithium plating may occur when lithium-ion batteries are charged at a high current rate and low ambient temperature. As a result, the battery degradation is accelerated and the risk of thermal runaway is increased. Along these lines, an enhanced electro-thermal model was proposed in this work for charging at low temperature values. By considering the battery with a reference electrode, a dual RC model was built. The developed model can predict the anode potential to prevent the lithium plating. The thermal model and absolute state of charge (SOC) were integrated into the battery model to solve the SOC inconsistency caused by the battery temperature rise during the charging process. Then, the proposed electro-thermal model was used to predict the electrical and thermal characteristics of a 50 Ah battery under various charging rates and low ambient temperature values conditions. In addition, the prediction results were validated by the experiment data. The maximum errors of the cathode potential and battery terminal voltage were 0.058 V and 0.154 V, respectively, whereas the maximum cell temperature error was 0.88 °C. Based on the anode prediction electro-thermal coupling model, a segmented heating strategy without lithium plating was proposed to describe the fast battery charging procedure, which can reduce energy consumption by 16.1% and save charging time by nearly 10 minutes compared with the continuous high-power heating mode.

Shear capacity of miniature beams with continuous staggered spiral stirrups

Scaled-down-reinforced concrete beams with rectangular staggered continuous spiral stirrups are experimentally investigated. Scale-down RC beams were considered in the current research due to ease of construction and economic feasibility. As the brittle shear failure should be avoided, continuous research trials have been conducted to find an effective technique to improve shear failure mechanism. Twenty-two beams were investigated for shear behavior under 4-point static push-over load considering the normal stirrups and compared with continuous staggered spiral ones. Stirrup spacing, shear arm ratio a/d, and shear reinforcement configurations are the main variables. All beams were designed and scaled down to be one-eighth of the full-scale beams. To minimize the size effect of using small-scale models, mortar was used instead of conventional concrete. The focus in this study was related to improved shear capacity, dissipated energy, and shear cracks propagation. It was found that using spiral reinforcement instead of normal one leads to a significant enhancement in shear capacity and dissipated energy by 33% and 45%, respectively. Therefore, the prototype RC model expected capacities were detailed calculated considering the scale-down factor used by authors. The experimental results were compared by calculated values according to international standard and specifications.

A modified high C-rate battery equivalent circuit model based on current dependence and concentration modification

The current battery equivalent circuit models are mainly suitable for room temperature or low C-rate conditions. However, the polarization phenomenon is serious at high C-rate, which results in the obvious voltage drop and significantly reduces the model accuracy at high C-rate. In this paper, the characteristics analysis of different RC models is firstly studied. It is found that the first-order RC circuit model is more suitable for high C-rate conditions due to the coupling of model parameter extraction. Based on the first-order RC model, a basic equivalent circuit model adapted to high C-rate condition is then determined. Secondly, the influences of sampling frequency and the identified time domain on instantaneous impedance and polarization resistance are researched. Thirdly, the change rules of different polarizations at high C-rate are analyzed. Furthermore, the relationship between the change rule of the model parameters and that of different overpotentials are researched. Subsequently, a modified high C-rate model is innovatively established, which considers the current dependence and concentration/temperature modification of instantaneous impedance and polarization resistance. Finally, the accuracy of the modified high C-rate model is verified at 5 °C, 25 °C, and 55 °C, respectively. Results show that the modified high C-rate model can improve the simulation accuracy of the voltage at high C-rate (≤6C), and the mean absolute error (MAE) and Root Mean Squared Error (RMSE) are within 68 mV. Compared with the existing high C-rate model, the accuracy of the modified high C-rate model proposed in this paper is improved and the complexity is also significantly reduced.

Innovative Model of Precast RC Curved Hybrid Deep Beams Composed Partially with High-Performance Concrete

Innovative Model of Precast RC Curved Hybrid Deep Beams Composed Partially with High-Performance Concrete

Obtaining the parameters of an RC model to know the insulation condition of a distribution transformer

Because it is a topic of great interest in the study of the insulation system of oil immersed Transformers, methods have been sought to determine the moisture content and aging of the pressboard, paper and dielectric oil from Transformers, measuring the response of the dielectric materials, which are characterized by known polarization phenomena. One of these techniques is dielectric spectroscopy in the time domain, measuring the polarization-depolarization currents, with this the parameters of an equivalent RC circuit (Debye model) are determined. Using the Debye model (T. K. Saha, P. Purkait y F. Muller, 2005) and obtaining the polarization current through the insulation resistance test, the parameters to establish the condition of the transformer oil-paper insulation system will be determined, using exponential curve fitting of the data obtained from the insulation resistance test with the high vs. Low connection plus tank, the parameters of the time constant τ, the resistance R and the capacitance C of the equivalent circuit are obtained and with this information, the condition of the transformer insulation system is evaluated and decisions are made regarding maintenance actions.

Comparative analysis of electrical models of lith-ium-ion batteries in electric vehicles

Problem. This article addresses the challenge of enhancing the environmental friendliness and energy efficiency of vehicles. It does so by conducting a comparative analysis and identifying ways to improve the electrical models of lithium-ion batteries used in electric vehicles. The study includes an examination of well-known electrical models of lithium-ion rechargeable batteries, such as the Rint model, the RC model, the Thevenin model, and the PNGV model. It identifies key characteristics of lithium-ion batteries in electric vehicles, including state of charge, mass, actual voltage, energy required for recharging, among others. The study also explores models of battery degradation, focusing on capacity reduction and the increase in active resistance. It substantiates directions for improving electrical models of lithium-ion batteries in electric vehicles by considering changes in capacity, internal resistance, polarization resistance, and both calendar and cyclic degradation. Goal. The aim of this work is to enhance the environmental friendliness and energy efficiency of vehicles through a comparative analysis and by determining ways to improve the electrical models of lithium-ion batteries in electric vehicles. Methodology. Our approach to achieving this goal involves using electrical models of lithium-ion batteries in electric vehicles, which describe various parameters such as state of charge, actual voltage during charge/discharge processes, and energy required for recharging. The study encompasses an investigation into the degradation of electric vehicle batteries, including their use in Vehicle to Grid (V2G) technology. Results. The analysis of electrical models of lithium-ion batteries in electric vehicles, aiming to increase their accuracy, considers the following aspects: changes in internal resistance and polarization resistance; capacity variation; and battery degradation. The change in internal resistance and polarization resistance should be considered based on two factors: the state of charge of the battery and the degree of its degradation. While the first factor is relevant primarily when the battery is deeply discharged (SoC<30%), the second factor must be considered at any state of charge. Capacity changes should be accounted for based on calendar and cyclic degradation. It has been determined that the primary causes of degradation in electric vehicle batteries are calendar aging (service life) and aging due to charge/discharge cycles. Contrarily, it is argued that using Vehicle to Grid (V2G) technology can reduce battery degradation by 10%. Originality. The results of this study provide a comprehensive understanding of the electrical models of lithium-ion batteries in electric vehicles and contribute to the improvement of existing models. Practical value. This research enhances the accuracy of current electrical models of lithium-ion batteries in electric vehicles by considering the variable nature of internal resistance and capacity during vehicle operation. It may be valuable in assessing the residual parameters of electric vehicle batteries during their secondary use, such as in the residential sector for solar energy support. The findings can be recommended to scientific and technical professionals involved in developing energy storage systems for electric vehicles.

Open circuit voltage - state of charge curve calibration by redefining max–min bounds for lithium-ion batteries

The relationship between open circuit voltage (OCV) and state of charge (SoC) is essential for SoC estimation of lithium-ion batteries, which can be secured by either low-current OCV test or incremental OCV test, with incremental OCV test demonstrating better results. Nevertheless, low current always leads to a discharge capacity that is inconsistent with maximum available capacity, which is a key parameter in SoC estimation. Therefore, the resultant OCV-SoC curve fails to provide a consistent SoC range and thus affects SoC estimation. In this paper, OCV-SoC curves obtained from low-current OCV tests are calibrated by redefining max–min bounds to improve SoC estimation accuracy. Max–min bounds of SoC are redefined by measuring, calculating and resetting upper and lower cut-off voltages of the OCV-SoC curve. Based on second-order RC model, model parameters are identified online and adaptive square-root unscented Kalman filter (ASRUKF) algorithm is introduced to perform SoC estimation, which is proved to be precise and adaptive. The calibrated OCV-SoC curve achieves an overall better SoC estimation performance than that obtained from the low-current OCV test and incremental OCV test, which is validated by experiments of LiNiMnCoO2 (NMC) batteries at 0°C and 25°C.

Study on thermal performance of dynamic insulation roof integrated with phase change material

Buildings are one of the largest parts of global energy consumption, and the building envelope is a paramount factor. In this study, a dynamic insulation roof integrated with phase change material (DRP) was proposed, whose thermal performance is improved by combining two phase change material (PCM) layers and a dynamic insulation (DI) layer. A dynamic heat transfer resistance-capacity model (RC model) for the DRP was established, and its accuracy was verified by experiment. The office building model with the DRP was established in TRNSYS to predict the annual loads. The DI layer and two PCM layers of the DRP were optimized, and the sum of the annual cooling and heating loads was reduced by 35.4% in comparison with the insulation roof in Wuhan (hot summer and cold winter areas). When the DRP is applied in Beijing (cold areas), the sum of the annual cooling and heating loads is reduced by 16.4%; when it is applied in Guangzhou (hot summer and warm winter areas), the cooling load is reduced by 24.8%. The energy-saving effect of the DRP in hot summer and warm winter areas, and in hot summer and cold winter areas is better than that in cold areas.

Hypoalbuminemia improves the ACS-NSQIP surgical risk calculator for gastrectomy

BackgroundThe ACS-NSQIP Surgical Risk Calculator (SRC) is used to predict surgical outcomes, but its accuracy in gastrectomy has been questioned.1,2 We investigated if adding hypoalbuminemia enhances its predictive ability in gastrectomy. MethodsWe identified gastrectomy patients from the ACS-NSQIP database from 2005 to 2019. We constructed pairs of logistic regression models: one with the existing 21 preoperative risk factors from the SRC and another with the addition of hypoalbuminemia. We evaluated improvement using Likelihood Ratio Test (LRT), Brier scores, and c-statistics. ResultsOf 18,070 gastrectomy patients, 34.5 ​% had hypoalbuminemia. Hypoalbuminemia patients had 2.34 higher odds of mortality and 1.79 higher odds of morbidity. Adding hypoalbuminemia to the RC model statistically improved predictions for mortality, cumulative morbidity, pulmonary, renal, and wound complications (LRT p ​< ​0.001). It did not improve predictions for cardiac complications (LRT p ​= ​0.11) ConclusionHypoalbuminemia should be considered as an additional variable to the ACS-NSQIP SRC for gastrectomy.

A Comparative Study of Fractional-Order Models for Supercapacitors in Electric Vehicles

Fractional-order models display many advantages compared with integer-order equivalent circuit models for modeling energy storage system, such as more precision, fewer parameters. This paper carries out a comparative study of fractional-order equivalent circuit models for modeling supercapacitor. These models are fractional-order RC model, fractional-order classic model, fractional-order RRCW model, fractional-order dynamic model, fractional-order transmission line model. An experiment load platform is built to test the supercapacitor for collecting the feature data. To be fair, the parameters of all models are optimized to achieve their best fitting accuracy by using the genetic algorithm. Accuracy and complexity two indices are considered to evaluate the above five models based on different test cycles. Simulation results show that the fractional-order RRCW model has the ideally comprehensive performance.

An Extended Kalman Filter Design for State-of-Charge Estimation Based on Variational Approach

State of charge (SOC) is a very important variable for using batteries safely and reliably. To improve the accuracy of SOC estimation, a novel variational extended Kalman filter (EKF) technique based on least square error method is herein provided by establishing a second-order equivalent circuit model for the battery. It was found that when SOC decreased, resistance polarization occurred in the electrochemical model, and the parameters in the equivalent RC model varied. To decrease the modeling error in the equivalent circuit model, the system parameters were identified online depending on the SOC’s estimated result. Through the SOC-estimation process, the variation theorem was introduced, which enabled the system parameters to track the real situations based on the output measured. The experiment results reveal the comparison of the SOC-estimation results of the variational EKF algorithm, the traditional EKF algorithm, the recursive least square (RLS) EKF algorithm, and the forgotten factor recursive least square (FFRLS) EKF algorithm based on different indices, including the mean square error (MSE) and the mean absolute error (MAE). The variational EKF algorithm provided in this paper has higher estimation accuracy and robustness than the traditional EKF, which verifies the superiority and effectiveness of the proposed method.

Evaluation of the Thermal Performance of Two Passive Facade System Solutions for Sustainable Development

Sustainable development is essential for the future of the planet. Using passive elements, like ventilated facades based on insulation and air chambers, or living walls, which are solutions based on nature, is a powerful strategy for cities to improve their thermal environment, reduce energy consumption, and mitigate the effects of climate change. This approach allows for the quantification of the influence of passive surfaces on energy fluxes compared to bare surfaces. In addition, it delves into understanding how the incorporation of vegetation on building facades alters surface energy fluxes, involving a combination of physical and biochemical processes. This comprehensive investigation seeks to harness the potential of passive and natural solutions to address the pressing challenges of urban sustainability and climate resilience. This research uses a surface energy balance model to analyze the thermal performance of two facades using experimental data from a PASLINK test cell. This study uses the grey box RC model, which links continuous-time ordinary differential equations with discrete measurement data points. This model provides insight into the complex interplay among factors that influence the thermal behavior of building facades, with the goal of comprehensively understanding how ventilated and green facades affect the dynamics of energy flow compared to conventional facades. The initial thermal resistance of the bare facade was 0.75 (°C m2)/W. The introduction of a ventilated facade significantly increased this thermal resistance to 2.47 (°C m2)/W due to the insulating capacity of the air chamber and its insulating layer (1.70 (°C m2)/W). Regarding the modular living wall, it obtained a thermal resistance value of 1.22 (°C m2)/W (this vegetated facade does not have an insulating layer). In this context, the modular living wall proved to be effective in reducing convective energy by 68% compared with the non-green facade. It is crucial to highlight that evapotranspiration was the primary mechanism for energy dissipation in the green facade. The experiments conclusively show that both the modular living wall and open-ventilated facade significantly reduce solar heat loads compared with non-passive bare wall facades, demonstrating their effectiveness in enhancing thermal performance and minimizing heat absorption.

Transient simulations and theoretical modeling of near-junction heat conduction in GaN-on-diamond HEMT

Under high-power working conditions, accurate predictions of the maximum temperature and thermal resistance distribution in devices during transient states are essential for monitoring the health status of the devices. In this work, the heat conduction characteristics of GaN-on-diamond high electron mobility transistors (HEMTs) near the junction region under transient conditions with thermal impedance are systematically investigated with the aid of finite element simulations and thermal impedance analyses. The effects of pulse characteristics, thermal properties of different layers on the maximum temperature and thermal resistance of the device are discussed specifically. The results show that pulse characteristics have a significant effect on the maximum temperature of the device. The shorter the pulse switching time is, the higher the maximum temperature will be. Regarding the effects of the thickness of the GaN layer, the maximum temperature initially decreases and then increases, with the lowest maximum temperature observed at 1.7 μm. As the TBR increases uniformly from 10 to 40 m2K/GW, the temperature increases by approximately 4 % per 10 m2K/GW. The analyses on the thermal resistance show that the GaN layer occupies the largest proportion of thermal resistance, while the diamond substrate occupies the smallest. The optimal substrate thickness is obtained by analyzing the thermal penetration depth in the substrate for working conditions with various pulse widths. For diamond substrate, the optimal thickness at a pulse width of 5 μs is about 25 μm, and increases as the increase in pulse width. At the end, a modified Cauer RC model is proposed for predicting the transient maximum temperature based on heat spreading analysis. The difference between the maximum temperatures predicted by the model and the finite element simulations is within 8 %.

Towards automated stochastic Grey-Box Model Calibration for Heat Transfer Coefficient Inference

An accurate on-site measurement of the intrinsic thermal performance of the building envelope is a strong leverage to promote quality management and energy performance contracting. The building sector expects on-site measurements of the Heat Transfer Coefficient to be as short as possible and highly accurate. Perturbation measurement methods analysed by RC models have been shown to be promising with accurate results within 2 days. However, stochastic RC model calibration is highly dependent on expert knowledge. In particular, model initialisation jeopardises the calibration success if not carefully chosen a priori and all the more so with second or higher order models. This paper proposes and assesses a novel automated initialisation procedure for RC models. After having brought to light that the capacitance initialisation is critical to the calibration success, the paper shows how first-order models are almost always highly identifiable. The first-order model estimated parameters then serve as initial values for all second and higher order models. This novel procedure is in 78 % of the 156 cases tested better than or similar to expert knowledge initialisation. The results are very encouraging and suggest a successful integration in a global RC model calibration and selection workflow for a fully automated process.

Differentiation of Hepatocellular Carcinoma from Intrahepatic Cholangiocarcinoma through MRI Radiomics.

The purpose of this study was to investigate the efficacy of magnetic resonance imaging (MRI) radiomics in differentiating hepatocellular carcinoma (HCC) from intrahepatic cholangiocarcinoma (ICC). The clinical and MRI data of 129 pathologically confirmed HCC patients and 48 ICC patients treated at the Affiliated Hospital of North Sichuan Medical College between April 2016 and December 2021 were retrospectively analyzed. The patients were randomly divided at a ratio of 7:3 into a training group of 124 patients (90 with HCC and 34 with ICC) and a validation group of 53 patients (39 with HCC and 14 with ICC). Radiomic features were extracted from axial fat suppression T2-weighted imaging (FS-T2WI) and axial arterial-phase (AP) and portal-venous-phase (PVP) dynamic-contrast-enhanced MRI (DCE-MRI) sequences, and the corresponding datasets were generated. The least absolute shrinkage and selection operator (LASSO) method was used to select the best radiomic features. Logistic regression was used to establish radiomic models for each sequence (FS-T2WI, AP and PVP models), a clinical model for optimal clinical variables (C model) and a joint radiomics model (JR model) integrating the radiomics features of all the sequences as well as a radiomics-clinical model combining optimal radiomic features and clinical risk factors (RC model). The performance of each model was evaluated using the area under the receiver operating characteristic curve (AUC). The AUCs of the FS-T2WI, AP, PVP, JR, C and RC models for distinguishing HCC from ICC were 0.693, 0.863, 0.818, 0.914, 0.936 and 0.977 in the training group and 0.690, 0.784, 0.727, 0.802, 0.860 and 0.877 in the validation group, respectively. The results of this study suggest that MRI-based radiomics may help noninvasively differentiate HCC from ICC. The model integrating the radiomics features and clinical risk factors showed a further improvement in performance.

Structure-property relationships in the electrical conductivity of multilayer graphene sheet/epoxy nanocomposites

The relationship between the physicochemical properties of multilayer graphene sheets (GSs), their resulting state of dispersion, and the effective electrical conductivity of GS/epoxy nanocomposites is investigated. To construct such relationship, four types of GSs with dissimilar properties were physicochemically, structurally, and morphologically characterized, and correlated with the direct current and alternating current (AC) electrical responses of their polymeric nanocomposites. It was found that the most influential parameters that affect the electrical conductivity of the nanocomposites are the size and density of agglomerates formed at the mesoscale. Both parameters, in turn, are governed by the physicochemical properties of the individual GSs. At the nanostructure level, the lateral size of the GS arises as the most influential parameter, with larger GSs yielding higher conductivities and lower percolation thresholds. The carbon/oxygen ratio of the GSs also showed moderate influence. The nanocomposites showed a resistive (R)-capacitive (C) electrical response in AC, which fits to a two-element parallel RC model. Both, the power law and general effective media (GEM) models predict similar percolation thresholds, but the GEM model is more general and adequately reproduces the whole electrical curve for all filler concentrations.

Conditional power and information fraction calculations at an interim analysis for random coefficient models.

Random coefficient (RC) models are commonly used in clinical trials to estimate the rate of change over time in longitudinal data. Trials utilizing a surrogate endpoint for accelerated approval with a confirmatory longitudinal endpoint to show clinical benefit is a strategy implemented across various therapeutic areas, including immunoglobulin A nephropathy. Understanding conditional power (CP) and information fraction calculations of RC models may help in the design of clinical trials as well as provide support for the confirmatory endpoint at the time of accelerated approval. This paper provides calculation methods, with practical examples, for determining CP at an interim analysis for a RC model with longitudinal data, such as estimated glomerular filtration rate (eGFR) assessments to measure rate of change in eGFR slope.

SOC estimation of lithium-ion batteries based on the improved Kalman filter

Nowadays, the environmental crisis and energy crisis have been known by people around the world as issues that have to be taken seriously. It is recognized that electric vehicles are more environmentally friendly than traditional fuel cars, but there are still many immature aspects of electric vehicle technology. Battery technology is one of them, and lithium battery is a common power storage tool for electric vehicles. Therefore, it is very important to monitor the status of the battery pack in real-time. Improving the practical capacity, safety, and service life of lithium batteries all have an important impact on the development of electric vehicles. In battery management systems (BMS), the accurate prediction of the battery state of charge (SOC) is particularly important. In this paper, the 18650 lithium battery was selected for the research experiment, and the circuit model of second-order RC was selected. Modeling by MATLAB, the SOC-OCV curve of the battery was determined through the data obtained from the charging and discharging experiment (HPPC), the original least squares method was improved, the forgetting factor was introduced, and the model was identified by the parameters based on the experimental data. The traditional SOC prediction method added noise adaptive rules and iterative theory in the extended Kalman filter(EKF), formed the adaptive Kalman filter (AIEKF) to estimate the SOC of lithium battery, and estimated the SOC using the American urban road cycle (UDDS) data. The results show that the AIEKF algorithm has fewer errors and advantages than the traditional EKF algorithm.

Finding connection “while everything is going to crap”: experiences in Recovery Colleges during the COVID-19 pandemic

BackgroundRecovery Colleges (RCs) are mental health and well-being education centres where people come together and learn skills that support their wellness. Co-production, co-learning and transformative education are fundamental to RCs. People with lived experience are recognized as experts who partner with health professionals in the design and actualization of educational programming. The pandemic has changed how RCs operate by necessitating a shift from in-person to virtual offerings. Given the relational ethos of RCs, it is important to explore how the experiences of RC members and communities were impacted during this time. To date, there has been limited scholarship on this topic.MethodsIn this exploratory phase of a larger project, we used participatory action research to interview people who were accessing, volunteering and/or working in RCs across Canada. Semi-structured interviews were conducted with twenty-nine individuals who provided insights on what is important to them about RC programming.ResultsOur study was conducted amid the COVID-19 pandemic. Accordingly, participants elucidated how their involvement in RCs was impacted by pandemic related restrictions. The results of this study demonstrate that RC programming is most effective when it: (1) is inclusive; (2) has a “good vibe”; and (3) equips people to live a fuller life.ConclusionsThe pandemic, despite its challenges, has yielded insights into a possible evolution of the RC model that transcends the pandemic-context. In a time of great uncertainty, RCs served as safe spaces where people could redefine, pursue, maintain or recover wellness on their own terms.

Investigating the electrode-electrolyte interface modelling in cochlear implants

Objective. Proposing a good electrode-electrolyte interface (EEI) model and properly identifying relevant parameters may help designing safer and more optimized auditory nerve fiber stimulation and recording in cochlear implants (CI). However, in literature, EEI model parameter values exhibit large variability. We aim to explain some root causes of this variability using the Cole model and its simpler form, the Basic RC model. Approach. We use temporal and spectral methods and fit the models to stimulation pulse voltage response (SPVR) and electrochemical impedance spectroscopy (EIS) data. Main Results. Temporal fittings show that there are multiple sets of model parameters that provide a good fit to the SPVR data. Therefore, small methodological differences in literature may result in different model fits. While these models share similar characteristics at high frequencies >500 Hz, the SPVR fitting is blind to low frequencies, thus it cannot correctly estimate the Faradaic resistor. Similarly, the polarization capacitor and its fractional order are not estimated robustly (capacitor variations in the nano- to micro-farad range) due to limited observation of mid-range frequencies. EIS provides a good model fit down to ∼3Hz, and thus robust estimation for the polarization capacitor. At lower frequencies charge mechanisms may modify the EEI, requiring multi-compartment Cole model fitting to EIS to improve the estimation of Faradaic characteristics. Our EIS data measurements down to 0.05Hz show that a two-compartment Cole model is sufficient to explain the data. Significance. Our study describes the scope and limitation of SPVR and EIS fitting methods, by which literature variability is explained among CI EEI models. The estimation of mid-to-low-frequency characteristics of the CI EEI is not in the scope of the SPVR method. EIS provides a better fit; however, its results should not be extrapolated to unobserved frequencies where new charge transfer mechanisms may emerge at the EEI.