Newman's Model Research Articles

A Novel Approach to Continuum FE Multiphysics Simulation of Batteries via Heterogeneous Homogenization

Advancements in modern technology necessitate batteries that can be safely charged and discharged at high rates with minimal aging. A promising strategy to achieve these ends is through optimization of porous microstructures of existing electrode materials to promote better electrical and ionic transport without compromising energy density, as has been shown experimentally by introducing laser ablation channels to the electrodes. The present work proposes a microstructure up-scaling methodology that efficiently accounts for material heterogeneity by assigning a porosity field to a continuum scale finite element mesh, effectively homogenizing materials while maintaining the heterogeneous porosity distribution inherent to the original microstructure. Constitutive relationships relating solid electric conductivity and pore tortuosity to local porosity are directly computed from fully resolved anode, cathode, and separator microstructure 3D models and assigned in the continuum as material properties of a finite element mesh. This allows for utilization of the computationally efficient, homogenized Newman model while maintaining dependency on the heterogeneous local porosity. Then, coupled electrochemical-thermal-mechanical-pore pressure finite element simulations are performed on the battery cell model with this heterogeneous porosity field, and associated local variations on transport properties are prescribed as material properties by the precomputed constitutive relationships. Performance metrics and lithium plating potential are compared to the results of simulations of the fully homogenized cell with the same constitutive relationships and to the base case of a fully homogenous cell with typical modelling assumptions applied to porosity dependent constitutive relationships. These comparisons are made both as a means of validating the methodology and to demonstrate the vast differences that arise when simplifying assumptions are used instead of the constitutive relationships of the actual material microstructure. Finally, the methodology is used in its intended optimization application to compare the performance of a typical battery microstructure to a similar porous microstructure with micro holes added via laser ablation. The results indicate that differences in the microstructure of materials with identical average porosities and similar constitutive relationships can have significant differences in both cell performance and aging that would typically be impossible to detect using fully homogeneous continuum modeling. Figure 1

Thermo-electrochemical level-set topology optimization of a heat exchanger for lithium-ion batteries for electric vertical take-off and landing vehicles

Developing electric vertical take-off and landing vehicles (eVTOL) that can meet the demanding power and energy requirements entails significant challenges, one of which is due to the weight of the battery packs. To address this challenge, optimization techniques can be employed to achieve lightweight designs while satisfying thermal criteria. This study focuses on optimizing a battery heat exchanger housing a high-energy-densitycylindrical cell using the level-set topology optimization method. To accurately account for heat generation from battery electrochemistry, we investigate both a high-fidelity, the Doyle Fuller Newman (DFN) model, and a low-fidelity electrochemical model, the Single Particle Model (SPM), which are compared to experimental results for an eVTOL flight profile. The novelty of the proposed approach resides in the integration of the electrochemical models within a three-dimensional unsteady thermo-electrochemical topology optimization framework. The battery heat exchanger is optimized considering the heat generated by the batteries at the material scale due to the system power requirements. The heat generated by the battery is incorporated as a source term in an unsteady heat conduction finite element model, forming the basis of the optimization process. Our objective is to minimize the integrated thermal compliance over time while satisfying a volume constraint, employing the level-set method. The SPM proves competent in predicting the voltage profile but underestimates the temperature increase. On the other hand, the DFN model accurately predicts both the temperature increase and the voltage profile, making it suitable for the thermal analysis of cells in eVTOL vehicles. Surprisingly, steady-state optimization turns out to be sufficient to generate optimized topologies that perform similarly to transient cases for the case investigated but at a reduced cost. By integrating electrochemical modeling, level-set topology optimization, and heat transfer analysis, our study contributes to the development of lightweight and thermally efficient battery heat exchangers for eVTOL vehicles, which can be extended to battery packs. Importantly, the presented methodology is versatile and can be applied to different battery chemistries, form factors, and power profiles.

Investigating the Effect of the Separation of Scales in Reduced Order Battery Modelling: Implications on the Validity of the Newman Model

Modelling lithium-ion battery behavior is essential for performance prediction and design improvement. However, this task is challenging due to processes spanning many length scales, leading to computationally expensive models. Reduced order models have been developed to address this, assuming a “separation of scales” between micro- and macroscales. This study compares two approaches: direct microstructure-resolved 3D domain electrochemical modelling and a simplified 1D homogenized model, similar to the Doyle-Fuller-Newman model. The research investigates the validity of the scale separation assumption in continuum electrode-level models by varying scale separation factors, boundary conditions, and geometries. The findings reveal increases in deviation between the 3D models and 1D models for more tortuous, less porous microstructures, especially under higher discharge rates. However, under realistic conditions, with an electrode featuring eight particles across its thickness and typical transport properties, the 3D model predicts only a slight (2%) increase in current compared to the 1D model at a high rate of 7C (approximately j ≈ 350 Am−2). These results suggest that the separation of scales assumption in the DFN model is generally suitable for a wide range of operating conditions. However, 1D models may overlook local variations in electrolyte concentration and potential, crucial for understanding degradation mechanisms.

Impact Makers (A): The Newman's Own of Management Consulting

This case requires students to evaluate alternative dual-share-class corporate structures that allow companies and entrepreneurs to pursue profit with purpose. The case explores Impact Makers, an IT consulting company based in Richmond, Virginia. While original founders of the firm hold all voting rights, the cash flow rights belong to two nonprofits setting the stage for a Newman's Own model of management consulting. The case discusses whether and how the alternative corporate structure aids the firm's overall strategy to attract top-quality employees, pay them competitive salaries, and provide superior service to its clients while donating 100% of its lifetime value to charitable causes, largely through partnerships with various nonprofit organizations. More importantly, the case asks students to evaluate how such a dual-share-class and dual-purpose company can raise capital to fund continued growth.The case opens with CEO Michael Pirron reminding himself of all the questions he had run through to execute a strategy to further grow Impact Makers' consulting business both through expanding a menu of services and through conquering new geographical markets. To do either, or both, the company needed a cash infusion. Internal cash was limited, as up to 40% of it flowed to charitable partners, demonstrating Impact Makers' commitment to its mission. Raising debt for a company without fixed assets was challenging and time consuming. Complicating it all was that being structured as a nonstock corporation rendered equity raising difficult. Could Impact Makers raise money to grow and stay true to community values at the same time?

On the analytical solution of single particle models and semi-analytical solution of P2D model for lithium-ion batteries

The Doyle–Fuller–Newman (DFN) model serves as a fundamental electrochemical modeling framework widely used in the field of lithium-ion batteries, especially in its pseudo-two-dimensional (P2D) formulation. Despite its widespread use, the DFN model imposes significant computational demands, especially for tasks such as cell characterization and state estimation in electric vehicles’ battery management systems (BMS). To mitigate these computational challenges, researchers have developed several reduced-order models. In this study, we tackle the associated computational efforts starting from the governing DFN equations. The analytical solutions for the transport equations in the electrode and the electrolyte are derived, considering generic time-varying boundary conditions under certain assumptions. The obtained exact solutions are then used to develop an analytical model for a single particle model with electrolyte dynamics (SPME) which provides higher predictive accuracy compared to a single particle model (SPM), especially in high C-rate scenarios, which are critical in many applications, including electric vehicles. Furthermore, the exact solution of lithium diffusion in active material particles is incorporated into the standard P2D model. This integration leads to a semi-analytical variant of the P2D model (SAP2D). A remarkable advantage of these analytical solutions is the significantly lower computational cost compared to corresponding numerical approaches. Spatial discretization becomes unnecessary, and the solutions can be obtained by a few explicit function evaluations. Moreover, when the time-varying boundary conditions are known, the need for time stepping is also eliminated.

Development and Evaluation of a Physicochemical Equivalent Circuit Model for Lithium-Ion Batteries

Physicochemical models of lithium-ion cells, like the Doyle Fuller Newman (DFN) model, are omnipresent in battery research and development as they provide crucial insight into the cell, while equivalent circuit models dominate the area of application-oriented models, where speed is paramount. In this work, we develop and analyze a model that combines the two approaches using equivalent circuits and the DFN theory. By using a generalized approach to equivalent circuits, we model the necessary electric and diffusional processes analogously. The developed model accounts for all physical processes and internal states contained in the standard DFN model. We investigate the impact of model discretization and compare the developed model to a reference DFN implementation. Agreement between the models for both the predicted cell voltage and internal states shows that the developed equivalent circuit model provides a physically meaningful description of a lithium-ion battery, thereby successfully combining the two main modeling approaches for lithium-ion batteries.

Experimental Validation of Newman Model Analysis for Modern Li-Ion Battery Cathode Materials

The Butler-Volmer equation is a fundamental equation extensively used to describe electrochemical kinetics and relates the reaction current at an electrode interface to the voltage. Newman and coworkers suggested an equation (Fig. 1a) for the exchange current density, which includes the state of charge (SoC) dependence of Li-ion batteries as well as theory-based anodic and cathodic transfer coefficients. This description of interface kinetics, part of the commonly used Newman model, is widely used for battery modeling.1,2 Because of the lack of experimental data, several assumptions were made in the derivations,3 such as assuming the intercalation reaction to be a first order reaction, and assuming the transfer coefficients to be 0.5. As new battery materials emerge, it is important to validate whether these assumptions are applicable, or whether the model needs to be extended.In this work, we compare experimentally obtained interface resistances from different cathode materials with the theoretical behavior of the interface kinetics predicted from the Newman model as well as experimental characterizations published in the research literature. The experimental data for the interface resistance is determined using electrochemical impedance spectroscopy,4 where the electrodes are built into a three-electrode cell setup, using a gold wire reference electrode, which allows studying the cathode impedance separately.5 As anode, we use graphite that is prelithiated to ~20% to make sure there is enough lithium in the cell to be able to relithiate the cathode completely. The loading of the electrodes is low to ensure negligible pore resistance contributions, so that the semicircle in the Nyquist-plot originates from kinetics only. The obtained charge transfer resistance is analyzed as a function of Li-content to show how the kinetics of the cathode depend on the degree of lithiation.From the Newman model, it is expected that the interface resistance shows a U-shape over the degree of lithiation with a minimum at 50% SoC with a drastic increase in charge transfer resistance for the first and last 10%. This is because the kinetics are slower when the cathode is completely empty and completely full, which can be seen from the equation in Fig. 1a. When c=c max or c=0, i 0 becomes 0. Fig. 1b. shows the theoretical behavior based on the Newman model, the experimental results found in this study, and experimental results found in the literature for NMC1116.Figure 1: a) Exchange current density suggested by Newman and coworkers. i 0 is the exchange current density, F is the Faraday constant, k a and k c are reaction rate constants for the anodic and cathodic reactions, α a and α c are the anodic and cathodic transfer coefficients, c max is the maximum lithium concentration in the material, c is the lithium concentration in the material, and c l is the salt concentration in the electrolyte. b) Charge transfer resistance vs. Li-content for ~0.5 mAh/cm² NMC111 (Gelon Lib) with prelithiated graphite (Sigma Aldrich) anode, gold wire reference electrode with a core diameter of 50 µm coated with a 7 µm polyimide insulation (Goodfellow), 80 µl 1 M LiPF6 in EC:EMC (3:7) (Gotion), two glass fiber separators (260 µm, VWR) at 25 °C determined from semi-circle width of cathode impedance (100 kHz to 10 mHz, 10 mV amplitude) using a potentiostat (VMP3, BioLogic) recorded in three electrode Swagelok cell (average of 3 measurements) is represented by dark grey points. The red points show theory according to Eq. 1 with, and the light brown points show NMC111 from Ref. 6. The lines between the points are drawn to enhance the features of the figure.Comparison of experimental data and the theoretical framework shows pronounced discrepancies, which cannot be resolved if the transfer coefficients are kept at 0.5. Similar measurements are conducted for a wide range of active materials and changed environmental conditions to further elaborate validity and/or necessity to modify the commonly used description of the interface kinetics relation. This will help to better understand interface resistance in battery cells and thereby allow optimization of material properties by, e.g., surface modification. References M. Ecker et al., J. Electrochem. Soc., 162, A1836 (2015).T. F. Fuller, M. Doyle, and J. Newman, J. Electrochem. Soc., 141, 1–10 (1994).M. Doyle, T. F. Fuller, and J. Newman, J. Electrochem. Soc., 140, 1526–1533 (1993).A. Lasia, in Electrochemical Impedance Spectroscopy and its Applications, A. Lasia, Editor, p. 203–250, Springer, New York, NY (2014).S. Solchenbach, D. Pritzl, E. J. Y. Kong, J. Landesfeind, and H. A. Gasteiger, J. Electrochem. Soc., 163, A2265–A2272 (2016).R. Morasch, H. A. Gasteiger, and B. Suthar, J. Electrochem. Soc., 170, 080522 (2023). Figure 1

Fatigue performance and parameter optimization of CFRP-reinforced steel plate considering elevated temperature

This article aims to investigate the effect of elevated temperature on the fatigue performance of CFRP-reinforced cracked steel plates and optimize the design parameters for their reinforcement. Based on the self-developed adhesive GY34, a finite element model of the CFRP-reinforced central cracked steel plate is established by the ABAQUS software. Parametric analysis is carried out on different bonding methods of CFRP plates and different thicknesses of adhesive layers for CFRP-reinforced cracked steel plates. The effect of elevated temperature on the reinforcement is also studied to verify the excellent performance of GY34 adhesive. Furthermore, the enhancement of fatigue life for cracked steel plates reinforced with CFRP plates at different temperatures is investigated using the Paris model and modified Newman model. The results show that double-sided reinforcement with CFRP is more effective than single-sided reinforcement. All of the different bonding methods could reduce effectively the stress intensity factor of cracked steel plates and improve significantly the bearing capacity of the steel plates. Among them, increasing the number of CFRP layers and their elastic modulus yield the most significant improvement on the reinforcing effect. Elevated temperatures effect greatly on the mechanical properties of adhesively bonded CFRP-reinforced steel plates. The effective bonding length and the stress intensity factor amplitude at the crack tip of the steel plate increases with temperature rises, which may accelerate the growth rate of fatigue crack and reduce the reinforcing effect on fatigue life.

Electrochemical reactions coupled multiphysics modeling for lithium ion battery with non-heterogeneous micro-scale electrodes structures

During high-rate discharging process of lithium-ion battery (LIB), the macroscopic models struggle to capture the actual three-dimensional spatial evolutions of physical fields. In this study, an electrochemical-thermal-species coupled microscale homogeneous (MNH) three-dimensional model for LIB is built, with which the electrochemical reaction, lithium-ion concentration, electric potential, and heat generation rate inside the microscale LIB electrodes can be visualized and analyzed. The simulation results show that conventional volume-averaged 1 + 1D Newman models underestimate the influence of the microscopic electrode structure on the diffusion kinetics and reaction kinetics within the electrode. At high multiplicities, the localized pore structure causes insufficient utilization for the electrode material near the collector, leading to the localized hot spots in narrow pores. Additionally, the effects of extreme heat exchange environments on the physical laws within the electrodes are discussed. The results indicate that under an isothermal environment, the mass transfer capability of the electrode decreases. Moreover, the extent of delithiation and heat generation behavior of the particles exhibit stronger spatial nonuniformity, resulting in an increased final cut-off voltage. Furthermore, we conclude that an electrolyte phase potential caused overpotential due to pore structure contributes to the sensitivity of the active polarization heat towards the heat exchange environment.

Investigation of Betty Newman’s Systemic Model in a Patient With Heart Failure

Background and Aim: Nurses should provide their care and interventions in a practical framework. Performance-based theories make nursing interventions based on principles and rules. Therefore, the present study was conducted to implement Betty Newman’s systemic model in a patient with heart failure. Materials and Methods: A case study was conducted in November 2020 in one of the hospitals of the Tehran University of Medical Sciences, Tehran, Iran. A patient with heart failure was examined and Newman’s model was applied to him. Nursing care was performed based on Newman’s nursing process. Results: A patient with heart failure was evaluated according to Newman’s model. Intrapersonal stressors (in the physiological dimension, including weakness and lethargy and the inability of the person, fatigue, bowel movements disorder, and shortness of breath), interpersonal stressors (away from family and children), and extrapersonal stressors (wife’s aggression and psychological pressure) were found. Based on the investigations, 10 nursing diagnoses and nursing care are presented based on three levels of prevention that are vital according to Newman. The results were used in the classification of nursing interventions and the classification and results of nursing, respectively. Conclusion: According to the results of the present study, the implementation of nursing care based on the Newman model can be effective in reducing the physiological and psychological needs of patients with heart failure. Nurses can play a more effective role in improving the problems of these patients by applying nursing care based on Newman’s systemic model.

Physics-Informed Neural Networks for State of Health Estimation in Lithium-Ion Batteries

One of the most challenging tasks of modern battery management systems is the accurate state of health estimation. While physico-chemical models are accurate, they have high computational cost. Neural networks lack physical interpretability but are efficient. Physics-informed neural networks tackle the aforementioned shortcomings by combining the efficiency of neural networks with the accuracy of physico-chemical models. A physics-informed neural network is developed and evaluated against three different datasets: A pseudo-two-dimensional Newman model generates data at various state of health points. This dataset is fused with experimental data from laboratory measurements and vehicle field data to train a neural network in which it exploits correlation from internal modeled states to the measurable state of health. The resulting physics-informed neural network performs best with the synthetic dataset and achieves a root mean squared error below 2% at estimating the state of health. The root mean squared error stays within 3% for laboratory test data, with the lowest error observed for constant current discharge samples. The physics-informed neural network outperforms several other purely data-driven methods and proves its advantage. The inclusion of physico-chemical information from simulation increases accuracy and further enables broader application ranges.

(Keynote) Current Density Outliers in Batteries at Rest

The need for creating safe electrolytes for lithium batteries is significant given the continued safety problems associated with current lithium-ion batteries. On the other hand, increasing electric vehicle (EV) adoption requires lithium-ion batteries that can be charged quickly. Some EV batteries have caught on fire despite neither being charged nor discharged. While the lithium that plates on graphite during fast charging affects battery safety, so do the internal ionic currents that occur when the battery is at rest after charging. These currents are difficult to quantify due to the absence of external current. We study a graphite electrode at rest after 6C fast charging using X-ray microtomography. We quantify spatially resolved current density distributions that originate at plated lithium and end in underlithiated graphite particles. The median current density at the plated lithium phase approaches zero in about 10 minutes after the battery current is stopped. Surprisingly, however, the range of current density was independent of time, with outliers above 20 mA cm-2. The persistence of outliers provides a clue as to the origin of catastrophic failure in batteries at rest. Some suggestions on how the outliers may be modeled using 3D Newman models will be discussed.

An Electrochemical-Thermal-Mechanical Model for Large Format Lithium Ion Batteries

The intercalation reaction in Lithium-Ion batteries causes significant volume changes in the active material phases during cycling. Due to spatial limitations in common battery applications, this entails mechanical stresses on different length scales and thereby influencing performance and lifetime of the battery[1]. Mechanical effects are closely linked to electrochemical and thermal processes in the cell, giving rise to complex interactions that are difficult to predict. Moreover, large cell dimensions, which become ever so common in today’s battery industry, further increase complexity due to local variations of state variables. Therefore, modeling is essential to enhance understanding of the governing- and limiting processes.In this contribution we present a fully coupled multi-scale-, multi-physics-model that combines an electrochemical model on the particulate electrode level, namely an extended Newman model featuring a particle size distribution, and a homogenized cell material model, displaying anisotropic electrical-, thermal- and mechanical properties. To include mechanical effects into a multi-physics-model[2] a new approach is considered. We apply the poroelastic theory to describe the mechanical behavior governed by the interaction of the porous electrode matrix and the infiltrated electrolyte. The use of homogenization techniques allows to keep the computational effort in check, while still doing justice to the complexity of a three-dimensional-, multi-scale-, and multi physics problem. The model is parametrized in-house by applying different electrochemical-, mechanical-, and microstructural characterization methods. The model, its parameterization and validation as well as selected simulation results will be presented. Acknowledgements This work was funded by the Deutsche Forschungsgemeinschaft (DFG) in the framework of the research training group SiMET (281041241/GRK2218).[1] Y. Zhao, P. Stein, Y. Bai, M. Al-Siraj, Y. Yang, B.-X. Xu, Journal of Power Sources 2019, 413, 259–283.[2] A. Schmidt, D. Oehler, A. Weber, T. Wetzel, E. Ivers-Tiffée, Electrochimica Acta 2021, 393, 1–14.

Individual, socioeconomic and healthcare access factors influencing the delays in leprosy presentation, diagnosis and treatment: a qualitative study.

This study investigated the factors influencing the presentation, diagnosis and treatment of leprosy in primary healthcare. Qualitative research was conducted on patients undergoing treatment in a priority hyperendemic region for leprosy control in northeastern Brazil. Interviews were conducted between September and December 2020 at primary healthcare centers. Data were analysed based on the basic interpretive qualitative structure according to Andersen and Newman's model of healthcare utilisation. Knowledge of leprosy symptoms influenced patients' search for a diagnosis. Unfavorable socioeconomic conditions experienced by patients made diagnosis and treatment difficult. Incorrect evaluations by health professionals caused difficulties and delays in obtaining a diagnosis of leprosy. Perceptions about the disease, such as non-acceptance of the disease and the adverse effects of the medications, affected treatment seeking and treatment continuity. Patients with leprosy faced delays and healthcare access barriers related to knowledge of the disease, socioeconomic conditions and the structure of healthcare services, which must be considered when creating care plans, surveillance and control actions against leprosy. Appropriate interventions are necessary to reduce delays and better control the disease.

A Descriptor System Approach for the Nonlinear State Estimation of Li-Ion Battery Series/Parallel Arrangements

The nonlinear state estimation for a series/parallel arrangement of lithium-ion (Li-ion) battery cells is addressed in this article assuming limited information. First, a unified model for series/parallel arrangements in terms of a nonlinear descriptor model is proposed in order to account for Kirchhoff’s laws characterizing the interconnection. It relies on a simplified electrochemical model of the individual cells, the so-called equivalent-hydraulic model. Then, assuming that the system and output equation nonlinearities locally satisfy Lipschitz-like constraints, a linear matrix inequality (LMI)-based technique is proposed for designing a nonlinear state observer, which estimates the state of each individual cell (such as the state-of-charge and inner temperature) as well as the algebraic variables (currents or voltages for respectively parallel or series configurations). The proposed design conditions also provide an estimate of the region of guaranteed convergence while ensuring a peak-to-peak performance with respect to exogenous inputs and model uncertainties. The effectiveness of the approach is demonstrated on a detailed battery electrochemical simulator (based on the Doyle–Fuller–Newman model) for a series arrangement of two cells. Comparisons with a standard extended Kalman filter demonstrate the superior performance of the proposed approach.

Dynamic Multi-Dimensional Numerical Transport Study of Lithium-Ion Battery Active Material Microstructures for Automotive Applications

In support of GM’s traction battery efforts, we derived and implemented a method to describe the electrochemical performance of a battery cell considering the nuances of the electrode microstructure at the anode and the cathode and the corresponding impact on the electrochemical transport in the solid and liquid phases. To assess the capability of the method, we compared model results from the microstructure framework with the commonly used continuum-level porous electrode model, commonly referred to as the pseudo-2-dimensional model, or the Newman Model. The microstructure modeling framework was applied to simulate the electrochemical and transport processes within the battery cell to predict the concentration gradients, local state-of-charge distributions, reaction distributions, and the overall terminal voltage of the system. In this report, we provide a commentary on the validity and practicality of the microstructure approach to drive battery cell design.

EFFECTIVENESS OF MULTI INTERVENTIONAL PROGRAMME ON MOBILE PHONE DEPENDENT PRACTICES AMONG HIGH SCHOOL STUDENTS

The present study investigated the effectiveness of multi interventional programme on mobile phone dependent practice among high school students of selected schools at Kottayam district. Aquantitative research approach was used for the study. The study was theoretically supported by Betty Newman's System model. Screening was done to the entire 8th and 9th class students of selected schools and 45 students were selected from among them according to the inclusion criteria using multistage random sampling technique. Pretest was done on the rst day using socio personal data sheet and Problematic Use of Mobile Phones (PUMP) scale. Multi-interventional programme was given on the second day which included introduction to digital detox challenge and demonstration of mindfulness based exercises. Students were instructed to take digital detox challenge and complete each week's task. Study subjects also practiced mindfulness exercises twice daily for 4 weeks. Post test was done on 29th day after multi-interventional programme using same tools. The results of the study revealed that multi-interventional programme reduced the mobile phone dependency level (p<0.01) among the study samples which indicated that the multi interventional programme was effective in reducing mobile phone dependent practices.

A Bayesian constitutive model selection framework for biaxial mechanical testing of planar soft tissues: Application to porcine aortic valves

A variety of constitutive models have been developed for soft tissue mechanics. However, there is no established criterion to select a suitable model for a specific application. Although the model that best fits the experimental data can be deemed the most suitable model, this practice often can be insufficient given the inter-sample variability of experimental observations. Herein, we present a Bayesian approach to calculate the relative probabilities of constitutive models based on biaxial mechanical testing of tissue samples. Forty-six samples of porcine aortic valve tissue were tested using a biaxial stretching setup. For each sample, seven ratios of stresses along and perpendicular to the fiber direction were applied. The probabilities of eight invariant-based constitutive models were calculated based on the experimental data using the proposed model selection framework. The calculated probabilities showed that, out of the considered models and based on the information available through the utilized experimental dataset, the May–Newman model was the most probable model for the porcine aortic valve data. When the samples were further grouped into different cusp types, the May–Newman model remained the most probable for the left- and right-coronary cusps, whereas for non-coronary cusps two models were found to be equally probable: the Lee–Sacks model and the May–Newman model. This difference between cusp types was found to be associated with the first principal component analysis (PCA) mode, where this mode’s amplitudes of the non-coronary and right-coronary cusps were found to be significantly different. Our results show that a PCA-based statistical model can capture significant variations in the mechanical properties of soft tissues. The presented framework is applicable to other tissue types, and has the potential to provide a structured and rational way of making simulations population-based.

Do Immigrants Use Less Health Care than Non-immigrants? A Population-based Study among People living with Multimorbidity in Canada

Abstract Background Immigrants face unique health care barriers, which can negatively impact their health service use and overall health. Those with multimorbidity may face a particular challenge given its association with increased need for health care. The purpose of this study was to compare health care utilization, as measured by the number of visits to family physicians and specialists, between immigrants and Canadian-born individuals living with multimorbidity. Methods A cross-sectional analysis was carried out using data from the 2015-2016 cycles of Canadian Community Health Survey (CCHS) on 9,014 study participants living with multimorbidity. The study utilized Andersen and Newman's behavioral model as a conceptual framework to identify quantifiable predictors associated with health service utilization. For the entire sample as well as for male and female subsamples, statistical models were fitted using negative binomial regressions to account for the count nature of the outcome variables. Results After adjusting for relevant confounders, no statistically significant differences were observed between immigrants and Canadian-born respondents in the number of visits to family physicians or specialists. However, subgroup analysis revealed that female immigrants with multimorbidity had considerably fewer visits to family physicians than Canadian-born females (Incident Rate Ratio [IRR]=0.86, 95% CI: 0.76-0.98), while for males these differences were not significant (IRR=1.03, 95% CI: 0.87-1.21). Conclusions Future research should focus on longitudinal studies to track the health status of immigrants over time, particularly those living with multimorbidity. Moreover, public health policies should be implemented to reduce cultural and social barriers to health care, with a special focus on female immigrants.

Faster and Healthier Charging of Lithium-Ion Batteries via Constrained Feedback Control

A constrained feedback control strategy designed on the basis of a simplified electrochemical–thermal model is considered for the fast and healthy charging of a lithium-ion battery cell. The constraints ensure avoidance of side reactions and operating modes that yield premature aging (healthier charging). They are enforced through a reference governor approach, hence requiring a low computational burden. A systematic approach is presented for model identification and control law design. The method is first validated on a detailed battery simulator based on the Doyle–Fuller–Newman model combined with a thermal model. Next, it is validated experimentally through battery-in-the-loop long-term aging campaigns, and the results show that the charging time is reduced by 22% while simultaneously ensuring a longer lifetime (26% less capacity degradation) compared to a 2C constant-current/constant-voltage (CCCV) approach. Compared to recommended C/2 CCCV, our method charges the battery 70% faster and degrades it similarly despite more demanding operating conditions.