A New Multiscale Modeling Framework for Lithium-Ion Battery Dynamics: Theory, Experiments, and Comparative Study with the Doyle-Fuller-Newman Model

Technological advancements and globalization in recent decades have largely been responsible for the ever-increasing energy and power demands across different industrial sectors. This has led to an extensive use of fossil fuel based resources such as gasoline and diesel, especially in the transportation industry [1]. The consequences of this utilization are excessive emission of greenhouse gases and degradation of air quality, which have raised significant environmental concerns. Added to this, concerns over the eventual depletion of fossil fuels has accelerated the exploration and development of new energy sources. At the same time, increasingly stringent regulations have been imposed to enhance the fuel efficiency and minimize emissions in automobiles. Efforts to meet current and future regulation targets have led to the development of new technologies, some of which are: a) vehicle electrification [2], b) gasoline direct injection technology [3], c) variable valve timing [4], d) advanced exhaust gas recirculation [5], and e) selective catalytic reduction for NOx [6]. On the energy front, wind and solar technologies have been vastly explored [7], but these technologies are time-dependent and intermittent in nature and must be supplemented by energy storage devices. Lithium-ion batteries have been considered the most preferred technology for grid energy storage and electrified transportation because of their higher energy and power densities, better efficiency, and longer lifespan in comparison with other energy storage devices such as lead acid, nickel metal hydride, and nickel cadmium [8]. Lithium-ion batteries are the most dominant technology today in small scale applications such as portable phones and computers [9]. However, their wide-scale adoption in automotive and grid energy storage applications has been hampered by concerns associated with battery life, safety, and reliability. A lack of comprehensive understanding of battery behavior across different environments and operating conditions make it challenging to extract their best performance. Currently, significant trade-offs are being made to optimize battery performance, such as over-sizing and under-utilization in automotive applications. While sensors are used to evaluate battery performance and regulate their operation, their fundamental limitation lies in the inability to measure battery internal states such as state-of-charge (SoC) or state-of-health (SoH). The aforementioned issues with lithium-ion batteries can addressed to a large extent with the help of mathematical modeling. They play an important role in the design and utilization of batteries in an efficient manner with existing technologies, because of their ability to predict battery behavior with minimal expenditure of time and materials [10]. While empirical mathematical models are computationally efficient, they rely on a significant amount of experimental data and calibration effort to predict future battery behavior. In addition, such models do not consider the underlying physicochemical transport processes and hence cannot predict battery degradation. Moreover, the knowledge acquired from such models cannot be generalized across different battery chemistry and geometry. This elucidates the need for fundamental physics-based mathematical models to aid in the development of advanced control strategies through model-based control and virtual sensor deployment. Such models can capture the underlying transport phenomena across various length and time scales, and enhance performance and longevity of batteries while ensuring safe operation. The overarching aim of this dissertation is to present a multiscale modeling approach that captures the behavior of such devices with high fidelity, starting from fundamental principles. The application of this modeling approach is focused on porous lithium-ion batteries. The major outcome of this work is to facilitate the development of advanced and comprehensive battery management systems by: a) developing a high fidelity multiscale electrochemical modeling framework for lithium-ion batteries, b) investigating the temperature-influenced and aging-influenced multiscale dynamics for different battery chemistry and operating conditions, c) formulating a methodology to analytically determine effective ionic transport properties using the electrode microstructure, and d) numerical simulation of the developed physics-based model and comparison analysis with the conventionally used Doyle-Fuller-Newman (DFN) electrochemical model. The new multiscale model presented in this dissertation has been derived using a rigorous homogenization approach which uses asymptotic expansions of variables to determine the macroscopic formulation of pore-scale governing transport equations. The conditions that allow successful upscaling from pore-to-macro scales are schematically represented using 2-D electrode and electrolyte phase diagrams. These phase diagrams are used to assess the predictability of macroscale models for different electrode chemistry and battery operating conditions. The effective transport coefficients of the homogenized model are determined by resolving a unit cell closure variable problem in the electrode microstructure, instead of conventionally employed empirical formulations. The equations of the developed full order homogenized multiscale (FHM) model are implemented and resolved using the finite element software COMSOL Multiphysics®. Numerical simulations are presented to demonstrate the enhanced predictability of the FHM against the traditionally used DFN model, particularly at higher temperatures of battery operation. Model parameter identification is performed by co-simulation studies involving COMSOL Multiphysics® and MATLAB® software using the Particle Swarm Optimization (PSO) technique. The parameter identification studies are performed using data from laboratory experiments conducted on 18650 cylindrical lithium-ion cells of nickel-manganese-cobalt oxide (NMC) cathode chemistry

Pharmacological evaluation of Allium cepa extract as hepatoprotective potential in albino rat

Objective: The study was designed to evaluate the hepatoprotective potential of Allium cepa extract in carbon tetrachloride (CCl4) induced hepatotoxicity in albino rats. Methods: All albino rats were divided into four groups having six animal in each (n=6) to evaluate the efficacy of Allium cepa extract in comparison with standard drug Silymarin. The drugs were orally administered to rats for 10 days in CCl4 model and animals were weighed periodically. At the end of the study, blood samples were collected for the test of SGOT, SGPT, ALP, total bilirubin, and total protein levels. The antioxidant enzyme parameters such as LPO, MDA, GSH, SOD, and CAT were also performed for all group of animal. The liver tissues of all groups were collected after scarifying the animals and histopathological examination reported for confirmation of potential activity. Results: All the test group of rats was significantly reduced levels of SGOT, SGPT, ALP, total bilirubin, in CCl4 induced hepatotoxic models. There was a significant increase in total protein level in all the tested formulations. The antioxidant enzyme parameters such as LPO, MDA, GSH, SOD, and CAT were shows significant improvement in the treatment group as compared to disease control. The extract treated rats effectively preserved the structural integrity of the hepato-cellular membrane and liver cell architecture damaged by CCl4. Conclusion: It can be concluded that the Allium cepa extract possesses hepatoprotective activity in CCl4, induced hepatotoxicity in rats. This may be effectively used as a hepatoprotective agent in the management of hepatitis caused by various toxins

Recent and advanced animal models used in the Screening of analgesics and anti-inflammatory activity

Non-Steroidal anti-inflammatory drugs (NSAIDs) are consisting of three major anti-pyretic, anti-inflammatory and anti-analgesics properties. They have reduced the sensation of pain, body temperature, and inflammation. It is also used for the treatment of the long-term health problems like arthritis (rheumatoid arthritis, osteoarthritis, and lupus). NSAIDs highly protect the lining of the stomach and intestines from the damaging effects of acid promote blood clotting by activating blood platelets, and promote normal function of the kidney. Incompatible with the action of NSAIDs many different types of drugs and plant use for the treatment of the analgesic, inflammation and pyretic activity. Diclofenac inhibit the cyclooxygenase (COX-2) enzyme with the greater potency that it (COX-1). NSAIDs are generally used in the management of pain because of the integrated role of the COX pathway that is recognition of pyretic, inflammation and analgesic. Introduction to painful procedures and/or stressors during the early neonatal period can reprogram the underlying neurocircuitry involved in nociception and neuropathic pain perception. The reprogramming of these systems can result in an enduring elevation in sympathy towards mechanical and thermal stimuli. During adolescence, hind paw mechanical removal thresholds were evaluated using an electronic von Frey Anesthesiometer. Animals challenged neonatally with LPS (nLPS) had increased pain sensitivity on this measure which was related with decreased Oprm1 expression in the prefrontal cortex (PFC) and periaqueductal gray (PAG) of both male and female rats. There was no effect of inflammatory treatment on either anxiety or depressive-like behavior suggesting that affective functioning did not account for differences in mechanical pain sensitivity

FORMULATION, STANDARDIZATION, AND EVALUATION OF POLYHERBAL DISPERSIBLE TABLET

Objective: The study was designed as formulation, standardization, and evaluation of polyherbal dispersible tablet prepared for the management of kidney disorders. To overcome the problem of dyspepsia in geriatric patients by the use of polyherbal dispersible tablets.Methods: Dispersible tablets were prepared using aqueous root extract powder of the selected plant viz. A. officinalis, B. diffusa, C. papaya, C. fistula, C. intybus, F. hispida, F. indica, C. nurvala, S. virgaurea, and V. negundo with the help of superdisintegrant addition technique using crospovidone, sodium starch glycolate and croscarmellose sodium in different percentage. Evaluation assessments such as the substantial test, weight variation, hardness, friability, content uniformity, disintegration, in vitro dispersion, stability study and IR compatibility were carried out.Results: Micromeritics of extracts powder were determined for all formulation, which signifying good flow properties. The substantial examination was established, which comply with official requirements for uniformity test, and the drug content was close to 100% in all formulations. Disintegration time was observed for all formulation in which the polyherbal formulation-3 (PHF-3) showing 1.10±0.10 min; during in vitro dispersion time, all formulation showed appropriate dispersion in which the PHF-3 captivating 2.00±0.45 min only. The IR compatibility shows none chemical interaction between the extracts and excipients.Conclusion: The PHF-3 showed satisfactory disintegration and in vitro dispersion time due to crospovidone and reported as the best formulation. The stability study and IR compatibility validate the PHF may represent new easily swallow dispersible tablet that may enhance drug permeability and advance bioavailability for nephrotic patients.Â

Estimating Causal Installed-Base Effects: A Bias-Correction Approach

New empirical models of consumer demand that incorporate social preferences, observational learning, word-of-mouth or network effects have the feature that the adoption of others in the reference group - the “installed-base” - has a causal effect on current adoption behavior. Estimation of such causal installed-base effects is challenging due to the potential for spurious correlation between the adoption of agents, arising from endogenous assortive matching into social groups (or homophily) and from the existence of unobservables across agents that are correlated. In the absence of experimental variation, the preferred solution is to control for these using a rich specification of fixed-effects, which is feasible with panel data. We show that fixedeffects estimators of this sort are inconsistent in the presence of installed-base effects; in our simulations, random-effects specifications perform even worse. Our analysis reveals the tension faced by the applied empiricist in this area: a rich control for unobservables increases the credibility of the reported causal effects, but the incorporation of these controls introduces biases of a new kind in this class of models. We present two solutions: an instrumental variable approach, and a new bias-correction approach, both of which deliver consistent estimates of causal installed-base effects. The bias-correction approach is tractable in this context because we are able to exploit the structure of the problem to solve analytically for the asymptotic bias of the installed-base estimator, and to incorporate it into the estimation routine. Our approach has implications for the measurement of social effects using non-experimental data, and for measuring marketing-mix effects in the presence of state-dependence in demand, more generally. Our empirical application to the adoption of the Toyota Prius Hybrid in California reveals evidence for social influence in diffusion, and demonstrates the importance of incorporating proper controls for the biases we identify

Floristic diversity and vegetation analysis of the community forests of South-West Haryana, India

Community forestry is an important form of forests and provides resources to over a half billion people in developing countries. They also play a significant part in mitigating the CO2 levels by sequestering a significant amount of carbon in the soil as well as biomass. The present paper assessed floristic diversity and vegetation structure in three different community forests of southwest Haryana which is a part of tropical dry deciduous forests. The vegetation sampling and data analysis were done following standard procedures. A total of 76 plant species belonging to 37 families in the form of 11 trees, 13 species of shrubs, 46 species of herbs, and 6 species of climbers are documented from all three sites. Poaceae was the most specious family in three sites. The highest tree diversity was recorded in Bhera forest followed by Daya and Dhanger. Regarding understory, the forest of Daya has a greater diversity than Bhera and Dhanger forests. Salavadora oleoides was the dominant tree species in Daya site and Dhanger site while in Bhera the dominant tree species was Ailanthus excelsa. The incidence of rampant livestock grazing and other anthropogenic disturbances were visible in all three sites which are primarily responsible for the degradation of these already fragmented village community forests

Extreme lensing induces spectro-temporal correlations in black-hole signals

Rapid progress in electromagnetic black hole observation presents a theoretical challenge: how can the universal signatures of extreme gravitational lensing be distilled from stochastic astrophysical signals? With this motivation, the two-point correlation function of specific intensity fluctuations across image positions, times, and frequencies is here considered. The contribution of strongly deflected light rays, those which make up the photon ring, is analytically computed for a Kerr black hole illuminated by a simple geometric-statistical emission model. We subsequently integrate over the image to yield a spectro-temporal correlation function which is relevant for unresolved sources. Finally, some observational aspects are discussed and a preliminary assessment of detectability with current and upcoming missions is provided

Advertising Content and Consumer Engagement on Social Media: Evidence from Facebook

We describe the effect of social media advertising content on customer engagement using data from Facebook. We content-code 106,316 Facebook messages across 782 companies, using a combination of Amazon Mechanical Turk and natural language processing algorithms. We use this data set to study the association of various kinds of social media marketing content with user engagement—defined as Likes, comments, shares, and click-throughs—with the messages. We find that inclusion of widely used content related to brand personality—like humor and emotion—is associated with higher levels of consumer engagement (Likes, comments, shares) with a message. We find that directly informative content—like mentions of price and deals—is associated with lower levels of engagement when included in messages in isolation, but higher engagement levels when provided in combination with brand personality–related attributes. Also, certain directly informative content, such as deals and promotions, drive consumers’ path to conversion (click-throughs). These results persist after incorporating corrections for the nonrandom targeting of Facebook’s EdgeRank (News Feed) algorithm and so reflect more closely user reaction to content than Facebook’s behavioral targeting. Our results suggest that there are benefits to content engineering that combines informative characteristics that help in obtaining immediate leads (via improved click-throughs) with brand personality–related content that helps in maintaining future reach and branding on the social media site (via improved engagement). These results inform content design strategies. Separately, the methodology we apply to content-code text is useful for future studies utilizing unstructured data such as advertising content or product reviews

Online Evaluation of Audiences for Targeted Advertising via Bandit Experiments

Firms implementing digital advertising campaigns face a complex problem in determining the right match between their advertising creatives and target audiences. Typical solutions to the problem have leveraged non-experimental methods, or used "split-testing" strategies that have not explicitly addressed the complexities induced by targeted audiences that can potentially overlap with one another. This paper presents an adaptive algorithm that addresses the problem via online experimentation. The algorithm is set up as a contextual bandit and addresses the overlap issue by partitioning the target audiences into disjoint, non-overlapping sub-populations. It learns an optimal creative display policy in the disjoint space, while assessing in parallel which creative has the best match in the space of possibly overlapping target audiences. Experiments show that the proposed method is more efficient compared to naive "split-testing" or non-adaptive "A/B/n" testing based methods. We also describe a testing product we built that uses the algorithm. The product is currently deployed on the advertising platform of JD.com, an eCommerce company and a publisher of digital ads in China

Advertising Media and Target Audience Optimization via High-dimensional Bandits

We present a data-driven algorithm that advertisers can use to automate their digital ad-campaigns at online publishers. The algorithm enables the advertiser to search across available target audiences and ad-media to find the best possible combination for its campaign via online experimentation. The problem of finding the best audience-ad combination is complicated by a number of distinctive challenges, including (a) a need for active exploration to resolve prior uncertainty and to speed the search for profitable combinations, (b) many combinations to choose from, giving rise to high-dimensional search formulations, and (c) very low success probabilities, typically just a fraction of one percent. Our algorithm (designated LRDL, an acronym for Logistic Regression with Debiased Lasso) addresses these challenges by combining four elements: a multiarmed bandit framework for active exploration; a Lasso penalty function to handle high dimensionality; an inbuilt debiasing kernel that handles the regularization bias induced by the Lasso; and a semi-parametric regression model for outcomes that promotes cross-learning across arms. The algorithm is implemented as a Thompson Sampler, and to the best of our knowledge, it is the first that can practically address all of the challenges above. Simulations with real and synthetic data show the method is effective and document its superior performance against several benchmarks from the recent high-dimensional bandit literature.Comment: 39 pages, 8 figure