PARAMETER IDENTIFICATION FOR ELECTROCHEMICAL MODELS OF LITHIUM ION BATTERIES USING SENSITIVITY ANALYSIS

2021 ◽  
pp. 1-33
Author(s):  
Chitra Dangwal ◽  
Marcello Canova
Keyword(s):  
Parameter Identification ◽  
Large Scale ◽  
Initial Conditions ◽  
Lithium Ion ◽  
Optimization Methods ◽  
Particle Model ◽  
Aerospace Applications ◽  
Novel Approach ◽  
Consistent Solution ◽  
The Common

Abstract Predicting the chemical and physical processes occurring in Lithium-ion cells with high-fidelity electrochemical models is today a critical requirement to accelerate the design and optimization of battery packs for automotive and aerospace applications. One of the common issues associated with electrochemical models is the complexity of parameter identification, particularly when relying only on experimental data obtained via non-invasive techniques. This paper presents a novel approach to improve the common methods of parameter calibration that consists of matching the predicted terminal voltage to test data via optimization methods. The study is conducted for an NMC-graphite cell, modeled using a reduced order Extended Single Particle Model (ESPM). The proposed approach relies on using a large-scale Particle Swarm Optimization (PSO), modified by including a term that accounts for the parameter sensitivity information, such that the rate of convergence and robustness of the algorithm to obtain a consistent solution in the presence of uncertainties in the initial conditions are significantly improved.

scholarly journals A NOTE ON PHASING LONG GENOMIC REGIONS USING LOCAL HAPLOTYPE PREDICTIONS

2006 ◽  
Vol 04 (03) ◽  
pp. 639-647 ◽  
Author(s):  
ELEAZAR ESKIN ◽  
RODED SHARAN ◽  
ERAN HALPERIN
Keyword(s):  
Large Scale ◽  
Computational Cost ◽  
Nucleotide Polymorphisms ◽  
Single Nucleotide ◽  
Novel Approach ◽  
Maximum Likelihood Criterion ◽  
The Common ◽  
Genomic Regions ◽  
High Computational Cost ◽  
Combining Information

The common approaches for haplotype inference from genotype data are targeted toward phasing short genomic regions. Longer regions are often tackled in a heuristic manner, due to the high computational cost. Here, we describe a novel approach for phasing genotypes over long regions, which is based on combining information from local predictions on short, overlapping regions. The phasing is done in a way, which maximizes a natural maximum likelihood criterion. Among other things, this criterion takes into account the physical length between neighboring single nucleotide polymorphisms. The approach is very efficient and is applied to several large scale datasets and is shown to be successful in two recent benchmarking studies (Zaitlen et al., in press; Marchini et al., in preparation). Our method is publicly available via a webserver at .

scholarly journals Nitrogen-Doped Porous Co3O4/Graphene Nanocomposite for Advanced Lithium-Ion Batteries

Nanomaterials ◽  
2019 ◽  
Vol 9 (9) ◽  
pp. 1253 ◽  
Author(s):  
Huihui Zeng ◽  
Baolin Xing ◽  
Lunjian Chen ◽  
Guiyun Yi ◽  
Guangxu Huang ◽  
...  
Keyword(s):  
Lithium Ion Batteries ◽  
Anode Material ◽  
Self Assembly ◽  
Active Sites ◽  
Large Scale ◽  
Lithium Ion ◽  
Reversible Capacity ◽  
Co3o4 Nanoparticles ◽  
Nitrogen Doped ◽  
Novel Approach

A novel approach is developed to synthesize a nitrogen-doped porous Co3O4/anthracite-derived graphene (Co3O4/AG) nanocomposite through a combined self-assembly and heat treatment process using resource-rich anthracite as a carbonaceous precursor. The nanocomposite contains uniformly distributed Co3O4 nanoparticles with a size smaller than 8 nm on the surface of porous graphene, and exhibits a specific surface area (120 m2·g−1), well-developed mesopores distributed at 3~10 nm, and a high level of nitrogen doping (5.4 at. %). These unique microstructure features of the nanocomposite can offer extra active sites and efficient pathways during the electrochemical reaction, which are conducive to improvement of the electrochemical performance for the anode material. The Co3O4/AG electrode possesses a high reversible capacity of 845 mAh·g−1 and an excellent rate capacity of 587 mAh·g−1. Furthermore, a good cyclic stability of 510 mAh·g−1 after 100 cycles at 500 mA·g−1 is maintained. Therefore, this work could provide an economical and effective route for the large-scale application of a Co3O4/AG nanocomposite as an excellent anode material in lithium-ion batteries.

scholarly journals Concurrent Real-Time Estimation of State of Health and Maximum Available Power in Lithium-Sulfur Batteries

Energies ◽  
2018 ◽  
Vol 11 (8) ◽  
pp. 2133 ◽  
Author(s):  
Vaclav Knap ◽  
Daniel Auger ◽  
Karsten Propp ◽  
Abbas Fotouhi ◽  
Daniel-Ioan Stroe
Keyword(s):  
Large Scale ◽  
Initial Conditions ◽  
Specific Capacity ◽  
Lithium Ion ◽  
Time Estimation ◽  
Capacity Fade ◽  
State Of Health ◽  
Complex Behaviour ◽  
Estimation Algorithms ◽  
Lithium Sulfur

Lithium-sulfur (Li-S) batteries are an emerging energy storage technology with higher performance than lithium-ion batteries in terms of specific capacity and energy density. However, several scientific and technological gaps need to be filled before Li-S batteries will penetrate the market at a large scale. One such gap, which is tackled in this paper, is represented by the estimation of state-of-health (SOH). Li-S batteries exhibit a complex behaviour due to their inherent mechanisms, which requires a special tailoring of the already literature-available state-of-charge (SOC) and SOH estimation algorithms. In this work, a model of SOH based on capacity fade and power fade has been proposed and incorporated in a state estimator using dual extended Kalman filters has been used to simultaneously estimate Li-S SOC and SOH. The dual extended Kalman filter’s internal estimates of equivalent circuit network parameters have also been used to the estimate maximum available power of the battery at any specified instant. The proposed estimators have been successfully applied to both fresh and aged Li-S pouch cells, showing that they can accurately track accurately the battery SOC, SOH, and power, providing that initial conditions are suitable. However, the estimation of the Li-S battery cells’ capacity fade is shown to be more complex, because the practical available capacity varies highly with the applied current rates and the dynamics of the mission profile.

Green synthesis, characterization of silver sulfide nanoparticles and antibacterial activity evaluation

2019 ◽  
Author(s):  
Chem Int
Keyword(s):  
Antibacterial Activity ◽  
Large Scale ◽  
Research Work ◽  
Silver Sulfide ◽  
Morphological Properties ◽  
Effective Diameter ◽  
Novel Approach ◽  
Transmission Electron ◽  
Green Route ◽  
Silver Sulfide Nanoparticles

This research work presents a facile and green route for synthesis silver sulfide (Ag2SNPs) nanoparticles from silver nitrate (AgNO3) and sodium sulfide nonahydrate (Na2S.9H2O) in the presence of rosemary leaves aqueous extract at ambient temperature (27 oC). Structural and morphological properties of Ag2SNPs nanoparticles were analyzed by X-ray diffraction (XRD) and transmission electron microscopy (TEM). The surface Plasmon resonance for Ag2SNPs was obtained around 355 nm. Ag2SNPs was spherical in shape with an effective diameter size of 14 nm. Our novel approach represents a promising and effective method to large scale synthesis of eco-friendly antibacterial activity silver sulfide nanoparticles.

A New Image Encryption Scheme Using Dual Chaotic Map Synchronization

2020 ◽  
Vol 18 (1) ◽  
Keyword(s):  
Image Encryption ◽  
Chaotic Systems ◽  
Initial Conditions ◽  
Chaotic Map ◽  
Security Applications ◽  
The Common ◽  
And Diffusion ◽  
Confusion And Diffusion ◽  
Sensitivity To Initial Conditions ◽  
Diffusion Properties

Chaotic systems behavior attracts many researchers in the field of image encryption. The major advantage of using chaos as the basis for developing a crypto-system is due to its sensitivity to initial conditions and parameter tunning as well as the random-like behavior which resembles the main ingredients of a good cipher namely the confusion and diffusion properties. In this article, we present a new scheme based on the synchronization of dual chaotic systems namely Lorenz and Chen chaotic systems and prove that those chaotic maps can be completely synchronized with other under suitable conditions and specific parameters that make a new addition to the chaotic based encryption systems. This addition provides a master-slave configuration that is utilized to construct the proposed dual synchronized chaos-based cipher scheme. The common security analyses are performed to validate the effectiveness of the proposed scheme. Based on all experiments and analyses, we can conclude that this scheme is secure, efficient, robust, reliable, and can be directly applied successfully for many practical security applications in insecure network channels such as the Internet

Student Perceptions of Community: A Different Perspective

NASPA Journal ◽  
1998 ◽  
Vol 35 (4) ◽  
Author(s):  
Jackie Clark ◽  
Joan Hirt
Keyword(s):  
Student Perceptions ◽  
Large Scale ◽  
Research University ◽  
Small Scale ◽  
Social Environments ◽  
Common Assumption ◽  
Small Communities ◽  
Public Research University ◽  
The Common ◽  
Using Data

The creation of small communities has been proposed as a way of enhancing the educational experience of students at large institutions. Using data from a survey of students living in large and small residences at a public research university, this study does not support the common assumption that small-scale social environments are more conducive to positive community life than large-scale social environments.

Solution of Inverse Problems in Thermal Systems

2019 ◽  
Vol 12 (1) ◽  
Author(s):  
Yogesh Jaluria
Keyword(s):  
Heat Treatment ◽  
Inverse Problems ◽  
Initial Conditions ◽  
Optimization Methods ◽  
Small Region ◽  
Temperature Cycle ◽  
Unknown Boundary ◽  
Thermal Systems ◽  
Data Points ◽  
Unknown Boundary Conditions

Abstract A common occurrence in many practical systems is that the desired result is known or given, but the conditions needed for achieving this result are not known. This situation leads to inverse problems, which are of particular interest in thermal processes. For instance, the temperature cycle to which a component must be subjected in order to obtain desired characteristics in a manufacturing system, such as heat treatment or plastic thermoforming, is prescribed. However, the necessary boundary and initial conditions are not known and must be determined by solving the inverse problem. Similarly, an inverse solution may be needed to complete a given physical problem by determining the unknown boundary conditions. Solutions thus obtained are not unique and optimization is generally needed to obtain results within a small region of uncertainty. This review paper discusses several inverse problems that arise in a variety of practical processes and presents some of the approaches that may be used to solve them and obtain acceptable and realistic results. Optimization methods that may be used for reducing the error are presented. A few examples are given to illustrate the applicability of these methods and the challenges that must be addressed in solving inverse problems. These examples include the heat treatment process, unknown wall temperature distribution in a furnace, and transport in a plume or jet involving the determination of the strength and location of the heat source by employing a few selected data points downstream. Optimization of the positioning of the data points is used to minimize the number of samples needed for accurate predictions.

scholarly journals Fractional ridge regression: a fast, interpretable reparameterization of ridge regression

GigaScience ◽  
2020 ◽  
Vol 9 (12) ◽  
Author(s):  
Ariel Rokem ◽  
Kendrick Kay
Keyword(s):  
Ridge Regression ◽  
Large Scale ◽  
Imaging Data ◽  
Regularization Technique ◽  
Large Scale Data ◽  
Novel Approach ◽  
Manual Exploration ◽  
L2 Norm ◽  
Software Implementations ◽  
Brain Imaging Data

Abstract Background Ridge regression is a regularization technique that penalizes the L2-norm of the coefficients in linear regression. One of the challenges of using ridge regression is the need to set a hyperparameter (α) that controls the amount of regularization. Cross-validation is typically used to select the best α from a set of candidates. However, efficient and appropriate selection of α can be challenging. This becomes prohibitive when large amounts of data are analyzed. Because the selected α depends on the scale of the data and correlations across predictors, it is also not straightforwardly interpretable. Results The present work addresses these challenges through a novel approach to ridge regression. We propose to reparameterize ridge regression in terms of the ratio γ between the L2-norms of the regularized and unregularized coefficients. We provide an algorithm that efficiently implements this approach, called fractional ridge regression, as well as open-source software implementations in Python and matlab (https://github.com/nrdg/fracridge). We show that the proposed method is fast and scalable for large-scale data problems. In brain imaging data, we demonstrate that this approach delivers results that are straightforward to interpret and compare across models and datasets. Conclusion Fractional ridge regression has several benefits: the solutions obtained for different γ are guaranteed to vary, guarding against wasted calculations; and automatically span the relevant range of regularization, avoiding the need for arduous manual exploration. These properties make fractional ridge regression particularly suitable for analysis of large complex datasets.

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