scholarly journals Parameter Identification and Sensitivity Analysis for Zero-Dimensional Physics-Based Lithium-Sulfur Battery Models

2021 ◽  
Vol 1 (4) ◽  
Author(s):  
Chu Xu ◽  
Timothy Cleary ◽  
Guoxing Li ◽  
Donghai Wang ◽  
Hosam Fathy
Keyword(s):  
Experimental Data ◽  
Sensitivity Analysis ◽  
Lithium Ion ◽  
Diffusion Reaction ◽  
Model Parameters ◽  
Data Sets ◽  
Diffusion Dynamics ◽  
Dimensional Models ◽  
Sulfur Battery ◽  
Lithium Sulfur

Abstract This paper examines parameter estimation for Lithium-Sulfur (Li-S) battery models from experimental data. Li-S batteries are attractive compared to traditional Lithium-ion batteries, thanks largely to their potential to achieve higher energy densities. The literature presents a number of Li-S battery models with varying fidelity and complexity levels. This includes both high-fidelity diffusion-reaction models as well as zero-dimensional models that neglect diffusion dynamics while capturing the underlying reduction-oxidation reaction physics. This paper focuses on four zero-dimensional models, representing different possible sets of redox reactions. There is a growing need for using experimental data sets to both parameterize and compare these models. To address this, Li-S coin cells were fabricated and tested. In parallel, a sensitivity analysis of key model parameters was conducted. Using this analysis, a subset of model parameters was selected for identification and estimation in all four Li-S battery models.

Potato Peel Based Carbon–Sulfur Composite as Cathode Materials for Lithium Sulfur Battery

2021 ◽  
Vol 21 (12) ◽  
pp. 6243-6247
Author(s):  
Arenst Andreas Arie ◽  
Shealyn Lenora ◽  
Hans Kristianto ◽  
Ratna Frida Susanti ◽  
Joong Kee Lee
Keyword(s):  
Cathode Materials ◽  
Porous Carbon ◽  
Chemical Activation ◽  
Specific Capacity ◽  
Lithium Ion ◽  
Electrochemical Characteristics ◽  
Potato Peel ◽  
Lithium Sulfur Battery ◽  
Sulfur Battery ◽  
Lithium Sulfur

Lithium sulfur battery has become one of the promising rechargeable battery systems to replace the conventional lithium ion battery. Commonly, it uses carbon–sulfur composites as cathode materials. Biomass based carbons has an important role in enhancing its electrochemical characteristics due to the high conductivity and porous structures. Here, potato peel wastes have been utilized to prepare porous carbon lithium sulfur battery through hydrothermal carbonization followed by the chemical activation method using KOH. After sulfur loading, as prepared carbon–sulfur composite shows stable coulombic efficiencies of above 98% and a reversible specific capacity of 804 mAh g−1 after 100 cycles at current density of 100 mA g−1. These excellent electrochemical properties can be attributed to the unique structure of PPWC showing mesoporous structure with large specific surface areas. These results show the potential application of potato peel waste based porous carbon as electrode’s materials for lithium sulfur battery.

A self-supported 3D aerogel network lithium–sulfur battery cathode: sulfur spheres wrapped with phosphorus doped graphene and bridged with carbon nanofibers

2020 ◽  
Vol 8 (16) ◽  
pp. 7980-7990 ◽  
Author(s):  
Junchao Tan ◽  
Dan Li ◽  
Yuqing Liu ◽  
Peng Zhang ◽  
Zehua Qu ◽  
...  
Keyword(s):  
Carbon Nanofibers ◽  
Network Structure ◽  
Lithium Ion ◽  
Lithium Sulfur Battery ◽  
Ion Storage ◽  
Doped Graphene ◽  
Sulfur Battery ◽  
Phosphorus Doped ◽  
Lithium Sulfur

An integrated PGCNF/S aerogel with a “network” structure has effectively restricted the shuttling of polysulfides and exhibited promising lithium ion storage capability.

scholarly journals Technical note: Monte Carlo genetic algorithm (MCGA) for model analysis of multiphase chemical kinetics to determine transport and reaction rate coefficients using multiple experimental data sets

2017 ◽  
Vol 17 (12) ◽  
pp. 8021-8029 ◽  
Author(s):  
Thomas Berkemeier ◽  
Markus Ammann ◽  
Ulrich K. Krieger ◽  
Thomas Peter ◽  
Peter Spichtinger ◽  
...  
Keyword(s):  
Experimental Data ◽  
Genetic Algorithm ◽  
Monte Carlo ◽  
Kinetic Models ◽  
Reaction Rate ◽  
Rate Coefficients ◽  
Model Parameters ◽  
Data Sets ◽  
Model Input ◽  
Input Parameters

Abstract. We present a Monte Carlo genetic algorithm (MCGA) for efficient, automated, and unbiased global optimization of model input parameters by simultaneous fitting to multiple experimental data sets. The algorithm was developed to address the inverse modelling problems associated with fitting large sets of model input parameters encountered in state-of-the-art kinetic models for heterogeneous and multiphase atmospheric chemistry. The MCGA approach utilizes a sequence of optimization methods to find and characterize the solution of an optimization problem. It addresses an issue inherent to complex models whose extensive input parameter sets may not be uniquely determined from limited input data. Such ambiguity in the derived parameter values can be reliably detected using this new set of tools, allowing users to design experiments that should be particularly useful for constraining model parameters. We show that the MCGA has been used successfully to constrain parameters such as chemical reaction rate coefficients, diffusion coefficients, and Henry's law solubility coefficients in kinetic models of gas uptake and chemical transformation of aerosol particles as well as multiphase chemistry at the atmosphere–biosphere interface. While this study focuses on the processes outlined above, the MCGA approach should be portable to any numerical process model with similar computational expense and extent of the fitting parameter space.

Li-Ion Cell Aging Model Online Parameter Estimation for Improved Prognosis

2016 ◽  
Author(s):  
Punit Tulpule ◽  
Chin-Yao Chang ◽  
Giorgio Rizzoni
Keyword(s):  
Sensitivity Analysis ◽  
Parameter Estimation ◽  
Lithium Ion ◽  
Remaining Useful Life ◽  
Estimation Methods ◽  
Cell Aging ◽  
Model Parameters ◽  
Positive Electrodes ◽  
Aging Model ◽  
Estimation Algorithms

In this paper, a semi-empirical aging model of lithium-ion pouch cells containing blended spinel and layered-oxide positive electrodes is calibrated using aging campaigns. Sensitivity analysis is done on this model to identify the effect of parameter variations on the State of Health (SOH) prediction. The sensitivity analysis shows that the aging model alone is not robust enough to perform long term predictions, hence we propose to use online parameter estimation algorithms to adapt the model parameters. Four different estimation methods are compared using aging campaign. It is demonstrated that the estimation algorithms improve aging model leading to significant improvement in Remaining Useful Life (RUL) prediction.

scholarly journals Battery Model Parameters Estimation Using Simulated Annealing

2017 ◽  
Vol 18 (1) ◽  
pp. 127 ◽  
Author(s):  
Marcia De Fatima Brondani ◽  
Airam Teresa Zago Romcy Sausen ◽  
Paulo Sérgio Sausen ◽  
Manuel Osório Binelo
Keyword(s):  
Mathematical Modeling ◽  
Experimental Data ◽  
Simulated Annealing ◽  
Visual Analysis ◽  
Lithium Ion ◽  
Parameters Estimation ◽  
Model Parameters ◽  
Algorithm Efficiency ◽  
Battery Model ◽  
Discharge Curves

In this paper, a Simulated Annealing (SA) algorithm is proposed for the Battery model parametrization, which is used for the mathematical modeling of the Lithium Ion Polymer (LiPo) batteries lifetime. Experimental data obtained by a testbed were used for model parametrization and validation. The proposed SA algorithm is compared to the traditional parametrization methodology that consists in the visual analysis of discharge curves, and from the results obtained, it is possible to see the model efficacy in batteries lifetime prediction, and the proposed SA algorithm efficiency in the parameters estimation.

scholarly journals Strategic Framework for Parameterization of Cell Culture Models

Processes ◽  
2019 ◽  
Vol 7 (3) ◽  
pp. 174
Author(s):  
Pavlos Kotidis ◽  
Cleo Kontoravdi
Keyword(s):  
Experimental Data ◽  
Cell Culture ◽  
Sensitivity Analysis ◽  
Goodness Of Fit ◽  
Sampling Methods ◽  
Model Parameters ◽  
Sensitivity Index ◽  
Cho Cell ◽  
Culture Models ◽  
Cell Culture Models

Global Sensitivity Analysis (GSA) is a technique that numerically evaluates the significance of model parameters with the aim of reducing the number of parameters that need to be estimated accurately from experimental data. In the work presented herein, we explore different methods and criteria in the sensitivity analysis of a recently developed mathematical model to describe Chinese hamster ovary (CHO) cell metabolism in order to establish a strategic, transferable framework for parameterizing mechanistic cell culture models. For that reason, several types of GSA employing different sampling methods (Sobol’, Pseudo-random and Scrambled-Sobol’), parameter deviations (10%, 30% and 50%) and sensitivity index significance thresholds (0.05, 0.1 and 0.2) were examined. The results were evaluated according to the goodness of fit between the simulation results and experimental data from fed-batch CHO cell cultures. Then, the predictive capability of the model was tested against four different feeding experiments. Parameter value deviation levels proved not to have a significant effect on the results of the sensitivity analysis, while the Sobol’ and Scrambled-Sobol’ sampling methods and a 0.1 significance threshold were found to be the optimum settings. The resulting framework was finally used to calibrate the model for another CHO cell line, resulting in a good overall fit. The results of this work set the basis for the use of a single mechanistic metabolic model that can be easily adapted through the proposed sensitivity analysis method to the behavior of different cell lines and therefore minimize the experimental cost of model development.

Effects of transmural pressure and wall shear stress on LDL accumulation in the arterial wall: a numerical study using a multilayered model

2007 ◽  
Vol 292 (6) ◽  
pp. H3148-H3157 ◽  
Author(s):  
Nanfeng Sun ◽  
Nigel B. Wood ◽  
Alun D. Hughes ◽  
Simon A. M. Thom ◽  
X. Yun Xu
Keyword(s):  
Experimental Data ◽  
Shear Stress ◽  
Wall Shear Stress ◽  
Arterial Wall ◽  
Numerical Study ◽  
Transmural Pressure ◽  
Diffusion Reaction ◽  
Model Parameters ◽  
Optimization Approach ◽  
Wall Shear

The accumulation of low-density lipoprotein (LDL) is recognized as one of the main contributors in atherogenesis. Mathematical models have been constructed to simulate mass transport in large arteries and the consequent lipid accumulation in the arterial wall. The objective of this study was to investigate the influences of wall shear stress and transmural pressure on LDL accumulation in the arterial wall by a multilayered, coupled lumen-wall model. The model employs the Navier-Stokes equations and Darcy's Law for fluid dynamics, convection-diffusion-reaction equations for mass balance, and Kedem-Katchalsky equations for interfacial coupling. To determine physiologically realistic model parameters, an optimization approach that searches optimal parameters based on experimental data was developed. Two sets of model parameters corresponding to different transmural pressures were found by the optimization approach using experimental data in the literature. Furthermore, a shear-dependent hydraulic conductivity relation reported previously was adopted. The integrated multilayered model was applied to an axisymmetric stenosis simulating an idealized, mildly stenosed coronary artery. The results show that low wall shear stress leads to focal LDL accumulation by weakening the convective clearance effect of transmural flow, whereas high transmural pressure, associated with hypertension, leads to global elevation of LDL concentration in the arterial wall by facilitating the passage of LDL through wall layers.

scholarly journals Lithium-Sulfur Battery: Chemistry, Challenges, Cost, and Future

2016 ◽  
Vol 9 (2) ◽  
Author(s):  
K. Patel
Keyword(s):  
Life Cycle ◽  
Energy Storage ◽  
Specific Energy ◽  
Lithium Ion ◽  
Development Stage ◽  
Electrical Vehicles ◽  
Complex Chemistry ◽  
The Cost ◽  
Sulfur Battery ◽  
Lithium Sulfur

Lithium sulfur (Li-S) battery has higher theoretical and experimental specific energy. There- fore, Li-S battery is the most capable energy storage option for electrical vehicles and power grid. However, it has life cycle issue to prevent its usage in electrical vehicles. Li-S battery need to im- prove life cycle issue in order to work with current battery applications. Li-S battery has complex chemistry and process to discharge and charge Li-S cell. The cost and manufacturing process of Li-S batteries will be one of the biggest challenge for commercial transformation from research and development stage. The Oxis Energy is developing Li-S battery for past 10 years and they have goal price US$250/kWh by 2020 with high volumes and more cheaper compare to lithium-ion battery.

Fe2O3/rGO/CNT composite sulfur host with physical and chemical dual-encapsulation for high performance lithium-sulfur batteries

2021 ◽  
Author(s):  
Wei Dong ◽  
meng lingqiang ◽  
zhao meina ◽  
yang fang ◽  
Ding Shen ◽  
...  
Keyword(s):  
Lithium Ion Battery ◽  
Cathode Materials ◽  
High Performance ◽  
Lithium Ion ◽  
Lithium Sulfur Battery ◽  
Lithium Sulfur Batteries ◽  
Sulfur Host ◽  
Physical And Chemical ◽  
Sulfur Battery ◽  
Lithium Sulfur

Lithium sulfur battery is one of the promising alternatives to traditional lithium-ion battery, but the dissolution of polysulfides and the low conductivity of cathode materials are two important factors for...

Sign in / Sign up

Export Citation Format

Share Document