Finite Element Analysis Using Free Mesh Method

2016 ◽  
Vol 835 ◽  
pp. 661-664
Author(s):  
Joon Seong Lee
Keyword(s):  
Smart Phone ◽  
Lithium Ion ◽  
Rechargeable Batteries ◽  
Battery Life ◽  
Thin Wall ◽  
Element Analysis ◽  
Mesh Method ◽  
Practical Performance ◽  
Element Free Method ◽  
Element Free

Recently, consumers are often dissatisfied with the battery life from even the most advanced lithium-ion rechargeable batteries in mobile phone. A mobile battery was chosen in this study because it is the flat plate of small thin wall which is expected to have some flexing. It is required to be evaluated and designed considering the coupled phenomena. Especially, this paper describes a meshless of element-free method based on fuzzy knowledge processing for battery of smart phone. Practical performance of the present method is demonstrated through heat conduction analysis.

Strength Analysis of Epoxy Molding Compound Module Using Automated FEA System

2014 ◽  
Vol 670-671 ◽  
pp. 852-855
Author(s):  
Joon Seong Lee ◽  
Hee Rok Hong ◽  
Gye Hyun Jo ◽  
Dong Keun Park
Keyword(s):  
Lithium Ion ◽  
Rechargeable Batteries ◽  
Strength Analysis ◽  
Battery Life ◽  
Thin Wall ◽  
Element Analysis ◽  
Epoxy Molding Compound ◽  
Molding Compound ◽  
Finite Element Analysis Simulation ◽  
Analysis System

Recently, consumers are often dissatisfied with the battery life from even the most advanced lithium-ion rechargeable batteries in mobile phone. A mobile battery was chosen in this study because it is the flat plate of small thin wall which is expected to have some flexing. It is required to be evaluated and designed considering the coupled phenomena. Especially, this paper describes finite element analysis simulation of strength evaluation for epoxy molding compound module using automated FE analysis system.

Finite Element Analysis for Ladle Apparatus of Casting Using Free-Mesh Method

2014 ◽  
Vol 680 ◽  
pp. 561-565
Author(s):  
Joon Seong Lee ◽  
Gye Hyeon Jo
Keyword(s):  
Finite Element Analysis ◽  
Finite Element ◽  
Present System ◽  
Knowledge Processing ◽  
Element Analysis ◽  
Mesh Method ◽  
Practical Performance ◽  
Element Free Method ◽  
Element Free ◽  
Fuzzy Knowledge

This paper describes a meshless of element-free method based on fuzzy knowledge processing. Node is generated if its distance from existing node points is similar to the node spacing function at the point. The node spacing function is well controlled by the fuzzy knowledge processing. Practical performance of the present system is demonstrated through ladle apparatus of casting solutions.

Recent advances in ferromagnetic metal sulfides and selenides as anodes for sodium- and potassium-ion batteries

2021 ◽  
Author(s):  
Yuhan Wu ◽  
Chenglin Zhang ◽  
Huaping Zhao ◽  
Yong Lei
Keyword(s):  
Lithium Ion Batteries ◽  
Lithium Ion ◽  
Rechargeable Batteries ◽  
Potassium Ion ◽  
Sodium Ion ◽  
Metal Sulfides ◽  
Sodium Ion Batteries ◽  
Next Generation ◽  
Cost Competitiveness ◽  
Sodium And Potassium

In next-generation rechargeable batteries, sodium-ion batteries (SIBs) and potassium-ion batteries (PIBs) have been considered as attractive alternatives to lithium-ion batteries due to their cost competitiveness. Anodes with complicated electrochemical mechanisms...

scholarly journals Separation and Efficient Recovery of Lithium from Spent Lithium-Ion Batteries

Metals ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 1091
Author(s):  
Eva Gerold ◽  
Stefan Luidold ◽  
Helmut Antrekowitsch
Keyword(s):  
Lithium Ion Batteries ◽  
Electric Vehicles ◽  
Sodium Phosphate ◽  
Room Temperature ◽  
State Of The Art ◽  
Potassium Phosphate ◽  
Lithium Ion ◽  
Rechargeable Batteries ◽  
Raw Material ◽  
Efficient Recovery

The consumption of lithium has increased dramatically in recent years. This can be primarily attributed to its use in lithium-ion batteries for the operation of hybrid and electric vehicles. Due to its specific properties, lithium will also continue to be an indispensable key component for rechargeable batteries in the next decades. An average lithium-ion battery contains 5–7% of lithium. These values indicate that used rechargeable batteries are a high-quality raw material for lithium recovery. Currently, the feasibility and reasonability of the hydrometallurgical recycling of lithium from spent lithium-ion batteries is still a field of research. This work is intended to compare the classic method of the precipitation of lithium from synthetic and real pregnant leaching liquors gained from spent lithium-ion batteries with sodium carbonate (state of the art) with alternative precipitation agents such as sodium phosphate and potassium phosphate. Furthermore, the correlation of the obtained product to the used type of phosphate is comprised. In addition, the influence of the process temperature (room temperature to boiling point), as well as the stoichiometric factor of the precipitant, is investigated in order to finally enable a statement about an efficient process, its parameter and the main dependencies.

scholarly journals Novel Practical Life Cycle Prediction Method by Entropy Estimation of Li-Ion Battery

Electronics ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 487
Author(s):  
Tae-Kue Kim ◽  
Sung-Chun Moon
Keyword(s):  
Lithium Ion Batteries ◽  
Prediction Method ◽  
Lithium Ion ◽  
Estimation Accuracy ◽  
Battery Life ◽  
Energy Storage Devices ◽  
Storage Devices ◽  
Basic Law ◽  
Life Problems ◽  
Li Ion

The growth of the lithium-ion battery market is accelerating. Although they are widely used in various fields, ranging from mobile devices to large-capacity energy storage devices, stability has always been a problem, which is a critical disadvantage of lithium-ion batteries. If the battery is unstable, which usually occurs at the end of its life, problems such as overheating and overcurrent during charge-discharge increase. In this paper, we propose a method to accurately predict battery life in order to secure battery stability. Unlike the existing methods, we propose a method of assessing the life of a battery by estimating the irreversible energy from the basic law of entropy using voltage, current, and time in a realistic dimension. The life estimation accuracy using the proposed method was at least 91.6%, and the accuracy was higher than 94% when considering the actual used range. The experimental results proved that the proposed method is a practical and effective method for estimating the life of lithium-ion batteries.

scholarly journals A Material Model for the Orthotropic and Viscous Behavior of Separators in Lithium-Ion Batteries under High Mechanical Loads

Energies ◽  
2021 ◽  
Vol 14 (15) ◽  
pp. 4585
Author(s):  
Marian Bulla ◽  
Stefan Kolling ◽  
Elham Sahraei
Keyword(s):  
Mechanical Behavior ◽  
Lithium Ion ◽  
Material Model ◽  
Tensile Tests ◽  
Element Analysis ◽  
Rate Dependent ◽  
Novel Material ◽  
Li Ion ◽  
Role Based ◽  
Drive Systems

The present study is focused on the development of a material model where the orthotropic-visco-elastic and orthotropic-visco-plastic mechanical behavior of a polymeric material is considered. The increasing need to reduce the climate-damaging exhaust gases in the automotive industry leads to an increasing usage of electric powered drive systems using Lithium-ion (Li-ion) batteries. For the safety and crashworthiness investigations, a deeper understanding of the mechanical behavior under high and dynamic loads is needed. In order to prevent internal short circuits and thermal runaways within a Li-ion battery, the separator plays a crucial role. Based on results of material tests, a novel material model for finite element analysis (FEA) is developed using the explicit solver Altair Radioss. Based on this model, the visco-elastic-orthotropic, as well as the visco-plastic-orthotropic, behavior until failure can be modeled. Finally, a FE simulation model of the separator material is performed, using the results of different tensile tests conducted at three different velocities, 0.1 mm·s−1, 1.0 mm·s−1 and 10.0 mm·s−1 and different orientations of the specimen. The purpose is to predict the anisotropic, rate-dependent stiffness behavior of separator materials in order to improve FE simulations of the mechanical behavior of batteries and therefore reduce the development time of electrically powered vehicles and consumer goods. The present novel material model in combination with a well-suited failure criterion, which considers the different states of stress and anisotropic-visco-dependent failure limits, can be applied for crashworthiness FE analysis. The model succeeded in predicting anisotropic, visco-elastic orthotropic and visco-plastic orthotropic stiffness behavior up to failure.

Experimental and Numerical Investigation on Radial Stiffness of Origami-inspired Tubular Structures

2021 ◽  
pp. 1-30
Author(s):  
Weijun Shen ◽  
Yang Cao ◽  
Xuepeng Jiang ◽  
Zhan Zhang ◽  
Gül E. Okudan Kremer ◽  
...  
Keyword(s):  
Circular Tube ◽  
Weight Ratio ◽  
Design Parameters ◽  
Thin Wall ◽  
Tubular Structures ◽  
Compression Tests ◽  
Element Analysis ◽  
Radial Stiffness ◽  
Paper Folding ◽  
Engineering Problems

Abstract Origami structures, which were inspired by traditional paper folding arts, have been applied for engineering problems for the last two decades. Origami-based thin-wall tubes have been extensively investigated under axial loadings. However, less has been done with radial stiffness as one of the critical mechanical properties of a tubular structure working under lateral loadings. In this study, the radial stiffness of novel thin-wall tubular structures based on origami patterns have been studied with compression tests and finite element analysis (FEA) simulations. The results show that the radial stiffness of an origami-inspired tube can achieve about 27.1 times that of a circular tube with the same circumcircle diameter (100 mm), height (60 mm), and wall-thickness (2 mm). Yoshimura, Kresling, and modified Yoshimura patterns are selected as the basic frames, upon which the influences of different design parameters are tested and discussed. Given that the weight can vary due to different designs, the stiffness-to-weight ratio is also calculated. The origami-inspired tubular structures with superior stiffness performances are obtained and can be extended to crashworthy structures, functional structures, and stiffness enhancement with low structural weight.

scholarly journals Applications of Nanoscale Materials in the Fields of Electrochemistry and Photoelectrochemistry

1998 ◽  
Vol 21 (2) ◽  
pp. 123-146 ◽  
Author(s):  
G. Campet ◽  
A. Deshayes ◽  
J. C. Frison ◽  
N. Treuil ◽  
J. Portier
Keyword(s):  
Energy Storage ◽  
Lithium Batteries ◽  
Nanocrystalline Materials ◽  
Lithium Ion ◽  
Electrical Energy ◽  
Rechargeable Batteries ◽  
Photoelectrochemical Cells ◽  
Nanoscale Materials ◽  
Nanocrystalline Films ◽  
Electrical Energy Storage

We have illustrated the important role played by the nanoscale materials in three-up-to-date energy topics.1/The solar-to-electrical energy conversion in photoelectrochemical cells: we have shown two favorable situations for which photoelectrochemical cells using porous nanocrystalline films have high efficiencies.2/The electrical energy storage in rechargeable rocking-chair lithium batteries: these systems, which use nanocrystalline materials, might be the next generation of rechargeable batteries showing higher capacity, cyclability, and safety than conventional lithium ion batteries.3/The energy saving with efficient electrochromic windows using nanocrystalline materials.

Online Estimation of Lithium-Ion Battery Capacity Using Deep Convolutional Neural Networks

2018 ◽  
Author(s):  
Sheng Shen ◽  
M. K. Sadoughi ◽  
Xiangyi Chen ◽  
Mingyi Hong ◽  
Chao Hu
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Lithium Ion ◽  
Relevance Vector Machine ◽  
Rechargeable Batteries ◽  
Li Ion Battery ◽  
Deep Convolutional Neural Networks ◽  
Battery Capacity ◽  
Li Ion

Over the past two decades, safety and reliability of lithium-ion (Li-ion) rechargeable batteries have been receiving a considerable amount of attention from both industry and academia. To guarantee safe and reliable operation of a Li-ion battery pack and build failure resilience in the pack, battery management systems (BMSs) should possess the capability to monitor, in real time, the state of health (SOH) of the individual cells in the pack. This paper presents a deep learning method, named deep convolutional neural networks, for cell-level SOH assessment based on the capacity, voltage, and current measurements during a charge cycle. The unique features of deep convolutional neural networks include the local connectivity and shared weights, which enable the model to estimate battery capacity accurately using the measurements during charge. To our knowledge, this is the first attempt to apply deep learning to online SOH assessment of Li-ion battery. 10-year daily cycling data from implantable Li-ion cells are used to verify the performance of the proposed method. Compared with traditional machine learning methods such as relevance vector machine and shallow neural networks, the proposed method is demonstrated to produce higher accuracy and robustness in capacity estimation.

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