Effects of SiO2 particles in copper current collector on diffusion induced stresses in layered Li-ion battery electrodes

2021 ◽  
pp. 095440622110036
Author(s):  
Roozbeh Pouyanmehr ◽  
Morteza Pakseresht ◽  
Reza Ansari ◽  
Mohammad Kazem Hassanzadeh-Aghdam
Keyword(s):  
Volume Fraction ◽  
Lithium Ion ◽  
Current Collector ◽  
Limiting Factors ◽  
Li Ion Battery ◽  
Current Collectors ◽  
Effect Of Particle Size ◽  
Li Ion ◽  
Induced Stresses ◽  
Copper Composite

One of the limiting factors in the life of lithium-ion batteries is the diffusion-induced stresses on their electrodes that cause cracking and consequently, failure. Therefore, improving the structure of these electrodes to be able to withstand these stresses is one of the ways that can extend the life of the batteries as well as improve their safety. In this study, the effects of adding graphene nanoplatelets and microparticles into the active plate and current collectors, respectively, on the diffusion induced stresses in both layered and bilayered electrodes are numerically investigated. The micromechanical models are employed to predict the mechanical properties of both graphene nanoplatelet-reinforced Sn-based nanocomposite active plate and silica microparticle-reinforced copper composite current collector. The effect of particle size and volume fraction in the current collector on diffusion induced stresses has been studied. The results show that in electrodes with a higher volume fraction of particles and smaller particle radii, decreased diffusion induced stresses in both the active plate and the current collector are observed. These additions will also result in a significant decrease in the bending of the electrode.

Nanoscale Materials for Lithium-Ion Batteries

MRS Bulletin ◽  
2002 ◽  
Vol 27 (8) ◽  
pp. 604-607 ◽  
Author(s):  
Charles R. Sides ◽  
Naichao Li ◽  
Charles J. Patrissi ◽  
Bruno Scrosati ◽  
Charles R. Martin
Keyword(s):  
Lithium Ion ◽  
Current Collector ◽  
Template Method ◽  
Li Ion Battery ◽  
Nanostructured Electrode ◽  
Nanostructured Electrodes ◽  
Monodisperse Nanoparticles ◽  
Limiting Step ◽  
Recent Developments ◽  
Li Ion

AbstractTemplate synthesis is a versatile nanomaterial fabrication method used to make monodisperse nanoparticles of a variety of materials including metals, semiconductors, carbons, and polymers. We have used the template method to prepare nanostructured lithium-ion battery electrodes in which nanofibers or nanotubes of the electrode material protrude from an underlying current-collector surface like the bristles of a brush. Nanostructured electrodes of this type composed of carbon, LiMn2O4, V2O5, tin, TiO2, and TiS2 have been prepared. In all cases, the nanostructured electrode showed dramatically improved rate capabilities relative to thin-film control electrodes composed of the same material. The rate capabilities are improved because the distance that Li+ must diffuse in the solid state (the current- and power-limiting step in Li-ion battery electrodes) is significantly smaller in the nanostructured electrode. For example, in a nanofiber-based electrode, the distance that Li+ must diffuse is restricted to the radius of the fiber, which may be as small as 50 nm. Recent developments in template-prepared nanostructured electrodes are reviewed.

scholarly journals Aluminum current collector for high voltage Li-ion battery. Part I: A benchmark study with statistical analysis

2021 ◽  
Vol 126 ◽  
pp. 107013
Author(s):  
Chloé Bizot ◽  
Marie-Anne Blin ◽  
Pierre Guichard ◽  
Jonathan Hamon ◽  
Vincent Fernandez ◽  
...  
Keyword(s):  
Statistical Analysis ◽  
High Voltage ◽  
Current Collector ◽  
Li Ion Battery ◽  
Benchmark Study ◽  
Li Ion

Enhancing Co/Co2VO4 Li-ion Battery Anode Performances via 2D-2D Heterostructure Engineering

Nanoscale ◽  
2021 ◽  
Author(s):  
Kun Wang ◽  
Yongyuan Hu ◽  
Jian Pei ◽  
Fengyang Jing ◽  
Zhongzheng Qin ◽  
...  
Keyword(s):  
Lithium Ion Batteries ◽  
Anode Material ◽  
Output Voltage ◽  
High Capacity ◽  
Lithium Ion ◽  
Li Ion Battery ◽  
Li Ion ◽  
Battery Anode

High capacity Co2VO4 becomes a potential anode material for lithium ion batteries (LIBs) benefiting from its lower output voltage during cycling than other cobalt vanadates. However, the application of this...

Reduced Graphene Oxide Films with Ultrahigh Conductivity as Li-Ion Battery Current Collectors

Nano Letters ◽  
2016 ◽  
Vol 16 (6) ◽  
pp. 3616-3623 ◽  
Author(s):  
Yanan Chen ◽  
Kun Fu ◽  
Shuze Zhu ◽  
Wei Luo ◽  
Yanbin Wang ◽  
...  
Keyword(s):  
Graphene Oxide ◽  
Reduced Graphene Oxide ◽  
Oxide Films ◽  
Li Ion Battery ◽  
Current Collectors ◽  
Reduced Graphene ◽  
Li Ion

Real-Time Estimation of Lithium-Ion Concentration in Both Electrodes of a Lithium-Ion Battery Cell Utilizing Electrochemical–Thermal Coupling

2017 ◽  
Vol 139 (3) ◽  
Author(s):  
Satadru Dey ◽  
Beshah Ayalew
Keyword(s):  
Real Time ◽  
Unscented Kalman Filter ◽  
Lithium Ion ◽  
Time Estimation ◽  
Li Ion Battery ◽  
Battery Cell ◽  
Thermal Dynamics ◽  
Estimation Scheme ◽  
Real Time Estimation ◽  
Li Ion

This paper proposes and demonstrates an estimation scheme for Li-ion concentrations in both electrodes of a Li-ion battery cell. The well-known observability deficiencies in the two-electrode electrochemical models of Li-ion battery cells are first overcome by extending them with a thermal evolution model. Essentially, coupling of electrochemical–thermal dynamics emerging from the fact that the lithium concentrations contribute to the entropic heat generation is utilized to overcome the observability issue. Then, an estimation scheme comprised of a cascade of a sliding-mode observer and an unscented Kalman filter (UKF) is constructed that exploits the resulting structure of the coupled model. The approach gives new real-time estimation capabilities for two often-sought pieces of information about a battery cell: (1) estimation of cell-capacity and (2) tracking the capacity loss due to degradation mechanisms such as lithium plating. These capabilities are possible since the two-electrode model needs not be reduced further to a single-electrode model by adding Li conservation assumptions, which do not hold with long-term operation. Simulation studies are included for the validation of the proposed scheme. Effect of measurement noise and parametric uncertainties is also included in the simulation results to evaluate the performance of the proposed scheme.

Stabilization of lithium metal anodes by conductive metal–organic framework architectures

2021 ◽  
Author(s):  
Jing Zhao ◽  
Hongye Yuan ◽  
Guiling Wang ◽  
Xiao Feng Lim ◽  
Hualin Ye ◽  
...  
Keyword(s):  
Metal Organic Framework ◽  
Current Collector ◽  
Li Ion Batteries ◽  
Lithium Metal ◽  
Seeding Layer ◽  
Current Collectors ◽  
Metal Organic ◽  
Lithium Metal Anodes ◽  
Li Ion ◽  
Cu Foil

The Cu-foil current collectors with Ni3(HITP)2 films were prepared to reduce the energy barrier of the current collector surface and thus provide a uniform seeding layer for the subsequent deposition of Li in Li-ion batteries.

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.

Sign in / Sign up

Export Citation Format

Share Document