scholarly journals Composition-Structure Relationships in the Li-Ion Battery Electrode Material LiNi0.5Mn1.5O4

2012 ◽  
Vol 24 (15) ◽  
pp. 2952-2964 ◽  
Author(s):  
Jordi Cabana ◽  
Montserrat Casas-Cabanas ◽  
Fredrick O. Omenya ◽  
Natasha A. Chernova ◽  
Dongli Zeng ◽  
...  
Keyword(s):  
Electrode Material ◽  
Li Ion Battery ◽  
Battery Electrode ◽  
Li Ion ◽  
Composition Structure

In Situ Measurements of Li Ion Battery Electrode Material Conductivity:  Application to LixCoO2and Conversion Reactions

2007 ◽  
Vol 111 (26) ◽  
pp. 9624-9630 ◽  
Author(s):  
F. Sauvage ◽  
J-M. Tarascon ◽  
E. Baudrin
Keyword(s):  
Electrode Material ◽  
In Situ Measurements ◽  
Li Ion Battery ◽  
Battery Electrode ◽  
Li Ion

Ternary Sn–Co–C Li-ion battery electrode material prepared by high energy ball milling

2007 ◽  
Vol 9 (8) ◽  
pp. 2075-2081 ◽  
Author(s):  
J HASSOUN ◽  
G MULAS ◽  
S PANERO ◽  
B SCROSATI
Keyword(s):  
Ball Milling ◽  
Electrode Material ◽  
High Energy ◽  
High Energy Ball Milling ◽  
Li Ion Battery ◽  
Battery Electrode ◽  
Energy Ball ◽  
Li Ion

scholarly journals Degradation Analysis of LiCoO2 Positive Electrode Material of a Li-Ion Battery Using the Li K-Edge Signal Obtained from STEM-EELS Measurements

2015 ◽  
Vol 13 (0) ◽  
pp. 284-288 ◽  
Author(s):  
Noboru Taguchi ◽  
Hikari Sakaebe ◽  
Kuniaki Tatsumi ◽  
Tomoki Akita
Keyword(s):  
Electrode Material ◽  
Positive Electrode ◽  
Li Ion Battery ◽  
Positive Electrode Material ◽  
Degradation Analysis ◽  
Li Ion

scholarly journals In-line monitoring of Li-ion battery electrode porosity and areal loading using active thermal scanning - modeling and initial experiment

2018 ◽  
Vol 375 ◽  
pp. 138-148 ◽  
Author(s):  
Przemyslaw Rupnowski ◽  
Michael Ulsh ◽  
Bhushan Sopori ◽  
Brian G. Green ◽  
David L. Wood ◽  
...  
Keyword(s):  
Li Ion Battery ◽  
Initial Experiment ◽  
Battery Electrode ◽  
Li Ion

LiFePO4 wrapped reduced graphene oxide for high performance Li-ion battery electrode

2015 ◽  
Vol 50 (12) ◽  
pp. 4244-4249 ◽  
Author(s):  
D. H. Nagaraju ◽  
Mirjana Kuezma ◽  
G. S. Suresh
Keyword(s):  
Graphene Oxide ◽  
Reduced Graphene Oxide ◽  
High Performance ◽  
Li Ion Battery ◽  
Reduced Graphene ◽  
Battery Electrode ◽  
Li Ion

scholarly journals A Model for Investigating Sources of Li-Ion Battery Electrode Heterogeneity: Part I. Electrode Drying and Calendering Processes

2021 ◽  
Author(s):  
Mojdeh Nikpour ◽  
Nathan Barrett ◽  
Zachary Hillman ◽  
Andrea Thompson ◽  
Brian A Mazzeo ◽  
...  
Keyword(s):  
Li Ion Battery ◽  
Battery Electrode ◽  
Li Ion

Considerations in Applying Neutron Depth Profiling (NDP) to Li-Ion Battery Research

2022 ◽  
Author(s):  
Daniel J. Lyons ◽  
Jamie L. Weaver ◽  
Anne C. Co
Keyword(s):  
Electrode Materials ◽  
Depth Profiling ◽  
Li Ion Battery ◽  
Neutron Depth Profiling ◽  
Battery Electrode ◽  
Li Ion

Li distribution within micron-scale battery electrode materials is quantified with neutron depth profiling (NDP). This method allows the determination of intra- and inter-electrode parameters such as lithiation efficiency, electrode morphology...

scholarly journals Machine Learning Approaches for Designing Mesoscale Structure of Li-Ion Battery Electrodes

Batteries ◽  
2019 ◽  
Vol 5 (3) ◽  
pp. 54 ◽  
Author(s):  
Yoichi Takagishi ◽  
Takumi Yamanaka ◽  
Tatsuya Yamaue
Keyword(s):  
Machine Learning ◽  
Process Parameters ◽  
Volume Fraction ◽  
Specific Resistance ◽  
Active Material ◽  
Li Ion Battery ◽  
Battery Electrode ◽  
Li Ion ◽  
Simulation Results ◽  
Charge Discharge

We have proposed a data-driven approach for designing the mesoscale porous structures of Li-ion battery electrodes, using three-dimensional virtual structures and machine learning techniques. Over 2000 artificial 3D structures, assuming a positive electrode composed of randomly packed spheres as the active material particles, are generated, and the charge/discharge specific resistance has been evaluated using a simplified physico-chemical model. The specific resistance from Li diffusion in the active material particles (diffusion resistance), the transfer specific resistance of Li+ in the electrolyte (electrolyte resistance), and the reaction resistance on the interface between the active material and electrolyte are simulated, based on the mass balance of Li, Ohm’s law, and the linearized Butler–Volmer equation, respectively. Using these simulation results, regression models, using an artificial neural network (ANN), have been created in order to predict the charge/discharge specific resistance from porous structure features. In this study, porosity, active material particle size and volume fraction, pressure in the compaction process, electrolyte conductivity, and binder/additives volume fraction are adopted, as features associated with controllable process parameters for manufacturing the battery electrode. As a result, the predicted electrode specific resistance by the ANN regression model is in good agreement with the simulated values. Furthermore, sensitivity analyses and an optimization of the process parameters have been carried out. Although the proposed approach is based only on the simulation results, it could serve as a reference for the determination of process parameters in battery electrode manufacturing.

scholarly journals Numerical analysis of the cyclic mechanical damage of Li-ion battery electrode and experimental validation

2021 ◽  
Vol 142 ◽  
pp. 105915 ◽  
Author(s):  
Xuanchen Zhu ◽  
Yu Xie ◽  
Haofeng Chen ◽  
Weiling Luan
Keyword(s):  
Numerical Analysis ◽  
Experimental Validation ◽  
Mechanical Damage ◽  
Li Ion Battery ◽  
Battery Electrode ◽  
Li Ion
Sign in / Sign up

Export Citation Format

Share Document