A proposed mathematical model for discharge curves of Li-Ion batteries

2013 ◽  
Author(s):  
Eva Aneiros ◽  
David Lobo ◽  
Alberto Lopez ◽  
Roberto Alvarez
Keyword(s):  
Mathematical Model ◽  
Li Ion Batteries ◽  
Li Ion ◽  
Discharge Curves

Novel Low-Temperature Electrolyte for Li-Ion Battery

2011 ◽  
Vol 287-290 ◽  
pp. 1283-1289 ◽  
Author(s):  
Yong Huan Ren ◽  
Chun Wei Yang ◽  
Bo Rong Wu ◽  
Cun Zhong Zhang ◽  
Shi Chen ◽  
...  
Keyword(s):  
Low Temperature ◽  
Room Temperature ◽  
Poor Performance ◽  
Temperature Series ◽  
Li Ion Batteries ◽  
Li Ion Battery ◽  
Half Cell ◽  
Li Ion ◽  
Charge Discharge ◽  
Discharge Curves

In order to overcome the limitation of Li-ion batteries at low temperature, series of electrolytes are prepared. Specially，FEC is chose to work as electrolyte solvent to enhance its poor performance. Electrolytes are composed of EC, PC, EMC and FEC, while VC is added as additive. Electrolytes with different ratio are examined, then the electrolyte with the best conductivity is studied in detail. Its characters are evaluated by CV, EIS and charge/discharge tests et al. The discharge curves of LiCo1/3Ni1/3Mn1/3O2/Li show that battery with this FEC-based electrolyte at 233K could yield 51% of room temperature capacity. Most obviously, MCMB/Li half cell with this electrolyte could fill 91% of its normal capacity at 233K while batteries barely charge any with traditional electrolyte（LiPF6/EC+DMC(1:1 in volume)）. This nice charge behavior won’t emerge unless the conductivity could basically meet the demand at 233K. The property of FEC-based electrolyte outweighs commercialized electrolyte as this article confirms.

Estimation of Depth of Discharge of Li-ion Battery Using Neural Network

2021 ◽  
Vol 105 (1) ◽  
pp. 541-547
Author(s):  
Radoslav Cipin ◽  
Marek Toman ◽  
Petr Prochazka ◽  
Ivo Pazdera
Keyword(s):  
Neural Network ◽  
Measured Data ◽  
Feedforward Neural Network ◽  
Li Ion Batteries ◽  
Li Ion Battery ◽  
Counting Method ◽  
Depth Of Discharge ◽  
Li Ion ◽  
Discrete Values ◽  
Discharge Curves

This paper deals with the estimation of depth of discharge for Li-ion batteries. Estimation is based on the knowledge of discharging curves measured for discrete values of loading currents. The estimator of the depth of discharge is a form of feedforward neural network which is trained with the measured data of discharge curves. Accuracy of estimation of the depth of discharge is shown for arbitrary generated and measured loading characteristics, where the depth of discharge is estimated by the designed neural network and measured by using the Coulomb counting method.

Electrochemical and Safety Performances of Polyimide Nano fiber-based Nonwoven Separators for Li-ion Batteries

2015 ◽  
Vol 6 (1) ◽  
pp. 26-33 ◽  
Author(s):  
Yeon-Joo Kim ◽  
Sang-Min Lee ◽  
Seok Hong Kim ◽  
Hyun-Soo Kim
Keyword(s):  
Li Ion Batteries ◽  
Nano Fiber ◽  
Li Ion

Lithiation Mechanisms in High Entropy Oxides as Anode Materials for Li-Ion Batteries

2020 ◽  
Author(s):  
Paolo Ghigna ◽  
Lorenzo Airoldi ◽  
Martina Fracchia ◽  
Umberto Anselmi-Tamburini ◽  
Paola D'angelo ◽  
...  
Keyword(s):  
Anode Materials ◽  
Li Ion Batteries ◽  
High Entropy ◽  
Li Ion

Rationally Designed Three-Dimensional Porous Nico 2n@C Reticular Structure for High-Performance Li-Ion Batteries

2020 ◽  
Author(s):  
Peiyao Wang ◽  
Bangchuan Zhao ◽  
Jin Bai ◽  
Kunzhen Li ◽  
Hongyang Ma ◽  
...  
Keyword(s):  
High Performance ◽  
Three Dimensional ◽  
Li Ion Batteries ◽  
Reticular Structure ◽  
Li Ion
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