New Insight into Differences in Cycling Behaviors of a Lithium-ion Battery Cell Between the Ethylene Carbonate- and Propylene Carbonate-Based Electrolytes

2019 ◽  
Vol 33 (28) ◽  
pp. 59-69 ◽  
Author(s):  
Ken Tasaki ◽  
Alexander Goldberg ◽  
Jian-Jie Liang ◽  
Martin Winter
Keyword(s):  
Lithium Ion Battery ◽  
Propylene Carbonate ◽  
Ethylene Carbonate ◽  
Lithium Ion ◽  
Battery Cell ◽  
Insight Into

New Insight into Electrochemical Differences in Cycling Behaviors of a Lithium-ion Battery Cell Between the Ethylene Carbonate- and Propylene Carbonate-Based Electrolytes

2011 ◽  
Vol 1313 ◽  
Author(s):  
Ken Tasaki ◽  
Alexander Goldberg ◽  
Jian-Jie Liang ◽  
Martin Winter
Keyword(s):  
Dft Calculations ◽  
Lithium Ion Battery ◽  
Propylene Carbonate ◽  
Salt Concentration ◽  
Density Functional ◽  
Ethylene Carbonate ◽  
Lithium Ion ◽  
Md Simulations ◽  
High Salt ◽  
Insight Into

ABSTRACTDensity functional theory (DFT) calculations and classical molecular dynamics (MD) simulations have been performed to gain insight into the difference in cycling behaviors between the ethylene carbonate (EC)-based and the propylene carbonate (PC)-based electrolytes in lithium-ion battery cells. DFT calculations for the ternary graphite intercalation compounds (Li+(S)iCn: S=EC or PC), in which the solvated lithium ion Li+(S)i (i=1~3) was inserted into a graphite cell, suggested that Li+(EC)iCn was more stable than Li+(PC)iCn in general. Furthermore, Li+(PC)3Cn was found to be energetically unfavorable, while Li+(PC)2Cn was stable, relative to their corresponding Li+(PC)i in the bulk electrolyte. The calculations also revealed severe structural distortions of the PC molecule in Li+(PC)3Cn, suggesting a rapid kinetic effect on PC decomposition reactions, as compared to decompositions of EC. In addition, MD simulations were carried out to examine the solvation structures at a high salt concentration: 2.45 mo kg-1. The results showed that the solvation structure was significantly interrupted by the counter anions, having a smaller solvation number than that at a lower salt concentration (0.83 mol kg-1). We propose that at high salt concentrations, the lithium desolvation may be facilitated due to the increased contact ion pairs, so that a stable ternary GIC with less solvent molecules can be formed without the destruction of graphite particles, followed by solid-electrolyte-interface film formation reactions. The results from both DFT calculations and MD simulations are consistent with the recent experimental observations.

Theoretical Insight into Oxidative Decomposition of Propylene Carbonate in the Lithium Ion Battery

2009 ◽  
Vol 113 (15) ◽  
pp. 5181-5187 ◽  
Author(s):  
Lidan Xing ◽  
Chaoyang Wang ◽  
Weishan Li ◽  
Mengqing Xu ◽  
Xuliang Meng ◽  
...  
Keyword(s):  
Lithium Ion Battery ◽  
Propylene Carbonate ◽  
Lithium Ion ◽  
Oxidative Decomposition ◽  
Theoretical Insight ◽  
Insight Into

scholarly journals Effect of dynamic loads and vibrations on lithium-ion batteries

2021 ◽  
pp. 146134842110081
Author(s):  
Xia Hua ◽  
Alan Thomas
Keyword(s):  
Lithium Ion Batteries ◽  
Lithium Ion Battery ◽  
Experimental Studies ◽  
Lithium Ion ◽  
Battery Pack ◽  
Dynamic Loads ◽  
Electrical Performance ◽  
Mechanical Degradation ◽  
Energy Storage Devices ◽  
Battery Cell

Lithium-ion batteries are being increasingly used as the main energy storage devices in modern mobile applications, including modern spacecrafts, satellites, and electric vehicles, in which consistent and severe vibrations exist. As the lithium-ion battery market share grows, so must our understanding of the effect of mechanical vibrations and shocks on the electrical performance and mechanical properties of such batteries. Only a few recent studies investigated the effect of vibrations on the degradation and fatigue of battery cell materials as well as the effect of vibrations on the battery pack structure. This review focused on the recent progress in determining the effect of dynamic loads and vibrations on lithium-ion batteries to advance the understanding of lithium-ion battery systems. Theoretical, computational, and experimental studies conducted in both academia and industry in the past few years are reviewed herein. Although the effect of dynamic loads and random vibrations on the mechanical behavior of battery pack structures has been investigated and the correlation between vibration and the battery cell electrical performance has been determined to support the development of more robust electrical systems, it is still necessary to clarify the mechanical degradation mechanisms that affect the electrical performance and safety of battery cells.

Post-lithium-ion battery cell production and its compatibility with lithium-ion cell production infrastructure

Nature Energy ◽  
2021 ◽  
Vol 6 (2) ◽  
pp. 123-134
Author(s):  
Fabian Duffner ◽  
Niklas Kronemeyer ◽  
Jens Tübke ◽  
Jens Leker ◽  
Martin Winter ◽  
...  
Keyword(s):  
Lithium Ion Battery ◽  
Lithium Ion ◽  
Battery Cell ◽  
Cell Production ◽  
Lithium Ion Cell

scholarly journals Box–Jenkins Black-Box Modeling of a Lithium-Ion Battery Cell Based on Automotive Drive Cycle Data

2021 ◽  
Vol 12 (3) ◽  
pp. 102
Author(s):  
Jaouad Khalfi ◽  
Najib Boumaaz ◽  
Abdallah Soulmani ◽  
El Mehdi Laadissi
Keyword(s):  
Lithium Ion Battery ◽  
Linear Model ◽  
Goodness Of Fit ◽  
Transfer Functions ◽  
Mean Squared Error ◽  
Lithium Ion ◽  
Drive Cycle ◽  
Cell Dynamics ◽  
Battery Cell ◽  
Parameter Identifications

The Box–Jenkins model is a polynomial model that uses transfer functions to express relationships between input, output, and noise for a given system. In this article, we present a Box–Jenkins linear model for a lithium-ion battery cell for use in electric vehicles. The model parameter identifications are based on automotive drive-cycle measurements. The proposed model prediction performance is evaluated using the goodness-of-fit criteria and the mean squared error between the Box–Jenkins model and the measured battery cell output. A simulation confirmed that the proposed Box–Jenkins model could adequately capture the battery cell dynamics for different automotive drive cycles and reasonably predict the actual battery cell output. The goodness-of-fit value shows that the Box–Jenkins model matches the battery cell data by 86.85% in the identification phase, and 90.83% in the validation phase for the LA-92 driving cycle. This work demonstrates the potential of using a simple and linear model to predict the battery cell behavior based on a complex identification dataset that represents the actual use of the battery cell in an electric vehicle.

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