scholarly journals Comparing Different Thermal Runaway Triggers for Two Automotive Lithium-Ion Battery Cell Types

2020 ◽  
Vol 167 (13) ◽  
pp. 130542
Author(s):  
C. Essl ◽  
A. W. Golubkov ◽  
A. Fuchs
Keyword(s):  
Lithium Ion Battery ◽  
Lithium Ion ◽  
Cell Types ◽  
Thermal Runaway ◽  
Battery Cell

Thermal Simulation Analysis of a Lithium-Ion Battery

2021 ◽  
Vol 4 (1) ◽  
Keyword(s):  
Lithium Ion Battery ◽  
Electric Vehicles ◽  
Heat Generation ◽  
Lithium Ion ◽  
High Energy ◽  
Thermal Runaway ◽  
Transport Processes ◽  
High Energy Density ◽  
Battery Cell ◽  
Discharge Rates

Lithium – Ion batteries are now extensively used in electric vehicles (EV) as well as in renewable power generation applications for both on-grid and off grid storage. Some of the major challenges with batteries for electric vehicles are the requirement of high energy density, compatibility with high charge and discharge rates while maintaining high performance, and prevention of any thermal runaway conditions. The objective of this research is to develop a computer simulation model for coupled electrochemical and thermal analysis and characterization of a lithium-ion battery performance subject to a range of charge and discharge loading, and thermal environmental conditions. The electrochemical model includes species and charge transport through the liquid and solid phases of electrode and electrolyte layers along with electrode kinetics. The thermal model includes several heat generation components such as reversible, irreversible and ohmic heating, and heat dissipation through layers of battery cell. Simulation is carried out to evaluate the electrochemical and thermal behavior with varying discharge rates. Results demonstrated a strong variation in the activation and ohmic polarization losses as well as in higher heat generation rates. Results show variation of different modes and order of cell heat generation rates that results in a higher rate of cell temperature rise as battery cell is subjected to higher discharge rates. The model developed will help in gaining a comprehensive insights of the complex transport processes in a cell and can form a platform for evaluating number new candidates for battery chemistry for enhanced battery performance and address safety issues associated with thermal runaway.

scholarly journals Probing the thermal runaway triggering process within a lithium-ion battery cell with local heating

AIP Advances ◽  
2018 ◽  
Vol 8 (10) ◽  
pp. 105323 ◽  
Author(s):  
X. M. Xu ◽  
R. Z. Li ◽  
L. Zhao ◽  
D. H. Hu ◽  
J. Wang
Keyword(s):  
Lithium Ion Battery ◽  
Local Heating ◽  
Lithium Ion ◽  
Thermal Runaway ◽  
Battery Cell ◽  
Triggering Process

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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