Mechanical Anisotropy and Strain-Rate Dependency of a Large Format Lithium-Ion Battery Cell: Experiments and Simulations

2021 ◽  
Author(s):  
Lingxiao Zhu ◽  
Yulong Ge ◽  
Lin Wang ◽  
Lei Zhang ◽  
Yuanjie Liu ◽  
...  
Author(s):  
Leila Ladani ◽  
Jafar Razmi ◽  
Soud Farhan Choudhury

Anisotropic mechanical behavior is an inherent characteristic of parts produced using additive manufacturing (AM) techniques in which parts are built layer by layer. It is expected that in-plane and out-of-plane properties be different in these parts. E-beam fabrication is not an exception to this. It is, however, desirable to keep this degree of anisotropy to a minimum level and be able to produce parts with comparable mechanical strength in both in-plane and out-of-plane directions. In this manuscript, this degree of anisotropy is investigated for Ti6Al4V parts produced using this technique through tensile testing of parts built in different orientations. Mechanical characteristics such as Young's modulus, yield strength (YS), ultimate tensile strength (UTS), and ductility are evaluated. The strain rate effect on mechanical behavior, namely, strength and ductility, is also investigated by testing the material at a range of strain rates from 10−2 to 10−4 s−1. Local mechanical properties were extracted using nanoindentation technique and compared against global values (average values obtained by tensile tests). Although the properties obtained in this experiment were comparable with literature findings, test results showed that in-plane properties, elastic modulus, YS, and UTS are significantly higher than out-of-plane properties. This could be an indication of defects in between layers or imperfect bonding of the layers. Strong positive strain rate sensitivity was observed in out-of-plane direction. The strain rate sensitivity evaluation did not show strain rate dependency for in-plane directions. Local mechanical properties obtained through nanoindentation confirmed the findings of tensile test and also showed variation of properties caused by geometry.


Author(s):  
Xia Hua ◽  
Alan Thomas

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.


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

2021 ◽  
Vol 12 (3) ◽  
pp. 102
Author(s):  
Jaouad Khalfi ◽  
Najib Boumaaz ◽  
Abdallah Soulmani ◽  
El Mehdi Laadissi

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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