lithium ion cell
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2022 ◽  
Vol 521 ◽  
pp. 230957
Author(s):  
Yifei Yu ◽  
Elena Vergori ◽  
Faduma Maddar ◽  
Yue Guo ◽  
David Greenwood ◽  
...  

Batteries ◽  
2021 ◽  
Vol 7 (4) ◽  
pp. 88
Author(s):  
Natascia Andrenacci ◽  
Francesco Vellucci ◽  
Vincenzo Sglavo

The prediction of capacity degradation, and more generally of the behaviors related to battery aging, is useful in the design and use phases of a battery to help improve the efficiency and reliability of energy systems. In this paper, a stochastic model for the prediction of battery cell degradation is presented. The proposed model takes its cue from an approach based on Markov chains, although it is not comparable to a Markov process, as the transition probabilities vary with the number of cycles that the cell has performed. The proposed model can reproduce the abrupt decrease in the capacity that occurs near the end of life condition (80% of the nominal value of the capacity) for the cells analyzed. Furthermore, we illustrate the ability of this model to predict the capacity trend for a lithium-ion cell with nickel manganese cobalt (NMC) at the cathode and graphite at the anode, subjected to a life cycle in which there are different aging factors, using the results obtained for cells subjected to single aging factors.


2021 ◽  
Vol 9 ◽  
Author(s):  
Weisi Li ◽  
Vanessa León Quiroga ◽  
K. R. Crompton ◽  
Jason K. Ostanek

High temperature gases released through the safety vent of a lithium-ion cell during a thermal runaway event contain flammable components that, if ignited, can increase the risk of thermal runaway propagation to other cells in a multi-cell pack configuration. Computational fluid dynamics (CFD) simulations of flow through detailed geometric models of four vent-activated commercial 18650 lithium-ion cell caps were conducted using two turbulence modeling approaches: Reynolds-averaged Navier-Stokes (RANS) and scale-resolving simulations (SRS). The RANS method was compared with independent experiments of discharge coefficient through the cap across a range of pressure ratios and then used to investigate the ensemble-averaged flow field for the four caps. At high pressure ratios, choked flow occurs either at the current collector plate when flow through the current collector plate is more restrictive or the positive terminal vent holes when flow through the current collector plate is less restrictive. Turbulent mixing occurred within the vent cap assembly, in the jets emerging from the vent holes, and in recirculating zones directly above the vent cap assembly. The global maximum turbulent viscosity ratio (μT/μ) of the MTI, LG MJ1, K2, and LG M36 caps at pressure ratio of P1/P2 = 7 were 4,575, 3,360, 3,855, and 2,993, respectively. SRS and RANS simulations showed that both velocity magnitude and fluctuating velocity magnitude were lower for vent holes which are obstructed by the burst disk. SRS showed high levels of fluctuating velocity in the jets, up to 48.5% of the global maximum velocity. The present CFD models and the resulting insights provide the groundwork for future studies to investigate how jet structure and turbulence levels influence combustion and heat transfer in propagating thermal runaway scenarios.


2021 ◽  
pp. 103418
Author(s):  
Suzhen Liu ◽  
Jingjing Chen ◽  
Chuang Zhang ◽  
Liang Jin ◽  
Qingxin Yang

Author(s):  
Alana Aragon Zulke ◽  
Ivan Korotkin ◽  
Jamie M. Foster ◽  
Mangayarkarasi Nagarathinam ◽  
Harry Hoster ◽  
...  

Abstract We demonstrate the predictive power of a parametrised Doyle-Fuller-Newman (DFN) model of a commercial cylindrical (21700) lithium-ion cell with NCA/Gr-SiOx chemistry. Model parameters result from the deconstruction of a fresh commercial cell to determine/confirm chemistry and microstructure, and also from electrochemical experiments with half-cells built from electrode samples. The simulations predict voltage proles for (i) galvanostatic discharge and (ii) drive-cycles. Predicted voltage responses deviate from measured ones by <1% throughout at least 95% of a full galvanostatic discharge, whilst the drive cycle discharge is matched to a 1-3% error throughout. All simulations are performed using the online computational tool DandeLiion, which rapidly solves the DFN model using only modest computational resource. The DFN results are used to quantify the irreversible energy losses occurring in the cell and deduce their location. In addition to demonstrating the predictive power of a properly validated DFN model, this work provides a novel simplifed parametrisation work that can be used to accurately calibrate an electrochemical model of a cell.


Author(s):  
S. Vishnu ◽  
Krishna Pai ◽  
P. S. Praveena Krishna ◽  
N. S. Jayalakshmi ◽  
S D Suraj ◽  
...  

Author(s):  
Natascia Andrenacci ◽  
Francesco Vellucci ◽  
Vincenzo Sglavo

The prediction of capacity degradation, and more generally of the behaviors related to battery aging, is useful in the design and use phases of a battery to help improve the efficiency and reliability of energy systems. In this paper, a stochastic model for the prediction of battery cell degradation is presented. The proposed model takes its cue from an approach based on Markov chains, although it is not comparable to a Markov process, as the transition probabilities vary as the number of cycles that the cell has performed varies. The proposed model can reproduce the abrupt decrease in the capacity that occurs near the end of life condition (80% of the nominal value of the capacity) for the cells analyzed. Furthermore, we illustrate the ability of this model to predict the capacity trend for a lithium-ion cell with nickel-manganese-cobalt (NMC) at the cathode and graphite at the anode subjected to a life cycle in which there are different aging factors, using the results obtained for cells subjected to single aging factors.


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