Influence of cycling aging and ambient pressure on the thermal safety features of lithium-ion battery

Xie, Song; Ren, Lixiang; Yang, Xing-guo; Wang, Haibin; Sun, Qiang; Chen, Xiantao; He, Yuanhua

doi:10.1016/j.jpowsour.2019.227425

Cited by 78 publications

(30 citation statements)

References 34 publications

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“…We have found no literature how in N 2 atmosphere different initial pressures affect thermal behaviour, either at constant volume or constant pressure. There is literature on the effects of different initial pressures in constant pressure air environment affecting heat released during TR, with higher heat release at higher pressure (Xie et al 2020;Chen et al 2019). However we believe this can not be extrapolated to the case of N 2 atmosphere, as we see the cause of the higher heat release at higher pressure in the higher availability of oxygen.…”

Section: Discussionmentioning

confidence: 99%

A Modelica library for Thermal-Runaway Propagation in Lithium-Ion Batteries

Gross¹,

Golubkov²

2021

Linköping Electronic Conference Proceedings

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Based on the Thermal Runaway (TR) experiments conducted in our laboratory a simple method of describing a battery's thermal behaviour was developed. In the approach -which we call simple tracing methodthe temperature rate measurement from Accelerating Rate Calorimetry (ARC) during a TR experiment is approximated to determine the thermal behaviour of the model. This method was implemented in Modelica using Dymola. Alongside the implementation of the TR model a complete Modelica package with useful models for TR propagation simulation was developed. The package called "BatterySafety" serves as a foundation for further model development to investigate TR propagation and physical counter measures in greater detail. The focus of all the models in the package was efficiency, intelligibility and user-friendliness. With this approach we are able to simulate TR propagation of a complete battery pack.

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Section: Discussionmentioning

confidence: 99%

A Modelica library for Thermal-Runaway Propagation in Lithium-Ion Batteries

Gross¹,

Golubkov²

2021

Linköping Electronic Conference Proceedings

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“…Lithium-ion batteries (LIBs) are the core elements of Battery Energy Storage Systems (BESS) because they are characterized by high specific energy, high efficiency, low maintenance, and long life ( Kim et al., 2019 ). The performance of batteries gradually deteriorates with the increase of service time, which might not only affect the normal operation of electrical equipment but also bring about serious accidents ( Saha et al., 2009 ; Xie et al., 2020 ). The state of health (SOH) is a measure of the battery life and plays a vital role in improving the performance of BESS and realizing timely maintenance of equipment.…”

Section: Introductionmentioning

confidence: 99%

Improved sparrow search algorithm optimization deep extreme learning machine for lithium-ion battery state-of-health prediction

Jia¹,

Yuan²,

Shi³

et al. 2022

iScience

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“…[ 12 ] In the study by Ouyang et al, [ 13 ] the high charging rate 2 C was determined to be the optimum cycle rate. In the study by Xie et al, [ 14 ] the thermal safety properties of lithium‐ion batteries were studied under the influence of ambient pressure and cyclic aging. Furthermore, in another study, [ 15 ] the multistep constant current (CC) charging protocol was used for battery pack aging to indicate the state‐of‐health and remaining useful life.…”

Section: Introductionmentioning

confidence: 99%

Investigation of Simultaneous Influences of Significant Charging Factors on Lithium‐Ion Batteries and Identifying Interaction Effects

Moralı

2021

Energy Tech

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Lithium‐ion batteries are an essential technology with extensive use in numerous electric applications. A complete understanding of the simultaneous effects of charging factors such as state‐of‐charge (SOC), C‐rate, and rest period is essential to manage lithium‐ion batteries. This study aims to determine the significant charging factors influencing the battery dynamics to provide detailed insights into the relationship between the selected charging factors and the battery dynamics. The cathodic charge transfer resistance and the ohmic resistance are substantially affected by the C‐rate, whereas the SOC dominantly contributes to only the Rt;c. The effect of the SOC on the ohmic resistance and the cathodic charge transfer resistance depends on the C‐rate. The cathode charge transfer resistance is the most sensitive dynamic parameter to charging factors. The solid electrolyte interphase formation charge transfer resistance is the most stable battery dynamic parameter. The sensitivity analysis expressly shows the part of the lithium‐ion battery that needs to be improved to provide better performance under the implemented charging protocols. Comprehensive analysis of the simultaneous effects of charging factors on battery dynamics can also be used in a battery management system to measure changes in battery dynamics that will support lithium‐ion battery controls.

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Influence of cycling aging and ambient pressure on the thermal safety features of lithium-ion battery

Cited by 78 publications

References 34 publications

A Modelica library for Thermal-Runaway Propagation in Lithium-Ion Batteries

A Modelica library for Thermal-Runaway Propagation in Lithium-Ion Batteries

Improved sparrow search algorithm optimization deep extreme learning machine for lithium-ion battery state-of-health prediction

Investigation of Simultaneous Influences of Significant Charging Factors on Lithium‐Ion Batteries and Identifying Interaction Effects

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