2022
DOI: 10.3390/batteries8110216
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Thermal Propagation Modelling of Abnormal Heat Generation in Various Battery Cell Locations

Abstract: With the increasing demand for energy capacity and power density in battery systems, the thermal safety of lithium-ion batteries has become a major challenge for the upcoming decade. The heat transfer during the battery thermal runaway provides insight into thermal propagation. A better understanding of the heat exchange process improves a safer design and enhances battery thermal management performance. This work proposes a three-dimensional thermal model for the battery pack simulation by applying an in-hous… Show more

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Cited by 8 publications
(7 citation statements)
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“…We make the assumption that the fluid is incompressible and that the cooling plate is isotropic and homogeneous. When this assumption is satisfied, the flow process of the fluid satisfies the equations of conservation of energy, conservation of momentum, and conservation of mass [37][38][39]. The battery energy conservation equation is expressed as follows:…”
Section: Cfd Methodsmentioning
confidence: 99%
“…We make the assumption that the fluid is incompressible and that the cooling plate is isotropic and homogeneous. When this assumption is satisfied, the flow process of the fluid satisfies the equations of conservation of energy, conservation of momentum, and conservation of mass [37][38][39]. The battery energy conservation equation is expressed as follows:…”
Section: Cfd Methodsmentioning
confidence: 99%
“…Prior to design and modeling, the parameters of the thermal management system must be determined or optimized. These parameters optimization include air velocity in the forced air cooling, the ambient temperature in the forced air cooling, the flow rate of the liquid, and cooling liquid temperature. Some studies have proposed optimization using the ML method, which is considered to be an excellent tool for optimizing and predicting parameters. Researchers attempted to implement ML models, such as artificial neural networks (ANNs), convolutional neural networks (CNNs), long short-term memory (LSTM), deep reinforcement learning (DRL), etc., to assist the BTM system for enhanced battery thermal safety and resilience. For example, Jaliliantabar et al developed an ANN model for the prediction of LIB temperature equipped with BTMs and proved the capability of ANN to predict battery temperature in various operating conditions of BTMs.…”
Section: Development Of Btmsmentioning
confidence: 99%
“…TR can nearly destroy the cell and contaminate the others by transferring the heat with direct contact or connectors [32,33]. It is essential to use mechanisms to identify the abuse previously, find the location and isolate the problematic cell to guarantee the safety of the packing and the vehicle.…”
Section: Thermal Failuresmentioning
confidence: 99%
“…The temperature signature indicates the OD present in cells. Despite the temperature increasing naturally during the discharging (especially when the resistance is higher when the cell has a voltage lower than 3 V [32]), the temperature increased more under OD abuse. In addition to that, current sensor C0 was close to the reference and could not indicate the presence of abuse.…”
Section: Analyzes Of Odmentioning
confidence: 99%