2021
DOI: 10.1016/j.applthermaleng.2021.116961
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Numerical investigation of cooling performance of a novel air-cooled thermal management system for cylindrical Li-ion battery module

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Cited by 57 publications
(7 citation statements)
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“…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%
“…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%
“…At the time of writing this paper, a lot of research is still being published to address irregularities or high battery temperatures. Koorata et al 34 demonstrated an efficient mini‐channel battery management system for LiFePO 4 pouch cells. The mini channels are involved in a cooling plate.…”
Section: Introductionmentioning
confidence: 99%
“…[11] Kausthubharam et al observed that relocating the air inlet and outlet from the central position enhanced temperature uniformity, while air velocity increasing resulted in a decrease in maximum temperature. [12] In comparison to air-cooling systems, liquid-cooling systems exhibit enhanced capacity; however, they may also present potential leakage issues. [13] Sheng et al [14] investigated the thermal management performance (TMP) of BTM using a serpentine-channel liquid-cooling plate heat exchanger.…”
Section: Introductionmentioning
confidence: 99%
“…[ 11 ] Kausthubharam et al observed that relocating the air inlet and outlet from the central position enhanced temperature uniformity, while air velocity increasing resulted in a decrease in maximum temperature. [ 12 ]…”
Section: Introductionmentioning
confidence: 99%