Improving the cooling performance of cylindrical lithium-ion battery using three passive methods in a battery thermal management system

Khaboshan, Hasan Najafi; Jaliliantabar, Farzad; Abdullah, Abdul Adam; Panchal, Satyam

doi:10.1016/j.applthermaleng.2023.120320

Cited by 77 publications

(18 citation statements)

References 54 publications

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“…Liu et al 16 considered energy consumption, emissions, and battery life and used this to propose a novel hybrid point-line energy management strategy. Khaboshan 17 et al designed a BTMS for 18 650 (Panasonic NCR18650PF, 2.4 A h) by combining phase change material (PCM), foam metal, fins, etc. It can reduce the cell surface temperature by 3 K under 3C current discharge conditions.…”

Section: Introductionmentioning

confidence: 99%

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Experimental and simulation study of REEV charging and discharging strategy considering battery temperature

Zhang

et al. 2023

Sustainable Energy Fuels

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Section: Introductionmentioning

confidence: 99%

“…Liu et al 16 considered energy consumption, emissions, and battery life and used this to propose a novel hybrid point-line energy management strategy. Khaboshan 17 et al…”

Section: Introductionmentioning

confidence: 99%

Experimental and simulation study of REEV charging and discharging strategy considering battery temperature

Zhang

et al. 2023

Sustainable Energy Fuels

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“…The rapid advancement of next-generation technology , has led to the development of highly integrated devices such as printed circuit boards (PCBs), micro light-emitting diodes (μLEDs), and automobiles, which are lightweight, miniaturized, and multifunctional. However, the efficient management of heat in these devices has emerged as a critical challenge, particularly in the context of electric vehicles, where severe thermal accumulation poses a significant industry-wide obstacle. , To address the issue of heat dissipation in electronic devices, thermal interface materials (TIM), such as polymer composites, have been developed. Despite the advantages of polymers, including their ease of processing, low cost, and lightweight nature, their inherent thermal conductivity is relatively low, typically less than 0.5 W m –1 K –1 .…”

Section: Introductionmentioning

confidence: 99%

Thermally Conductive Epoxy Composites Using Porous Boron Nitride Network and Polydopamine-Treated Aluminum Oxide

Lee

Yang

Kim

2023

ACS Appl. Polym. Mater.

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A high-performance thermally conductive composite was fabricated by using epoxy (EP) and hybrid fillers consisting of polydopamine (PDA)-treated alumina (AO) and a porous boron nitride (BN) network with carbonized cellulose nanofibers (BNCNF). The continuous heat transfer paths were formed by freeze-cast porous BN. Moreover, the improved interfacial interaction between filler and matrix was achieved by hydrogen bonding derived from amin group on PDA-AO and hydroxyl group on BNCNF. This composite exhibited a high thermal conductivity (4.3 W m–1 K–1, 2050% enhancement compared to pure EP) along the through-plane direction within the matrix and improved tensile stress (80.86%). Based on these findings, it can be concluded that the EP/BNCNF/PDA-AO composites have the potential to significantly improve thermal management in electronic devices. The fabricated epoxy composites also exhibited promising characteristics for the development of thermal management systems.

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“…Model-based prediction methods need a better understanding of the battery degradation system so they can handle non-linear and complex systems and can be more adapted to new situations or environments. Non-linear equation and complex modeling can be found in many studies of battery thermal management system [3][4][5]. In V.G.…”

Section: Introductionmentioning

confidence: 99%

Remaining useful life prediction of lithium-ion batteries based on the Support Vector Regression Optimized and Enhanced Whale Optimization Algorithm

Wu,

Chen,

et al. 2023

J. Phys.: Conf. Ser.

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Accurate and reliable predictions of the remaining useful life (RUL) of lithium-ion batteries are crucial for ensuring the proper functioning and longevity of lithium-ion battery systems. Predicting the RUL in its early stages can be challenging and difficult for conventional RUL prediction techniques. To tackle this challenge, this paper proposes a novel approach that integrates five carefully chosen health features with a Support Vector Regression Optimized model and Enhanced Whale Optimization Algorithm (EWOA-SVR) to predict the future capacity trend and subsequently estimate the RUL using XGBoost method according to the predicted capacity. The proposed approach aims to achieve early and accurate capacity estimation and RUL prediction for lithium-ion batteries. To begin, this method extracts some health features during the relaxation process and during the constant-current and constant-voltage charge (CC-CV) process. To demonstrate the strength of the correlation between the health features and capacity, both Pearson and Spearman rank correlation analysis are employed in this study. Then based on the measurable health features and faded capacity data, Ant Lion Optimization (ALO), Whale Optimization Algorithm (WOA) and EWOA are used to select suitable penalty factor and kernel parameter in the SVR algorithm to improve the performance of SVR. With the well-trained model, the future capacity can be predicted with an accuracy of MAPE within 1%, and the RUL prediction error is lower than 11 cycles. The results suggest that the proposed model can achieve a precise and dependable early estimation of the future capacity and RUL prediction.

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Improving the cooling performance of cylindrical lithium-ion battery using three passive methods in a battery thermal management system

Cited by 77 publications

References 54 publications

Experimental and simulation study of REEV charging and discharging strategy considering battery temperature

Experimental and simulation study of REEV charging and discharging strategy considering battery temperature

Thermally Conductive Epoxy Composites Using Porous Boron Nitride Network and Polydopamine-Treated Aluminum Oxide

Remaining useful life prediction of lithium-ion batteries based on the Support Vector Regression Optimized and Enhanced Whale Optimization Algorithm

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