Recent progress in lithium-ion battery thermal management for a wide range of temperature and abuse conditions

Jiang, Zhao; Li, H.B.; Qu, Zhiguo; Zhang, J.F.

doi:10.1016/j.ijhydene.2022.01.008

Cited by 105 publications

(35 citation statements)

References 186 publications

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“…Battery thermal management technology can be divided into air cooling [13][14][15][16][17][18][19][20], liquid cooling [21][22][23][24][25][26][27][28] and phase change material (PCM) cooling [29][30][31][32][33][34][35] according to different cooling media. Air cooling is one of the most common and widely used cooling methods.…”

Section: Introductionmentioning

confidence: 99%

A Novel Method for Heat Exchange Evaluation in EV

Saraireh¹

2023

Intelligent Automation &Amp; Soft Computing

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With the growing global energy and environmental problems, electric vehicles that are both environmentally friendly and cost effective have seen rapid growth. An electrified vehicle's effective thermal management must include all of the vehicle's systems. However, optimizing the thermal behavior of each component is insufficient. A lithium-ion battery's operating temperature has a significant impact on its performance. When working at low temperatures, the internal resistance of lithium-ion batteries increases, the available energy and power of the system decreases, and lithium precipitation caused by low-temperature charging may cause safety issues; high-temperature operation and temperature inconsistency between battery cells will cause accelerated ageing of the battery, which may result in safety issues such as thermal runaway. As a result, to keep the temperature of the battery module under control, electric cars require a strong thermal management system. Thermal management must be handled at the system level to maximize the vehicle's overall performance. The integrated thermal management system for electric vehicles has the potential to significantly increase vehicle energy efficiency. However, it's construction is complicated due to the thermal requirements of the battery and cabin. In this research, a multi-channel liquid cooling system with a serpentine wavy structure was used to conduct an experimental thermal investigation of a lithium-ion battery pack. The suggested cooling system's capabilities was examined under various charge/discharge scenarios using varied coolant flow rates and pumping power. The findings revealed that the suggested cooling system was successful across a variety of charging and discharging settings, with most of the tested scenarios achieving a maximum temperature difference.

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Section: Introductionmentioning

confidence: 99%

A Novel Method for Heat Exchange Evaluation in EV

Saraireh¹

2023

Intelligent Automation &Amp; Soft Computing

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“…There are two key topics of concern in battery thermal management: handling the charge/discharge cycle and governing the battery heat growth [13,14]. Many pieces of research focused on the battery thermal management system of EVs have been conducted [15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Integration of Computational Fluid Dynamics and Artificial Neural Network for Optimization Design of Battery Thermal Management System

Yuen

Chen

et al. 2022

Batteries

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The increasing popularity of lithium-ion battery systems, particularly in electric vehicles and energy storage systems, has gained broad research interest regarding performance optimization, thermal stability, and fire safety. To enhance the battery thermal management system, a comprehensive investigation of the thermal behaviour and heat exchange process of battery systems is paramount. In this paper, a three-dimensional electro-thermal model coupled with fluid dynamics module was developed to comprehensively analyze the temperature distribution of battery packs and the heat carried away. The computational fluid dynamics (CFD) simulation results of the lumped battery model were validated and verified by considering natural ventilation speed and ambient temperature. In the artificial neural networks (ANN) model, the multilayer perceptron was applied to train the numerical outputs and optimal design of the battery setup, achieving a 1.9% decrease in maximum temperature and a 4.5% drop in temperature difference. The simulation results provide a practical compromise in optimizing the battery configuration and cooling efficiency, balancing the layout of the battery system, and safety performance. The present modelling framework demonstrates an innovative approach to utilizing high-fidelity electro-thermal/CFD numerical inputs for ANN optimization, potentially enhancing the state-of-art thermal management and reducing the risks of thermal runaway and fire outbreaks.

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“…2,3 However, a critical challenge that hinders the further promotion of LIBs is its poor fast charging capability, lowtemperature charging capability, and safety under overcharge in real scenarios. 4 These issues mainly stem from lithium plating on the anode of LIBs. 5 When lithium plating occurs during charging, partial Li-ions are deposited as metallic Li on the surface of anodes instead of intercalated into the anodes, 6 which could result in irreversible loss of active lithium, 7 severe capacity attenuation, 8 internal short circuit due to separator penetration, 9 and thermal runaway.…”

Section: Introductionmentioning

confidence: 99%

“…Lithium‐ion batteries (LIBs) have experienced an unprecedented deployment in the field of electric vehicles (EVs), battery energy storage systems (BESS), and portable electronics, 1 to their high energy efficiency and high energy density 2,3 . However, a critical challenge that hinders the further promotion of LIBs is its poor fast charging capability, low‐temperature charging capability, and safety under overcharge in real scenarios 4 . These issues mainly stem from lithium plating on the anode of LIBs 5 .…”

Section: Introductionmentioning

confidence: 99%

Deep neural network‐driven in‐situ detection and quantification of lithium plating on anodes in commercial lithium‐ion batteries

Tian

Lin

et al. 2022

EcoMat

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Lithium plating seriously threatens the life of lithium-ion batteries at low temperatures charging conditions, but the onboard detection and quantification of lithium plating are severely hampered by the limited available signals and volatile operating conditions in real scenarios. Herein, we propose a detection method to predict the occurrence and quantification of lithium plating under uncertain conditions by only using constant-current curves during charging based on deep learning. A deep neural network (DNN) is developed to extract data-driven features induced by lithium plating from the charge curves, avoiding the challenge of manual feature selection. Only using the most common voltage and current signals as inputs, the network exhibits superior adaptability and accuracy. The detection accuracy of the proposed method is 98.64%, while the quantity of the lithium plating can be accurately predicted with a root-mean-square error <4.1712 mg. Moreover, the generalization ability of the proposed method is verified by its reliable detection accuracy under conditions that are not used in the training dataset. The detection accuracy is 92.39% for brand new charging conditions and 95.53% for brand new aging states. This method shortens the detection time that currently takes more than several hours (the widely used differential curve analysis) to milliseconds and eliminates the need for a rigorous testing environment, showing great potential for onboard application in future battery management systems.

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Recent progress in lithium-ion battery thermal management for a wide range of temperature and abuse conditions

Cited by 105 publications

References 186 publications

A Novel Method for Heat Exchange Evaluation in EV

A Novel Method for Heat Exchange Evaluation in EV

Integration of Computational Fluid Dynamics and Artificial Neural Network for Optimization Design of Battery Thermal Management System

Deep neural network‐driven in‐situ detection and quantification of lithium plating on anodes in commercial lithium‐ion batteries

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