Lithium-ion battery equivalent thermal conductivity testing method based on Bayesian optimization algorithm

The thermal conductivity is one of the key thermal property's parameters in the design, modeling, and simulation of lithium-ion battery thermal management systems. Accurate measurement of thermal conductivity allows for a deep understanding of the heat transfer behavior inside lithium-ion batte...

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Vydané v:Journal of thermal analysis and calorimetry Ročník 149; číslo 24; s. 15073 - 15083
Hlavní autori: Wang, Fang, Liu, Ruihao, Ma, Xiaole, Zhang, Yuxuan, Bai, Guangli, Ma, Biao, Li, Danhua, Wei, Zhen, Liu, Shiqiang, Zhu, Yueying
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Cham Springer International Publishing 01.12.2024
Springer
Springer Nature B.V
Predmet:
Accuracy
Algorithms
Analytical Chemistry
Batteries
Bayesian analysis
Boundary conditions
Chemistry
Chemistry and Materials Science
Conduction heating
Conduction model
Conductive heat transfer
Conductivity
Design optimization
Deviation
Efficiency
Heat conductivity
Heat transfer
Inorganic Chemistry
Lithium
Lithium-ion batteries
Management systems
Measurement Science and Instrumentation
Methods
Optimization algorithms
Physical Chemistry
Polymer Sciences
Research methodology
Simulation
Temperature
Temperature measurement
Temperature measurements
Thermal conductivity
Thermal management
Thermal properties
Thermodynamic properties
Vehicles
Lithium-ion battery
Quasi-steady state
Bayesian optimization algorithm
Equivalent thermal conductivity
ISSN:1388-6150, 1588-2926
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Shrnutí:The thermal conductivity is one of the key thermal property's parameters in the design, modeling, and simulation of lithium-ion battery thermal management systems. Accurate measurement of thermal conductivity allows for a deep understanding of the heat transfer behavior inside lithium-ion batteries, providing essential insights for optimizing battery design, enhancing energy density, and improving safety. In this study, the surface temperature variation data of lithium-ion batteries were obtained by externally heating the batteries using a constant pressure source in an accelerating rate calorimeter enhanced system (ARC). Based on the Fourier one-dimensional heat conduction model, the average specific heat capacity and vertical thermal conductivity of the lithium-ion batteries were calculated. Additionally, the Bayesian optimization algorithm was employed to significantly reduce the number of iterations and rapidly invert the in-plane thermal conductivity of the batteries. The accuracy of the thermal conductivity measurement results was verified by comparing the consistency between experimental and simulation data. The results indicate that the transient deviation between experimental and simulation data at each temperature measurement point does not exceed 0.2 °C, demonstrating the high accuracy of the proposed method. Furthermore, the thermal conductivity of the lithium-ion battery was measured using the Hot Disk method for comparative validation. The results show that the maximum transient deviation of the Hot Disk data is 0.4 °C, indicating that compared to the Hot Disk method, the proposed method exhibits higher accuracy.
Bibliografia:ObjectType-Article-1
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content type line 14
ISSN:1388-6150
1588-2926
DOI:10.1007/s10973-024-13884-0