Anisotropic Thermal Conductivity of Lithium‐Ion Batteries at the Full‐Cell Level: A Review of Methodology Advances, Data Analytics, and Future Applications

L Liang Wang J Jia Liu G Guang‐Bo Liu (State Key Laboratory of Clean Energy Utilization Zhejiang University Hangzhou People's Republic of China) Y Yu‐Qi Huang (Institute of Power Machinery and Vehicular Engineering School of Energy Engineering Zhejiang University Hangzhou Zhejiang 310027 P. R. China) X Xiao‐Li Yu (Key Laboratory of Eco‐Chemical Engineering, Ministry of Education, International Science and Technology Cooperation Base of Eco‐Chemical Engineering and Green Manufacturing College of Chemistry and Molecular Engineering Qingdao University of Science and Technology Qingdao P. R. China) L Li‐Wu Fan (State Key Laboratory of Clean Energy Utilization Zhejiang University Hangzhou People's Republic of China)

Abstract

Abstract The knowledge of anisotropic thermal conductivity of lithium‐ion batteries (LIBs) is paramount for accurate battery thermal modeling and diagnosis because of its decisive influence on the internal temperature distribution and overall thermal behaviors of LIBs. However, due to the complex materials, geometries, and dynamic state variations of LIBs, obtaining accurate anisotropic thermal conductivity at the full‐cell level remains challenging. Here, a comprehensive review is provided on the latest methodologies and data analytics of the anisotropic thermal conductivity of battery cells with different chemistries and geometries under dynamic operating conditions. The applicability, uncertainty, advantages, and limitations of current measurement methods are examined, providing guidance for method selection and future improvement. The anisotropic thermal conductivity data across various cell formats and electrode materials are evaluated, and their variations with temperature, state of charge, state of health, and charge/discharge rates  are analyzed. Additionally, future applications are highlighted for thermal conductivity to serve as a key input for thermal modeling and an indicator for battery diagnostics, with the development of in situ and real‐time measurement methods and artificial‐intelligence‐driven models. These efforts collectively support an integrated framework for next‐generation thermal management systems toward improving the performance, safety, and lifespan of LIBs and other emerging batteries.

Article Details

Volume / Issue Vol. 38, Issue 5
Published January 01, 2026
ISSN 0935-9648
Publisher Unknown Publisher

Journal Info

Advanced Materials

Unknown Publisher

ISSN: 0935-9648 Physical Sciences

Authors (6)

L

Liang Wang

J

Jia Liu

G

Guang‐Bo Liu

State Key Laboratory of Clean Energy Utilization Zhejiang University Hangzhou People's Republic of China

Y

Yu‐Qi Huang

Institute of Power Machinery and Vehicular Engineering School of Energy Engineering Zhejiang University Hangzhou Zhejiang 310027 P. R. China

X

Xiao‐Li Yu

Key Laboratory of Eco‐Chemical Engineering, Ministry of Education, International Science and Technology Cooperation Base of Eco‐Chemical Engineering and Green Manufacturing College of Chemistry and Molecular Engineering Qingdao University of Science and Technology Qingdao P. R. China

L

Li‐Wu Fan

State Key Laboratory of Clean Energy Utilization Zhejiang University Hangzhou People's Republic of China