Next Article in Journal
Optimizing Methane Production from Lignocellulosic Biomass: Low-Temperature Potassium Ferrate Pretreatment via Response Surface Methodology
Previous Article in Journal
Fourier Transform-Based Inversion of Surface Deformation for Risk Assessment of Casing Damage: A Case Study of the Daqing Oilfield
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Processes
Volume 13
Issue 9
10.3390/pr13092769
processes-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Review

Data-Driven Prediction of Li-Ion Battery Thermal Behavior: Advances and Applications in Thermal Management

by
Weijia Qian
1,
Wenda Fang
2,
Yongjun Tian
3,4,*,
Guangwu Dai
3,4,
Tao Yan
3,4,
Siheng Yang
5 and
Ping Wang
1
1
Institute for Energy Research, Jiangsu University, Zhenjiang 212013, China
2
School of Energy and Power Engineering, Jiangsu University, Zhenjiang 212013, China
3
TCATARC Automotive Test Center (Changzhou) Company Limited, Changzhou 213000, China
4
Jiangsu Provincial Engineering Technology Research Center for Integrated Photovoltaic, Energy Storage, Charging, and Testing, Changzhou 213000, China
5
School of Energy Engineering, Zhejiang University, Hangzhou 310013, China
*
Author to whom correspondence should be addressed.
Processes 2025, 13(9), 2769; https://doi.org/10.3390/pr13092769
Submission received: 12 August 2025 / Revised: 25 August 2025 / Accepted: 28 August 2025 / Published: 29 August 2025
(This article belongs to the Section Energy Systems)
Browse Figures
Versions Notes

Abstract

Lithium-ion batteries (LIBs) are critical for various applications, and effective thermal management is important for their safety, performance, and lifespan. Traditional physics-based modeling of battery thermal behavior is computationally complex and requires detailed parameters. Using data-driven modeling to predict thermal characteristics of batteries offers a promising alternative. This review comprehensively examines the utilization of data-driven methods in predicting LIB thermal behavior and designing battery thermal management systems. It explores commonly used data-driven techniques and focuses on their applications in predicting heat generation, temperature distribution, and cooling performance. Specific data-driven models for battery thermal prediction are presented, with a comparative analysis of their strengths and weaknesses. The review concludes that data-driven models can effectively predict battery thermal behavior, offering computational efficiency compared to physics-based simulations. Future research directions include hybrid data-driven/physical modeling, ensemble modeling, and incorporating explainable artificial intelligence techniques to enhance model interpretability. These advancements will lead to more accurate and interpretable models, contributing to the safe and efficient applications of LIB systems.
Keywords: lithium-ion battery; data-driven modeling; machine learning; heat-generation rate; temperature prediction; battery thermal management system lithium-ion battery; data-driven modeling; machine learning; heat-generation rate; temperature prediction; battery thermal management system

Share and Cite

MDPI and ACS Style

Qian, W.; Fang, W.; Tian, Y.; Dai, G.; Yan, T.; Yang, S.; Wang, P. Data-Driven Prediction of Li-Ion Battery Thermal Behavior: Advances and Applications in Thermal Management. Processes 2025, 13, 2769. https://doi.org/10.3390/pr13092769

AMA Style

Qian W, Fang W, Tian Y, Dai G, Yan T, Yang S, Wang P. Data-Driven Prediction of Li-Ion Battery Thermal Behavior: Advances and Applications in Thermal Management. Processes. 2025; 13(9):2769. https://doi.org/10.3390/pr13092769

Chicago/Turabian Style

Qian, Weijia, Wenda Fang, Yongjun Tian, Guangwu Dai, Tao Yan, Siheng Yang, and Ping Wang. 2025. "Data-Driven Prediction of Li-Ion Battery Thermal Behavior: Advances and Applications in Thermal Management" Processes 13, no. 9: 2769. https://doi.org/10.3390/pr13092769

APA Style

Qian, W., Fang, W., Tian, Y., Dai, G., Yan, T., Yang, S., & Wang, P. (2025). Data-Driven Prediction of Li-Ion Battery Thermal Behavior: Advances and Applications in Thermal Management. Processes, 13(9), 2769. https://doi.org/10.3390/pr13092769

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop