Accurately estimating internal temperature of lithium-ion batteries based on the distribution of relaxation time and data-driven

Xiyu Chen, Qingbo Li, Bohan Shao, Weilin Dou, Chunyan Lai, Taolin Lu and Jingying Xie

Energy, 2025, vol. 320, issue C

Abstract: Accurately estimating the internal temperature of lithium-ion batteries (LIBs) represents a crucial means of minimizing battery thermal accidents. An effective way to estimate the battery's internal temperature is electrochemical impedance spectroscopy (EIS). Distribution of relaxation times (DRT) methodologies can achieve a rapid decomposition of EIS, but DRT is primarily used for battery analysis. In this work, a method for estimating the internal temperature of LIBs based on the combination of DRT and machine learning was proposed, and more than 1200 EIS spectra of commercial LIBs were collected at different states of charge and internal temperatures, which is very scarce in the field of battery internal temperature. DRT calculations were performed on the collected data and the features strongly correlated to internal temperature were confirmed. The mapping of input features to the internal temperature is established with six machine-learning algorithms. The results indicate that although the battery status is unknown, the models can accurately estimate the internal temperature, and the mean absolute error (MAE) is below 0.319 °C. A comparison to the same machine learning algorithms based on EIS shows that using DRT as input can reduce model outliers and improve model stability.

Keywords: Lithium-ion batteries; Internal temperature estimation; Electrochemical impedance spectroscopy; Distribution of relaxation time; Machine learning (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544225011351
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:320:y:2025:i:c:s0360544225011351

DOI: 10.1016/j.energy.2025.135493

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().