Analysis of State-of-Charge Estimation Methods for Li-Ion Batteries Considering Wide Temperature Range

Yu Miao, Yang Gao (), Xinyue Liu, Yuan Liang and Lin Liu ()
Additional contact information
Yu Miao: School of Electromechanic Engineering, North Minzu University, Yinchuan 750030, China
Yang Gao: Ningxia Engineering Research Center for Hybrid Manufacturing System, Yinchuan 750021, China
Xinyue Liu: School of Electromechanic Engineering, North Minzu University, Yinchuan 750030, China
Yuan Liang: Ningxia Engineering Research Center for Hybrid Manufacturing System, Yinchuan 750021, China
Lin Liu: Department of Mechanical Engineering, University of Kansas, 3138 Learned Hall, 1530 W. 15th Street, Lawrence, KS 66045-4709, USA

Energies, 2025, vol. 18, issue 5, 1-27

Abstract: Lithium-ion batteries are the core energy storage technology for electric vehicles and energy storage systems. Accurate state-of-charge (SOC) estimation is critical for optimizing battery performance, ensuring safety, and predicting battery lifetime. However, SOC estimation faces significant challenges under extreme temperatures and complex operating conditions. This review systematically examines the research progress on SOC estimation techniques over a wide temperature range, focusing on two mainstream approaches: model improvement and data-driven methods. The model improvement method enhances temperature adaptability through temperature compensation and dynamic parameter adjustment. Still, it has limitations in dealing with the nonlinear behavior of batteries and accuracy and real-time performance at extreme temperatures. In contrast, the data-driven method effectively copes with temperature fluctuations and complex operating conditions by extracting nonlinear relationships from historical data. However, it requires high-quality data and substantial computational resources. Future research should focus on developing high-precision, temperature-adaptive models and lightweight real-time algorithms. Additionally, exploring the deep coupling of physical models and data-driven methods with multi-source heterogeneous data fusion technology can further improve the accuracy and robustness of SOC estimation. These advancements will promote the safe and efficient application of lithium batteries in electric vehicles and energy storage systems.

Keywords: lithium-ion battery; state of charge; estimation method; temperature (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/18/5/1188/pdf (application/pdf)
https://www.mdpi.com/1996-1073/18/5/1188/ (text/html)

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:gam:jeners:v:18:y:2025:i:5:p:1188-:d:1602301

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().