TY - JOUR
T1 - Research on the output characteristics and SOC estimation method of lithium-ion batteries over a wide range of operating temperature conditions
AU - Shu, Xiong
AU - Li, Yongjing
AU - Wei, Kexiang
AU - Yang, Wenxian
AU - Yang, Bowen
AU - Zhang, Ming
N1 - Publisher Copyright:
© 2025 The Authors
PY - 2025/2/15
Y1 - 2025/2/15
N2 - With the rapid growth of the EV market, the use of lithium-ion batteries (LIBs) has increased significantly. However, the safety of these battery systems remains a concern. Accurate estimation of the state of charge (SOC) is crucial to enhance battery safety and longevity. In this paper, the impact of temperature on LIB performance is investigated and it is found that temperature variations can lead to inaccurate SOC estimation. To address this issue, LIB performance and capacity degradation at different operating temperatures are experimentally studied, and Electrochemical impedance spectroscopy (EIS) characteristics are analyzed. Based on the analysis results, an SOC estimation method, combining recursive least squares with forgetting factor (FFRLS) and adaptive extended Kalman filtering (AEKF) with temperature compensation, is proposed in the study. This method is tested respectively at 0 °C, 25 °C and 45 °C, demonstrating the feasibility and higher prediction accuracy of the proposed method across a wide temperature range.
AB - With the rapid growth of the EV market, the use of lithium-ion batteries (LIBs) has increased significantly. However, the safety of these battery systems remains a concern. Accurate estimation of the state of charge (SOC) is crucial to enhance battery safety and longevity. In this paper, the impact of temperature on LIB performance is investigated and it is found that temperature variations can lead to inaccurate SOC estimation. To address this issue, LIB performance and capacity degradation at different operating temperatures are experimentally studied, and Electrochemical impedance spectroscopy (EIS) characteristics are analyzed. Based on the analysis results, an SOC estimation method, combining recursive least squares with forgetting factor (FFRLS) and adaptive extended Kalman filtering (AEKF) with temperature compensation, is proposed in the study. This method is tested respectively at 0 °C, 25 °C and 45 °C, demonstrating the feasibility and higher prediction accuracy of the proposed method across a wide temperature range.
KW - Lithium-ion battery
KW - Electric vehicle
KW - SOC
KW - FFRLS-AEKF
UR - http://www.scopus.com/inward/record.url?scp=85216103122&partnerID=8YFLogxK
U2 - 10.1016/j.energy.2025.134726
DO - 10.1016/j.energy.2025.134726
M3 - Article
VL - 317
JO - Energy
JF - Energy
SN - 0360-5442
M1 - 134726
ER -