Online Estimation of LithiumIon
Battery State of Charge Considering Temperature and Aging State

doi:10.3969/j.issn.1001-2222.2025.05.010

Vehicle Engine ›› 2025, Vol. 0 ›› Issue (5) : 69. DOI: 10.3969/j.issn.1001-2222.2025.05.010

Online Estimation of LithiumIon Battery State of Charge Considering Temperature and Aging State

MO Die¹，XIAO Renxin¹，XIE Weifeng²

Author information +

History +

Abstract

With the widespread use of lithium-ion batteries in electric vehicles, accurately estimating the state of charge (SOC) has become a key factor in ensuring safe and stable operation of battery management system (BMS) and optimizing performance. To address the challenge of accurately estimating the internal state of power batteries under wide temperature ranges and throughout the entire service life, an SOC estimation method for lithium-ion batteries was proposed based on adaptive least squares (AFFRLS) and multi-innovation unscented kalman filter (MIUKF). The method can realize accurate estimation under different temperatures and aging states. By using a second-order RC equivalent circuit model, the method achieved real-time monitoring of the power battery＇s internal state and online identification of model parameters through AFFRLS, with comparisons made to traditional Least Squares (RLS), extended Kalman filter (EKF), and forgetting factor recursive least squares (FFRLS). To improve estimation accuracy, the multi-innovation unscented kalman filter (MIUKF) algorithm was introduced to address the low historical data utilization issue in traditional unscented Kalman filter (UKF). The effectiveness of this method was verified through SOC estimation experiments under various temperature and aging conditions. Additionally, robustness tests were conducted under typical battery testing conditions such as UDDS, LA92 and HWFEF. The experimental results show that AFFRLS-MIUKF method effectively enhances the utilization of historical data and accurately reflects the internal state of power batteries under different temperatures and aging conditions, with SOC estimation errors controlled within 2% range, demonstrating good robustness.

Key words

lithium-ion battery / state of charge / adaptive least square method / multi-innovation unscented Kalman filter / prediction model

Cite this article

EndNote

Ris (Procite)

Bibtex

Download Citations

MO Die, XIAO Renxin, XIE Weifeng. Online Estimation of LithiumIon Battery State of Charge Considering Temperature and Aging State[J]. Vehicle Engine. 2025, 0(5): 69 https://doi.org/10.3969/j.issn.1001-2222.2025.05.010