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