TY - JOUR
T1 - A syncretic state-of-charge estimator for LiFePO4 batteries leveraging expansion force
AU - Xu, Peipei
AU - Li, Junqiu
AU - Xue, Qiao
AU - Sun, Fengchun
N1 - Publisher Copyright:
© 2022
PY - 2022/6
Y1 - 2022/6
N2 - State of Charge (SoC) estimation for LiFePO4 (LFP) batteries is particularly challenging due to the flat open circuit voltage (OCV) characteristics. This paper proposes a novel method of SoC estimation for LFP batteries based on expansion force which has not been investigated in the existing literature. After a series of experiments, it is found that the expansion force is more sensitive to SoC than voltage and independent of the dynamic current. However, the estimation work is still technically challenging because of the non-monotonic relationship between expansion force and SoC. To cope with it, an expansion force model based on the least-square support vector machine (LSSVM) method is firstly exploited, which is employed as the measurement equation of adaptive unscented Kalman filters (AUKF). Meanwhile, the moving window method is applied to enhance the adaptability of the established model and then an accurate SoC estimation result is attained. Finally, the proposed method is evaluated via sufficient experiment data covering different temperatures and constraint conditions. Experimental results show that the root means square errors of measure equation and SoC estimation can be bounded within 1% and 0.54%. In conclusion, the proposed method provides a new perspective for SoC estimation of LFP batteries.
AB - State of Charge (SoC) estimation for LiFePO4 (LFP) batteries is particularly challenging due to the flat open circuit voltage (OCV) characteristics. This paper proposes a novel method of SoC estimation for LFP batteries based on expansion force which has not been investigated in the existing literature. After a series of experiments, it is found that the expansion force is more sensitive to SoC than voltage and independent of the dynamic current. However, the estimation work is still technically challenging because of the non-monotonic relationship between expansion force and SoC. To cope with it, an expansion force model based on the least-square support vector machine (LSSVM) method is firstly exploited, which is employed as the measurement equation of adaptive unscented Kalman filters (AUKF). Meanwhile, the moving window method is applied to enhance the adaptability of the established model and then an accurate SoC estimation result is attained. Finally, the proposed method is evaluated via sufficient experiment data covering different temperatures and constraint conditions. Experimental results show that the root means square errors of measure equation and SoC estimation can be bounded within 1% and 0.54%. In conclusion, the proposed method provides a new perspective for SoC estimation of LFP batteries.
KW - Adaptive unscented Kalman filters (AUKF)
KW - Expansion force
KW - Least-square support vector machines (LSSVM)
KW - LiFePO batteries
KW - SoC estimation
UR - http://www.scopus.com/inward/record.url?scp=85128560766&partnerID=8YFLogxK
U2 - 10.1016/j.est.2022.104559
DO - 10.1016/j.est.2022.104559
M3 - Article
AN - SCOPUS:85128560766
SN - 2352-152X
VL - 50
JO - Journal of Energy Storage
JF - Journal of Energy Storage
M1 - 104559
ER -