TY - JOUR
T1 - Model improvement and SOC estimation based on aluminium ion batteries
AU - Jia, Wang
AU - Sun, Xiujuan
AU - Lin, Mengchang
AU - Wang, Chuanjiang
AU - Du, Huiping
AU - Chen, Pijian
AU - Liu, Zhidong
N1 - Funding Information:
The Application research and industrialisation of rechargeable aluminium ion battery technology. The project of Qingdao Science and Technology Innovation Top Talent Team Introduction. (No.17-2-1-1-zhc).
Publisher Copyright:
© 2021 IOP Publishing Ltd.
PY - 2021/3
Y1 - 2021/3
N2 - The main work of this paper can be divided into three parts: (1) An aluminium ion battery is made, the battery capacity is calibrated under a constant current, and the battery's voltage and current data under pulse conditions is collected. (2) The influence of the hysteresis effect on the SOC estimation of aluminium ion batteries is fully considered. A model suitable for aluminium ion batteries is established based on the model that the parameter identification and verification was carried out with. (3) The error with the EKF and AEKF algorithms in estimating the SOC of aluminium ion batteries is compared. The experimental results show that the model established in this paper can describe the working state of aluminium ion batteries very well. Compared with the EKF algorithm, the AEKF algorithm is more accurate in estimating the SOC of aluminium ion batteries.
AB - The main work of this paper can be divided into three parts: (1) An aluminium ion battery is made, the battery capacity is calibrated under a constant current, and the battery's voltage and current data under pulse conditions is collected. (2) The influence of the hysteresis effect on the SOC estimation of aluminium ion batteries is fully considered. A model suitable for aluminium ion batteries is established based on the model that the parameter identification and verification was carried out with. (3) The error with the EKF and AEKF algorithms in estimating the SOC of aluminium ion batteries is compared. The experimental results show that the model established in this paper can describe the working state of aluminium ion batteries very well. Compared with the EKF algorithm, the AEKF algorithm is more accurate in estimating the SOC of aluminium ion batteries.
KW - Aluminium battery model
KW - Hysteresis voltage
KW - Parameter identification and verification
KW - State of charge estimation
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U2 - 10.1088/2631-8695/abe7d2
DO - 10.1088/2631-8695/abe7d2
M3 - Article
AN - SCOPUS:85103541378
SN - 2631-8695
VL - 3
JO - Engineering Research Express
JF - Engineering Research Express
IS - 1
M1 - 015038
ER -