TY - JOUR
T1 - Estimation of lithium battery state of charge using PSO-AEKF algorithm
AU - Zhu, Tianye
AU - Shi, Zhihan
AU - Zhang, Tianyang
AU - Zhang, Guangming
N1 - Publisher Copyright:
© Published under licence by IOP Publishing Ltd.
PY - 2024
Y1 - 2024
N2 - For the construction of a second-order model, it is necessary to accurately identify five key parameters: R0, Rp, Cp, Rp, and Rd. Traditional offline parameter identification methods rely solely on fitting the curve during the quiescent period after discharge to determine these parameters. This paper employs the Particle Swarm Optimization (PSO) algorithm combined with a second-order RC discrete model to fit the operating curve, thereby enhancing the model's accuracy. In subsequent estimations, an adaptive Kalman filter is introduced to compare these two sets of parameters.
AB - For the construction of a second-order model, it is necessary to accurately identify five key parameters: R0, Rp, Cp, Rp, and Rd. Traditional offline parameter identification methods rely solely on fitting the curve during the quiescent period after discharge to determine these parameters. This paper employs the Particle Swarm Optimization (PSO) algorithm combined with a second-order RC discrete model to fit the operating curve, thereby enhancing the model's accuracy. In subsequent estimations, an adaptive Kalman filter is introduced to compare these two sets of parameters.
UR - http://www.scopus.com/inward/record.url?scp=85205254748&partnerID=8YFLogxK
U2 - 10.1088/1742-6596/2835/1/012026
DO - 10.1088/1742-6596/2835/1/012026
M3 - 会议文章
AN - SCOPUS:85205254748
SN - 1742-6588
VL - 2835
JO - Journal of Physics: Conference Series
JF - Journal of Physics: Conference Series
IS - 1
M1 - 012026
T2 - 2024 4th International Conference on Energy, Power and Advanced Thermodynamic Systems, EPATS 2024
Y2 - 26 April 2024 through 28 April 2024
ER -