TY - JOUR
T1 - Parameter Identification Method for a Fractional-Order Model of Lithium-Ion Batteries Considering Electrolyte-Phase Diffusion
AU - Jia, Yanbo
AU - Dong, Lei
AU - Yang, Geng
AU - Jin, Feng
AU - Lu, Languang
AU - Guo, Dongxu
AU - Ouyang, Minggao
N1 - Publisher Copyright:
© 2022 by the authors.
PY - 2022/8
Y1 - 2022/8
N2 - The physics-based fractional-order model (FOM) for lithium-ion batteries has shown good application prospects due to its mechanisms and simplicity. To adapt the model to higher-level applications, this paper proposes an improved FOM considering electrolyte-phase diffusion (FOMe) and then proposes a complete method for parameter identification based on three characteristic SOC intervals: the positive solid phase, negative solid phase, and electrolyte phase. The method mainly determines the above three characteristic intervals and identifies four thermodynamic parameters and five dynamic parameters. Furthermore, the paper describes a framework, which first verifies the model and parameter identification method separately based on pseudo two-dimensional model simulations, and secondly verifies FOMe and its parameters as a whole based on the experiments. The results, which are based on simulations and actual (Formula presented.) lithium-ion batteries under multiple typical operating profiles and comparisons with other parameter identification methods, show that the proposed model and parameter identification method is highly accurate and efficient.
AB - The physics-based fractional-order model (FOM) for lithium-ion batteries has shown good application prospects due to its mechanisms and simplicity. To adapt the model to higher-level applications, this paper proposes an improved FOM considering electrolyte-phase diffusion (FOMe) and then proposes a complete method for parameter identification based on three characteristic SOC intervals: the positive solid phase, negative solid phase, and electrolyte phase. The method mainly determines the above three characteristic intervals and identifies four thermodynamic parameters and five dynamic parameters. Furthermore, the paper describes a framework, which first verifies the model and parameter identification method separately based on pseudo two-dimensional model simulations, and secondly verifies FOMe and its parameters as a whole based on the experiments. The results, which are based on simulations and actual (Formula presented.) lithium-ion batteries under multiple typical operating profiles and comparisons with other parameter identification methods, show that the proposed model and parameter identification method is highly accurate and efficient.
KW - fractional-order model
KW - lithium-ion battery
KW - parameter identification
UR - http://www.scopus.com/inward/record.url?scp=85137328607&partnerID=8YFLogxK
U2 - 10.3390/batteries8080090
DO - 10.3390/batteries8080090
M3 - Article
AN - SCOPUS:85137328607
SN - 2313-0105
VL - 8
JO - Batteries
JF - Batteries
IS - 8
M1 - 90
ER -