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Improved splice‐electrochemical circuit polarization modeling and optimized dynamic functional multi‐innovation least square parameter identification for lithium‐ion batteries.

Shi, Haotian; Wang, Shunli; Fernandez, Carlos; Yu, Chunmei; Fan, Yongcun; Cao, Wen

Authors

Haotian Shi

Shunli Wang

Chunmei Yu

Yongcun Fan

Wen Cao



Abstract

The internal nonlinearity of the lithium-ion battery makes its mathematical modeling a big challenge. In this paper, a novel lithium-ion battery splice-electrochemical circuit polarization (S-ECP) model is proposed, which integrates the strengths of various lithium-ion battery models and refines the ohm and polarization characteristics of the electrochemical Nernst model and the differences in charge-discharge internal resistance. Moreover, by applying the one-sided limit to the discrete system, a multi-innovation least squares algorithm optimized based on the dynamic function (F-MILS) introduced to constrain the original innovation weight is put forward, which tackles the problem of large algorithm errors caused by huge changes in the system input. In order to evaluate the regulating ability of weight constraint factors, the relevant parameter values in the dynamic function are discussed as independent variables. Furthermore, parameters of the S-ECP model are identified online by HPPC experimental data combined with the multi-innovation least squares (MILS) algorithm ameliorated by the dynamic function, and the convergence speed of parameters in the identification process is analyzed. Finally, an urban dynamometer driving schedule experiment is carried out on the lithium-ion battery under more complex working conditions. It is revealed that the accuracy of F-MILS is 0.5% higher than that of unoptimized MILS, further confirming the accuracy of the S-ECP model and the superiority of the F-MILS algorithm.

Citation

SHI, H., WANG, S., FERNANDEZ, C., YU, C., FAN, Y. and CAO, W. [2021]. Improved splice-electrochemical circuit polarization modeling and optimized dynamic functional multi-innovation least square parameter identification for lithium-ion batteries. International journal of energy research [online], Early View. Available from: https://doi.org/10.1002/er.6807

Journal Article Type Article
Acceptance Date Apr 18, 2021
Online Publication Date May 9, 2021
Deposit Date May 27, 2021
Publicly Available Date May 10, 2022
Journal International Journal of Energy Research
Print ISSN 0363-907X
Electronic ISSN 1099-114X
Publisher Wiley Open Access
Peer Reviewed Peer Reviewed
DOI https://doi.org/10.1002/er.6807
Keywords Dynamic function optimization; Lithium-ion batteries; Multi-innovation least squares; Parameter identification; Splice-electrochemical circuit polarization model
Public URL https://rgu-repository.worktribe.com/output/1341135