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A novel dual correction extended Kalman filtering algorithm for the state of charge real-time estimation of packing lithium-ion batteries.

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

Authors

HaoTian Shi

Shunli Wang

Carlos Fernandez

Chunmei Yu

Yongcun Fan

Wen Cao



Abstract

This paper explores the state estimation method of lithium-ion battery pack through theoretical analysis and experimental research. Combining the advantages of the empirical models of various electrochemical models, a new type of composite electrochemistry-dual circuit polarization (E-DCP) model is proposed to better reflect the dynamic performance of the power lithium-ion battery under the conditions of meeting its safe and reliable energy supply requirements. Using the multi-innovation least squares (MILS) algorithm to identify the parameters in the E-DCP model online, so that it has the characteristics of high data utilization efficiency and high parameter identification accuracy. The battery charge and discharge efficiency function is introduced to dynamically modify the battery capacity, and the dynamic function is used to improve the Kalman gain in the extended Kalman filter (EKF), a new type of based on dynamic function improvement and combined with actual capacity correction (FC-DEKF) algorithm is applied to the estimation of battery pack operating characteristics, which solves the problem that the traditional EKF algorithm is difficult to estimate errors when the system input change rate is large. The experimental results of urban dynamometer driving schedule (UDDS) and complex charge-discharge cycle test show that the maximum error of terminal voltage does not exceed 0.04V, the accuracy is 99.05%, and the errors of MILS algorithm combined with FC-DEKF algorithm for SOC estimation are all within 1%. The proposed equivalent circuit modeling method and state estimation correction strategy provide a theoretical basis for the reliable application of high-power lithium-ion battery packs.

Citation

SHI, H., WANG, S., FERNANDEZ, C., YU, C., FAN, Y. and CAO, W. 2020. A novel dual correction extended Kalman filtering algorithm for the state of charge real-time estimation of packing lithium-ion batteries. International journal of electrochemical science [online], 15(12), pages 12706-12723. Available from: https://doi.org/10.20964/2020.12.52

Journal Article Type Article
Acceptance Date Oct 7, 2020
Online Publication Date Oct 31, 2020
Publication Date Dec 31, 2020
Deposit Date Jan 15, 2021
Publicly Available Date Jan 15, 2021
Journal International journal of electrochemical science
Print ISSN 1452-3981
Publisher Electrochemical Science Group
Peer Reviewed Peer Reviewed
Volume 15
Issue 12
Pages 12706-12723
DOI https://doi.org/10.20964/2020.12.52
Keywords Electrochemistry-dual circuit polarization model; Parameteridentification; Multi-innovation least squares; Improved extended Kalman filter; Dynamic function optimization; Lithium-ion batteries
Public URL https://rgu-repository.worktribe.com/output/1107440

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