SOC-SOH estimation method for lithium iron phosphate battery considering energy storage operating conditions and current mutation detection

A method to estimate the SOC-SOH of lithium iron phosphate battery, with consideration of batteries’ characteristic working conditions of energy storage, was utilized to estimate the high-precision state of LiFePO4 battery with the interference of the strong current fluctuation and battery aging in...

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Bibliographic Details
Published in:Ionics Vol. 31; no. 8; pp. 7849 - 7862
Main Authors: Xie, Jun, Ma, Xiaojian, Zhang, Yutong, Wang, Chunxin, Xie, Qing
Format: Journal Article
Language:English
Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.08.2025
Springer Nature B.V
Subjects:
Accuracy
Algorithms
Alternating current
Batteries
Chemistry
Chemistry and Materials Science
Condensed Matter Physics
Efficiency
Electric charge
Electrochemistry
Energy Storage
Iron
Lithium
Lithium-ion batteries
Methods
Mutation
Numerical integration
Optical and Electronic Materials
Parameter estimation
Parameter identification
Power plants
Renewable and Green Energy
State of charge
Working conditions
LiFePO4 battery
Battery energy storage systems
State of charge
Kernel density estimation
State of health
ISSN:0947-7047, 1862-0760
Online Access:Get full text
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Summary:A method to estimate the SOC-SOH of lithium iron phosphate battery, with consideration of batteries’ characteristic working conditions of energy storage, was utilized to estimate the high-precision state of LiFePO4 battery with the interference of the strong current fluctuation and battery aging in the energy storage power station. First, the actual operation data of the energy storage power station based on time sequence was selected to extract characteristic currents and construct testing conditions suitable for the experimental platform. Then, parameters of the second-order RC model were obtained through an offline hybrid pulse power characteristic (HPPC) test. On this basis, the initial values of the forgetting factor recursive least squares (FFRLS) algorithm were determined to realize precise online parameter identification in the conditions of constant current and alternating current. At the same time, the threshold of the mutation test was calculated based on kernel density estimation (KDE) and Gauss-Legendre numerical integration method to quickly identify the current mutation and effectively differentiate the constant current stage and the alternating current stage. At last, the data of the constant current stage were used to estimate the SOH of the battery, which was used for the subsequent SOC estimation in the alternating current stage. The results showed that the method of improving parameter identification precision by incorporating KDE was significantly better than the method of just using HPPC or the FFRLS method based on fixed initial values, and the SOC-SOH joint estimation effect was significantly better than the single estimation of SOC.
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ISSN:0947-7047
1862-0760
DOI:10.1007/s11581-025-06478-9