SOH estimation for lithium-ion batteries based on combination of frequency impedance characteristics

doi:10.19799/j.cnki.2095-4239.2022.0721

Abstract

Abstract:

Accurate estimating the state of health (SOH) of electric vehicle batteries is crucial for their safe and efficient operation. One approach to achieve high-precision SOH estimation is by extracting health characteristics from electrochemical impedance spectroscopy (EIS). However, collecting online EIS data requires high-tech on-board equipment, posing a challenge to the efficacy of this technique. In addition, SOH estimation based on single frequency impedance leads to low accuracy. To address these issues, a new SOH estimation method has been proposed in this study that combines the frequency impedance characteristics. The method involves forming a combination of frequency impedance characteristics by merging the imaginary impedance part in the first 120 cycles at 10 Hz with that in the last 320 cycles at 7.94 Hz after analyzing the experimental data. The method then involved training a long short term memory neural network model with test data from B1 and B2 cells to estimate battery SOH, based on the selected combination frequency impedance characteristics. Subsequently, this model was validated with data from B3 and B4 cells. Results estimate that SOH estimation model based on the combination of frequency impedance features yields a root mean square error of 0.3% at least. This figure is at least 23.9% lower than that achieved with the single frequency impedance model. Therefore, the SOH estimation method not only facilitates performing impedance measurements, but it also promises high estimation accuracy. Additionally, it can be applied to online SOH estimation.

Key words: lithium-ion battery, state of health, electrochemical impedance spectroscopy, correlation analysis, long short-term memory neural network

CLC Number:

TM 912

Linze LI, Xiangwen ZHANG. SOH estimation for lithium-ion batteries based on combination of frequency impedance characteristics[J]. Energy Storage Science and Technology, 2023, 12(5): 1705-1712.

Figures/Tables 14

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电池编号	初始容量/mAh
B1	3009
B2	3018
B3	3013
B4	2993

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