This paper presents a state-of-health (SOH) estimation algorithm and hardware platform for lithium-ion batteries. Based on features obtained from the battery’s electrochemical impedance spectroscopy (EIS), an artificial neural network (ANN)-based SOH algorithm is developed. EIS measurements collected at different aging levels are utilized to train and test the SOH estimation algorithm. The minimum impedance magnitude and the impedance magnitude at zero phase show correlations with the battery SOH level and can be utilized to indicate the SOH value. The SOH estimation algorithm performance is evaluated, and the performance evaluation results indicate that the SOH estimation algorithm can be utilized to estimate the SOH.
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An Electrochemical Impedance Spectrum-Based State of Health Differential Indicator with Reduced Sensitivity to Measurement Errors for Lithium–Ion Batteries
As the use of electrochemical batteries, especially lithium–ion (Li-Ion) batteries, increases due to emerging applications and expanding markets, the accurate and fast estimation of their state of health (SOH) is becoming increasingly important. The accuracy of the SOH estimation is highly dependent on the correlation strength between the used indicator and SOH and the accuracy of the SOH indicator measurement. This paper presents a new differential indicator which has a strong and consistent correlation with the SOH of Li-Ion batteries, based on a new Electrochemical Impedance Spectrum (EIS) Phase–Magnitude relationship. It is shown in this paper that the EIS Phase–Magnitude relationship exhibits a phase-based differential impedance magnitude SOH indicator between a first-phase peak point and a last-phase valley point. Because of the differential nature of this SOH indicator and because the two impedance values are measured at a phase peak point and a valley phase point regardless of the phase absolute values, the effect of impedance measurement shift/offset (error) on SOH estimation is reduced. This supports the future development of more accurate and faster online and offline SOH estimation algorithms and systems that have a higher immunity to impedance measurement shift/offset (error). Furthermore, in this work, the EIS was measured for a lithium–ion battery that was down to a ~15% SOH, which was not only used to support the conclusions of this paper, but also helped in filling a gap in the literature for EIS data under deep/high degradation levels.
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- Award ID(s):
- 2213918
- PAR ID:
- 10609321
- Publisher / Repository:
- https://www.mdpi.com/journal/batteries
- Date Published:
- Journal Name:
- Batteries Journal
- Volume:
- 10
- Issue:
- 10
- ISSN:
- 2313-0105
- Page Range / eLocation ID:
- 368
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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