Data-Driven Online Health Estimation of Li-Ion Batteries Using A Novel Energy-Based Health Indicator

Li-Ion batteries have been widely applied in power engineering. Aiming at online state of health (SOH) estimation of Li-Ion batteries, this letter develops a data-driven method using a novel energy-based health indicator (HI). The proposed HI is extracted from the discharge process considering that...

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Bibliographic Details
Published inIEEE transactions on energy conversion Vol. 35; no. 3; pp. 1715 - 1718
Main Authors Liu, Wei, Xu, Yan
Format Journal Article
LanguageEnglish
Published New York IEEE 01.09.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Datasets
Discharge
discharge rate
Discharges (electric)
Electric potential
Estimation
health indicator (HI)
Li-Ion batteries
Lithium
Lithium-ion batteries
Online SOH estimation
Process control
Real-time systems
Rechargeable batteries
Training
Voltage
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Summary:Li-Ion batteries have been widely applied in power engineering. Aiming at online state of health (SOH) estimation of Li-Ion batteries, this letter develops a data-driven method using a novel energy-based health indicator (HI). The proposed HI is extracted from the discharge process considering that the discharge process is often less controllable than the charge process. Unlike previous works where only voltage sequences are considered, this HI incorporates both voltage sequences and discharge rates. Therefore, the developed HI enables online SOH estimation at different discharge rates from the offline training dataset. An open dataset is used for verification of the proposed method and very high accuracy is reported with an average RMSE of 1.23%.
Bibliography:ObjectType-Article-1
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content type line 14
ISSN:0885-8969
1558-0059
DOI:10.1109/TEC.2020.2995112