Journal
IEEE TRANSACTIONS ON ENERGY CONVERSION
Volume 35, Issue 3, Pages 1715-1718Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TEC.2020.2995112
Keywords
Discharges (electric); Estimation; Training; Lithium-ion batteries; Process control; Real-time systems; Online SOH estimation; Li-Ion batteries; health indicator (HI); discharge rate
Funding
- Nanyang Assistant Professorship from Nanynag Technological University, Singapore
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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%.
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