Singapore Institute of Technology
Browse

File(s) stored somewhere else

Please note: Linked content is NOT stored on Singapore Institute of Technology and we can't guarantee its availability, quality, security or accept any liability.

Linear Model for Online State of Health Estimation of Lithium-Ion Batteries Using Segmented Discharge Profiles

journal contribution
posted on 2023-09-27, 06:07 authored by Yun Mei, Elisa AngYun Mei, Elisa Ang, Yew Chai PawYew Chai Paw

The pressing need to reduce CO 2 emissions has triggered the exponential growth of electric vehicles (EVs) powered by lithium-ion batteries (LIBs). Accurate real-time state of health (SOH) diagnosis of LIBs is paramount to ensure reliable operation of these batteries through their entire service life. This article presents an intuitive multiple linear regression algorithm using short segment of voltage measurements from a battery’s constant current discharge profile to determine its SOH accurately with a root mean square error (RMSE) of less than 4%. The result from this work shows that the voltage segment of just 0.02 V is sufficient to provide SOH prediction with less than 3% RMSE. However, the accuracy of the model is shown to be dependent on the specific voltage range of data used. To ensure robust SOH estimation and for practicality reasons, the voltage segment of 0.1 V [about 13% state of charge (SOC)], within a range of 3.6–3.9 V for nominal LIBs cell voltage (about 54%–91% SOC), is recommended to be used for the SOH estimation. In this work, nickel cobalt aluminum oxides (NCA) and lithium cobalt oxide (LCO) chemistry type are used for validation. The use of small voltage decay segments provides the capability for online SOH estimation.

History

Journal/Conference/Book title

IEEE Transactions on Transportation Electrification

Publication date

2023-06

Version

  • Published

Usage metrics

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC