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

Abstract

The pressing need to reduce CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> emissions has triggered the exponential growth of electric vehicles 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 paper 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 voltage segments 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, voltage segment of 0.1 V (about 13% SOC), within a range of 3.6 V to 3.9 V for nominal LIBs cell voltage (about 54 to 91% SOC), is recommended to be used for the SOH estimation. In this work, Nickel Cobalt Aluminum Oxides and Lithium Cobalt Oxide chemistry type are used for validation. The use of small voltage decay segments provides the capability for online SOH estimation.