A Fast Estimation Algorithm for State of Health of Lithium-ion Battery Modules Based on Lorenz Plot

Abstract

Accurate battery state of health (SOH) assessment is one of the keys to the safe and stable operation of battery systems. A novel fast SOH evaluation method for lithium-ion battery modules is proposed based on Lorenz plot (LP). The average Lorenz radius (ALR) of a module in a certain SOC interval is extracted as a health factor for this module SOH. The research results show that the ALR value of the module gradually increases in the low SOC range of the charging curve or the discharge curve as the battery module ages. When the ALR values at 20 % SOC are extracted as health factors, the ALR-SOH evaluation models present a linear negative correlation with a goodness of fit of over 0.99. When the voltage data from any SOC interval containing a voltage of 20 % SOC are extracted to calculate the ALR values of the module, the accuracy of the ALR-SOH evaluation models based on the discharge voltage is generally better than that based on the charging voltage. When the ALR values of the module are calculated using voltage data from any SOC interval starting from 10 % SOC during discharge, the goodness of fit of the ALR-SOH evaluation models based on the discharge voltage data is above 0.97, suggesting the robustness of the SOH evaluation method based on LP. This will provide ample options for the practical application of this method.