Novel State-Of-Health Prediction Method For Lithium-Ion Batteries In Battery Storage System By Using Voltage Variation At Rest Period After Discharge

We have developed a diagnostic architecture of lithium-ion batteries that finds the variation of State of Health (SOH) by actively sensing the voltage transient response at rest period after discharge, that is where each battery cell returns to an equilibrium state. We found that this transient response represents SOH of the battery cells. Since at this rest period after discharge the cell balance controller is not activated, by sensing the voltage variation we can also predict the SOH variation inside the Battery Storage System (BSS). This architecture was applied to a 20 kVA BSS. We have experimentally verified this architecture. We have estimate the BSS's SOH by also considering this SOH distribution only using limited data from the Battery Management System (BMS). Through the analyses of both data from accelerated cycle test and data from the BSS, a robust parameter strongly correlated with battery SOH is identified. This parameter is extracted by using the voltage standing state characteristics of the battery at the rest period after discharge. By measuring the voltage time differential at the transient state in the rest period, we can estimate the BSS's average SOH with the accuracy under 1% with a short period of measurement time (within seconds). By monitoring the minimum voltage, average voltage and maximum voltage at the standing state characteristic that are available from BSS, we can estimate the SOH distribution inside BSS.