Validation of Remaining Useful Life Prediction of Li-Ion Battery Based on the Voltage drop

Abstract

Batteries, which are used for the energy storage and power distribution, tend to degrade, and their capacity declines with repeated charging and discharging cycles. The battery is considered to fail when it reaches 80% of its initial
capacity. In general, the battery state under operation can be characterized by identifying the state of health (SOH) and state of life (SOL), which refer the capacity degradation and remaining useful life respectively. Recently, authors have found that the SOH can be indirectly estimated based on the observation that the slope of voltage curve under charging is proportional to the capacity degradation. In the study, only the full charge and discharge cycles under room temperature were conducted with Li-ion battery, which is not the case in reality. In this study, more research is conducted to find out more reliable and robust measurement of the capacity and voltage drop that may be independent of the degradation conditions. Several tests are made under various C-rates, charging stabilization time and surrounding temperatures. Once succeeded, the regression model is established between the capacity and voltage drop, that is used in the estimation of the SOH. Adaptive Particle filtering (APF) framework is then applied during the battery usage to estimate the SOH and predict the RUL in the form of a probability distribution. In the APF, the recursive state transition and measurement functions are given by the empirical degradation model and the regression model, respectively. The APF performs the two functions at the same time which are the anomaly detection and prognostics. Experiments are conducted for a Li-ion battery by repeating full charge discharge cycles, in which a fault is imbedded to change the degradation pattern at a certain moment of the
cycle to illustrate the technique.