Remaining useful life prediction method for lithium-ion batteries

In the field of battery management, accurately predicting the remaining useful life (RUL) of lithium-ion batteries is critical to safeguarding battery and device safety. However, most of the methods for predicting RUL mainly consider data such as voltage, current and capacity during charging or discharging of the battery, ignoring the effect of temperature change in the charging and discharging process. To address this problem, a method for predicting the remaining useful life of lithium-ion batteries is proposed. First, extract the two features of the time when the battery reaches the maximum temperature during charging and discharging; then, use these two features to construct a health indicator; finally, this health indicator is utilized to construct a remaining useful life prediction model. The proposed method is validated using the aging data of lithium-ion batteries released by NASA Ames Research Center, and the results show that the method can accurately predict their remaining useful life.