A health indicator extraction based on surface temperature for lithium-ion batteries remaining useful life prediction

Abstract

For the close relationship with safety, surface temperature has been a hot topic in the research of lithium-ion batteries. Studying surface temperature changes can ensure safety and provide information for degradation estimation. However, there are few studies on obtaining degradation information from surface temperature. In this paper, a new health indicator (HI) is proposed to predict the remaining useful life (RUL) of lithium-ion batteries from the discharge surface temperature, which is convenient for real-time measurement and online estimation. First, according to the actual trend of surface temperature, an exponential model is used to fit the surface temperature from 1000 s to the end of the discharge. Then, a new HI is extracted from the model parameters, which indicates the change rate of surface temperature. Pearson and Spearman correlation analysis verified that HI is related to battery capacity degradation. What's more, a comparison of the new HI and other types of HIs is performed. Finally, a method combining HI and relevance vector machine (RVM) is proposed for online RUL prediction. Predictions have been made for different algorithms and starting points, the results show that the new HI is effective for degradation modeling. And the RUL prediction error is less than 5 cycles for 5#, 6# and 7# batteries.