Remaining useful life prediction of lithium-ion battery using a novel health indicator

Remaining useful life (RUL) prediction plays a significant role in the health prognostic of lithium-ion batteries (LIBs). The capacity or internal resistance is commonly used to quantify degradation process and predict RUL of LIB, but those two indicators are difficult to be obtained due to complex operational conditions and high costs, respectively. To address this issue, we extract a novel health indicator (HI) from the battery current profiles that can be directly measured online. Furthermore, the indicator is optimized by Box-Cox transformation and evaluated by correlation analysis for degradation modeling accurately. Finally, relevance vector machine (RVM) algorithm is utilized to make a probabilistic prediction for battery RUL based on the extracted HI. The correlation analysis verifies the effectiveness of the novel HI, and comparative experiments demonstrate the proposed method can predict RUL of LIB more accurately.

Automated summary

A novel health indicator (HI) is proposed to address the difficulties in obtaining traditional indicators for RUL prediction. Extracted from battery current profiles, optimized by Box-Cox transformation, and used in probabilistic prediction with RVM algorithm, the HI effectively improves the accuracy of LIB RUL prediction.