Multi-stage state of health estimation of lithium-ion battery with high tolerance to heavily-partial charging

State of health (SOH) is critical to the management of lithium-ion battery (LIB) due to its deep insight into the health diagnostic and protection. However, the absence of complete charging data, which is common in practice, risks impairing the charging-based SOH estimators. This paper proposes a multi-stage SOH estimation method with a broad scope of applications, including the unfavorable but practical scenarios of heavily-partial charging. In particular, different sets of health indicators (HIs), covering both the morphological IC features and the voltage entropy information, are extracted from the partial CC charging data with different initial charging voltage to characterize the aging status. Following this endeavor, artificial neural network (ANN)-based HI fusion is proposed to estimate the SOH of LIB precisely in real time. The proposed method is evaluated with long-term aging experiments performed on different types of LIBs. Results validate several superior merits of the proposed method, including high estimation accuracy, high tolerance to partial charging, strong robustness to cell inconsistency, and wide generality to different battery types