Multi-Segment State of Health Estimation of Lithium-ion Batteries Considering Short Partial Charging

Abstract

State of health (SOH) is a critical state parameter of lithium-ion batteries (LIBs). Health indicators (HIs), which are derived from the measured features of LIBs, are used in the current data-driven SOH estimation techniques to determine SOH. However, the common partial charging and discharging make it challenging to derive reliable HIs. In this paper, a SOH estimation approach considering short partial charging is proposed. Unlike other techniques, the constant current charging stage is divided into short segments, the HI, based on the charging capacity and actual initial charging voltage, is extracted within each short segment, and a kernel ridge regression-based estimator is created to characterize the SOH mapping relationship. Subsequently, an estimator fusion frame is established to merge the estimates of the eligible segments, which is decided based on the actual start and end charging voltages of the partial charging. The effectiveness of the proposed approach is validated with two well-known LIBs aging datasets containing real partial charging cycles. The results are satisfactory in terms of accuracy, robustness to partial charging, and good generality to different types of LIBs. Effective SOH value can be deduced whenever the charging voltage range covers at least one short estimation segment.