부산대학교 도서관

Data-driven methods for estimating the state of health (SOH) of lithium-ion batteries (LIBs) are widely used by extracting health indicator (HI) from charge–discharge measurements. However, many existing HIs have shortcoming of heavy computing burden, which causes the difficulty on online implementation. Therefore, this article proposes a novel HI called equal voltage range sampling count number (EVRSCN), which is used to estimate SOH of LIBs. The proposed HI is extracted from the charging process. The EVRSCN HI can be extracted online with reduced calculation burden of battery management system (BMS). Gaussian process regression (GPR) is used to quickly achieve accurate SOH estimation based on EVRSCN. The SOH estimation, which applies three typical and widely used datasets of Oxford, sandia national laboratory (SNL), and center of advanced life cycle engineering (CALCE), shows that the proposed method can achieve a promised accuracy, and the root-mean-square error (RMSE) could lower than 0.5% in some typical cases. In addition, the noise immunity of EVRSCN has been evaluated and compared with the existing HIs. The results show that the proposed EVRSCN has stable and promised SOH estimation accuracy, as well as good noise immunity.