Abstract: Accurate estimation of state of health (SOH) and remaining useful life (RUL) of lithium batteries is crucial to ensure the safe and stable. operation of batteries. However, both of them are difficult to be directly measured. A SOH and RUL joint estimation approach based on gaussian process regression (GPR) was proposed in this paper. Health factor (HF) was extracted from the charging curve and indirect health factor (IHF) was obtained through principal component analysis (PCA). Then, an aging battery model based on GPR was established to estimate SOH. Furthermore, the least squares support vector machine (LS-SVM) was used to predict IHF in the future cycles, and the IHF obtained were combined with the established battery aging model to realize RUL estimation. Two battery data sets at different temperatures were utilized to verify the accuracy and adaptability of the algorithm. The results show high accuracy and robustness of the proposed method.