The state of charge (SOC), state of health (SOH) and residual mission (RUL) of lithium-ion battery are important state parameters for the safe and stable operation of lithium-ion battery. In this paper, a coupled estimation method of lithium-ion battery state based on the rising segment of charging voltage is proposed to realize the coupled estimation of SOC, SOH and RUL in a long operation cycle from the starting point of battery prediction (SP) to the end of life (EOL). The framework estimates SOH and RUL in the charging phase and SOC in the discharge phase. Firstly, the rising time of constant current charging voltage curve segment is extracted as the health feature (HF), and the HF as the input and cycle capacity as the output are used to establish the least squares support vector machine (LSSVM) battery aging model for SOH estimation; The equivalent circuit model is used for nonlinear fitting of the voltage segment, and the state space model is established with the fitting parameters, which is combined with the unscented Kalman filter algorithm to estimate SOC; Gaussian process regression time series model is used to model the health feature series, and the change trend of HF is predicted by extrapolation of cycle times,which is combined with LSSVM model to predict RUL and the corresponding confidence interval. The experimental results show that the proposed method has high estimation accuracy and good stability.