In the context of the implementation of the national dual-carbon strategy, the issue of energy storage has also received increasing attention. Energy storage battery state estimation is one of the core functions of the energy storage battery management system, and the estimation of battery state of charge (SOC) is the core link. The SOC estimate is also affected by the current maximum available capacity(Qn) of the battery,. In this paper, the variable forgetting factor recursive least squares method (VFF-RLS) and the adaptive extended Kalman filter method (AEKF) are used to estimate the battery SOC and State Of Health (SOH) together. The maximum available capacity of the battery is added to the Parameters to be identified by VFF-RLS to realize the collaborative estimation of SOH and SOC and improve the estimation accuracy of SOC.