In order to improve the management of lithium-ion batteries (LiBs), it is necessary to predict the state of health (SOH) in real time. In this paper, a real-time prediction method for SOH of LiBs based on digital twin is proposed. First, we construct a future data reconstruction model, which can reconstruct the complete discharge cycle data based on real-time input data. After that, we build an SOH prediction model to predict the future battery SOH by combining historical data and current real-time reconstruction data. Experiments on MIT's public battery dataset show that the proposed method has excellent real-time prediction effect and high accuracy in the initial stage of battery cycling.