Accurate estimation of the state of charge (SOC) and state of health (SOH) is a fundamental requirement for the management system of a lithium-ion battery, but also important to the safety and operational effectiveness of electric vehicles and energy storage systems. Here a model-based method is implemented to assess the SOC and SOH simultaneously. An equivalent circuit model is employed to describe the battery dynamics with recursive least squares online identifying model parameters and unscented Kalman filter estimating battery state. A minimalist electrochemical model is proposed to describe the distribution of the lithium content inside the battery relating the SOH to the capacity fading due to irreversible loss of Li. Based on the real-time capacity value, the state of charge could further be estimated. Comparing the experimental results shows that the battery capacity, i.e., SOH could be predicted timely with a mean error around 2%, which confirms the validity of the proposed co-estimation method for SOC and SOH.