The accurate determination of the capacity degradation path and state of charge (SoC) is very important for the battery energy storage systems widely used in electric vehicles. This research can be summarized as follows. First, a three-dimensional response surface-based SoC-open circuit voltage (OCV) capacity method covering the entire lifetime of a battery has been constructed, which can be used to describe the battery capacity degradation characteristics and determine the corresponding SoC. Second, in order to capture the battery health state and energy state, a genetic algorithm (GA) is applied to identify the battery capacity and initial SoC based on a first-order RC model. Finally, to verify the proposed method, six experimental cases, including batteries with different aging states and with different data calculation durations, are considered. The results indicate that the maximum capacity and SoC estimation errors are less than 5.0% and 2.1%, respectively, for batteries with different aging states, which points to the high accuracy, stability and robustness of the proposed GA-based battery capacity and initial SoC estimator during the entire battery lifespan.