The recycling of retired power batteries is about to reach a peak. The secondary use of retired batteries can effectively avoid the waste of resources and environmental pollution. State of health (SOH) assessment is a key evaluation index for the secondary use. But traditional methods for estimating the SOH have the disadvantages of time and energy consumption. This paper proposes a rapid estimation strategy for the SOH of retired batteries based on resting voltage curves. After discharging the retired batteries to the same voltage lower limit, there will be differences in the state of charge (SOC) of different SOH batteries. Eventually, the resting voltage curves of different SOH batteries varies. By analyzing the health characteristics from this resting voltage difference, this paper achieves rapid estimation of the SOH of retired batteries. There are outliers in data collection due to acquisition errors, which can lead to incorrect training of regression algorithm models in the training set. In order to address this problem, this paper analyzes the impact of the number of outliers on the accuracy of regression algorithm estimation of SOH. This paper proposes using the density-based spatial clustering of applications with noise (DBSCAN) clustering algorithm to identify these outliers. The identifications of outliers can avoid the impact of outliers on the accuracy of SOH estimation model and effectively improve the accuracy of SOH estimation.