In order to comprehensively assess the state of health (SOH) of station-use 2 V/500 AH batteries to prolong the battery life and improve the monitoring accuracy. The effects of temperature, charge/discharge current and discharge depth were analyzed through orthogonal experiments, combined with ultrasonic nondestructive testing technology to capture the micro changes of electrodes, and state of health(HHT) noise reduction and construction of grey wolf optimizer-artificial neural network(GWO-ANN) model to realize the SOH prediction with high accuracy. The experiments show that the life span is shortened by 40%~50% for every 10 ℃ increase in temperature, and the life span at 50% discharge depth is three times of that at 100%. The prediction accuracy of the HHT-GWO-ANN model (R²=0.941) is significantly better than that of the ANN model (R²=0.749), and the accuracy rate is more than 90% under complex working conditions. The proposed method combining ultrasonic nondestructive testing and the HHT-GWO-ANN model provides a high-precision and nondestructive solution for the SOH evaluation of batteries, and the “shallow charging and shallow discharging” strategy can extend the battery life by more than 20%, which is of great value for the extension of the battery life and intelligent monitoring.