Controlling a brushless direct current motor requires the position information of rotor, and deviation in position recognition can reduce motor efficiency. This study focuses on brushless direct current motor with switch-type Hall sensors and, and estimates the relative installation deviation of Hall sensors based on the minimum deviation principle. It clarifies the ideal commutation position recognition method based on LBEMF to eliminate the installation deviation between Hall sensors, and a pre-calibration method is used to calculate the delay introduced by the signal conditioning circuit. Additionally, a rotor position information correction strategy is proposed. An adaptive position information prediction algorithm based on the Sage-Husa method is designed to filter out the deviation of position information that pre-calibration cannot eliminate. The results of hydrogen circulation pump experimental platform show that, the MAPE of speed fluctuation and the average phase current are decreased by 72. 4% and 62. 8% with the pre-calibration method respectively, and it provides a significant improvement in system efficiency. Compared with the traditional KF prediction algorithm, the proposed algorithm reduces speed fluctuation, speed curve overshoot and commutation time fluctuation by 16. 0% , 19. 4% , 42. 1% , and 35. 0% , respectively, which demonstrates the higher disturbance resistance and more accurate and stable prediction of commutation timing.