To solve the problems of the existing remaining useful lifetime prediction methods for lithium battery with low prediction accuracy and long training time, a prediction model based on GBDT algorithm with grid search method is proposed. Firstly, analyze the charge-discharge cycle of lithium battery and select voltage, current and temperature as useful health index. Secondly, process the outliers of historical data and average useful health index data as feature input. Finally, establish the remaining useful lifetime prediction model for lithium battery by GBDT algorithm and optimize parameters by grid search method. Based on the capacity decay data of NASA lithium battery, the results show that the prediction model is superior to other methods about tenfold in RMSE, MAE, MAPE. The remaining useful lifetime prediction error is within 0. 05 and the training time reduces to 4. 5 s.