Abstract: Standardizing the driving behavior of heavy trucks can effectively reduce fuel consumption， which plays a positive role in energy conservation and emission reduction in the transportation industry and the national “Double Carbon” strategy. This paper collects CAN bus data of several heavy trucks for one month， and quantitatively analyzes the relationship between different accelerating behaviors （rapid acceleration， normal driving， rapid deceleration） and fuel consumption. To the problem of the low efficiency and poor accuracy of the existing fuel consumption prediction methods， this paper improves the LSTM model and proposes a shared weight LSTM model （Shared-LSTM）. Based on the collected vehicle CAN bus data， this paper compares and analyzes the fuel consumption prediction effects of Shared-LSTM， GRU and BP neural network models under the same vehicle type， the same road condition and multiple behaviors. The experimental results show that the prediction efficiency of the improved LSTM model is improved by more than 3% under different accelerating driving behaviors， and the prediction indexes in all aspects are better than other models. Taking the rapid acceleration driving behavior as an example， the Shared-LSTM model is reduced by more than 5% compared with GRU and BP neural network in terms of mean absolute error， mean square error and mean root error. Therefore， the Shared-LSTM model can be widely used to predict fuel consumption under a variety of driving behaviors.

Key words: heavy truck, fuel consumption prediction, driving behavior, LSTM, prediction efficie