Hybrid Electric Vehicle Speed Profile Prediction Based on Data Fusion

doi:10.3901/JME.2024.06.342

Abstract

Abstract: Energy Management Strategy (EMS) can optimize energy consumption, reduce emissions and improve fuel economy of hybrid electric vehicles (HEV) in different scenarios. One of the keys of optimization is the prediction of vehicle speed profile： predict the speed profile of the future interval, and then calculate the power demand, and optimize the power distribution of the engine and motor. Therefore, an intelligent prediction method of vehicle velocity profile based on driving behavior and lidar data is proposed, which could be applied to energy optimization of lidar equipped hybrid electric vehicle to satisfy driving intention. Firstly, a driving intention recognition model based on gated recurrent unit (GRU) network is constructed to identify driving intentions from vehicle states, and driver's driving needs are considered in real time. Secondly, in the traffic scene where intelligent vehicles and traditional vehicles are mixed, the speed profile prediction method relying on vehicle communication may not be available. In this study, the traffic flow speed is estimated by lidar equipped on vehicles. The algorithm used the Joint Probability Data Association tracker and Interactive Multipul Model method to get the speed of the vehicle in front relative to the vehicle in real time, without prior knowledge of road and condition. Finally, the driver's expected speed curve in the next second is predicted by integrating the driving intention and the relative speed of the vehicle ahead. Experimental results show that the training accuracy of GRU model is between 85%-95%, and the recognition accuracy can reach 88%, which can effectively identify the driver's driving intention. The proposed method has good velocity measurement accuracy. Prediction speed and actual speed of the gap in a smaller range, can be used for EMS, then for power battery management system (BMS) provides the basis for reasonable control of power battery energy output.

Key words: energy management, velocity profile prediction, data fusion, driving intention, lidar

CLC Number:

U461

GAO Kai, LUO Pan, XIE Jin, HU Lin, CHEN Bin, DU Ronghua. Hybrid Electric Vehicle Speed Profile Prediction Based on Data Fusion[J]. Journal of Mechanical Engineering, 2024, 60(6): 342-353.

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