Volume 08: Proceedings of Applied Energy Symposium: MIT A+B, United States, 2020

Research on Energy Management Strategy of Hybrid Electric Vehicles Based on Hierarchical Control in the Connected Environment Shichun Yang, Hehui Xie, Fei Chen, Jian Liu, Song Feng, Junbing Zhang

Abstract

Vehicle energy management is the core technology of hybrid vehicles, which determines the fuel economy and emission performance of the vehicle. At present, most of the common energy management is based on known operating conditions, without considering actual road traffic information, which makes vehicles unable to achieve optimal energy management. With the development of GPS and ITS, future traffic information can be obtained in advance. In the paper, a hierarchical energy control method for hybrid electric vehicles is proposed. Model predictive control algorithm is utilized to predict the optimal vehicle velocity in the upper controller. The lower controller is designed to follow the optimal velocity, and uses the neural network control algorithm to optimize the power distribution between the engine and the motor to reduce fuel consumption. Compared with the traditional energy management strategy, the proposed method can prevent the vehicle from stopping at the red light, thereby reducing the fuel consumption of the vehicle to achieve the purpose of saving fuel consumption.

Keywords Hierarchical Control, energy management, neural network, connected environment

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