Study on Energy Management Strategy for Hybrid Heavy-duty Truck based on Dynamic Programming

In order to further tap the fuel saving potential of hybrid heavy-duty trucks, energy management strategy based on dynamic programming is designed. Firstly, the quasi-static vehicle model of hybrid heavy-duty truck is established based on Matlab software platform, and the mathematical model of dynam...

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Bibliographic Details
Main Authors: Pei Zhang, Xianpan Wu, Hongming Xu, Changqing Du, Biao He
Format: Article
Language:zho
Published: Editorial Office of Journal of Mechanical Transmission 2020-01-01
Series:Jixie chuandong
Subjects:
Online Access:http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2020.11.002
Description
Summary:In order to further tap the fuel saving potential of hybrid heavy-duty trucks, energy management strategy based on dynamic programming is designed. Firstly, the quasi-static vehicle model of hybrid heavy-duty truck is established based on Matlab software platform, and the mathematical model of dynamic programming optimization control is built with the fuel economy taken as the objective function, the state of charge for the batteries taken as the state variable, and the torque distribution proportion coefficient of hybrid system taken as the control variable. Then, based on energy consumption test conditions of heavy-duty hybrid vehicles in China named C-WTVC, the simulation analysis of hybrid heavy-duty truck is carried out. Finally, based on the simulation results of dynamic programming, improved rules are extracted, and the C-WTVC simulation is carried out based on the joint simulation platform of AMESim and Simulink. The simulation results show that, compared with the rule-based energy management strategy, the energy management strategy based on dynamic programming can improve the fuel economy of hybrid heavy-duty trucks by 13.9%, and the control strategy based on improved rules can improve the fuel economy of hybrid heavy-duty trucks by 2.6%, which verifies the effectiveness of the improved rule-based strategy.
ISSN:1004-2539