共 42 条
A robust co-state predictive model for energy management of plug-in hybrid electric bus
被引:14
作者:

Guo, Hongqiang
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机构:
Liaocheng Univ, Sch Mech & Automot Engn, Liaocheng 252059, Shandong, Peoples R China Liaocheng Univ, Sch Mech & Automot Engn, Liaocheng 252059, Shandong, Peoples R China

Liang, Binbin
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h-index: 0
机构:
Liaocheng Univ, Sch Mech & Automot Engn, Liaocheng 252059, Shandong, Peoples R China Liaocheng Univ, Sch Mech & Automot Engn, Liaocheng 252059, Shandong, Peoples R China

Guo, Hongliang
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h-index: 0
机构:
Liaocheng Univ, Sch Mech & Automot Engn, Liaocheng 252059, Shandong, Peoples R China Liaocheng Univ, Sch Mech & Automot Engn, Liaocheng 252059, Shandong, Peoples R China

Zhang, Kun
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h-index: 0
机构:
Liaocheng Univ, Sch Mech & Automot Engn, Liaocheng 252059, Shandong, Peoples R China Liaocheng Univ, Sch Mech & Automot Engn, Liaocheng 252059, Shandong, Peoples R China
机构:
[1] Liaocheng Univ, Sch Mech & Automot Engn, Liaocheng 252059, Shandong, Peoples R China
关键词:
Plug-in hybrid electric bus;
Energy management;
PMP;
Co-state predictive model;
Design for six sigma;
INTELLIGENT POWER MANAGEMENT;
CONTROL STRATEGY;
RECENT PROGRESS;
VEHICLES;
OPTIMIZATION;
INFORMATION;
DESIGN;
SYSTEM;
D O I:
10.1016/j.jclepro.2019.119478
中图分类号:
X [环境科学、安全科学];
学科分类号:
08 ;
0830 ;
摘要:
This paper proposes a robust co-state predictive model for Pontryagin's Minimum Principle (PMP)-based energy management of plug-in hybrid electric bus (PHEB). The main innovation is that the robust co-state predictive model is only expressed by a simplified formula. Moreover, it is exclusively designed by the Design For Six Sigma (DFSS) method in consideration of noises of driving cycles and stochastic vehicle mass. Because the DFSS strives to minimize the weighted sum of mean and standard deviation of fuel consumption, the proposed strategy can simultaneously improve the fuel economy of the PHEB and its robustness. The DFSS results show that the coefficients of the robust co-state predictive model can be found; the simulation results demonstrate that the proposed strategy has similar fuel economy to dynamic programming (DP); the hardware-in-loop (HIL) results demonstrate that the proposed strategy has good real-time control performance, and can averagely improve the fuel economy by 35.19% compared to a rule-based control strategy. (C) 2019 Elsevier Ltd. All rights reserved.
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