Real-time global driving cycle construction and the application to economy driving pro system in plug-in hybrid electric vehicles

被引:86
作者
He Hongwen [1 ,2 ]
Guo Jinquan [1 ,2 ]
Peng Jiankun [1 ,2 ]
Tan Huachun [1 ,2 ]
Sun Chao [1 ,2 ]
机构
[1] Beijing Inst Technol, Natl Engn Lab Elect Vehicles, Beijing 100081, Peoples R China
[2] Beijing Inst Technol, Collaborat Innovat Ctr Elect Vehicles Beijing, Beijing 100081, Peoples R China
基金
中国国家自然科学基金; 中国博士后科学基金;
关键词
Global driving cycle; Traffic information; Tensor completion; Dynamic programming; EDPS; PHEV; ENERGY MANAGEMENT; OPTIMIZATION;
D O I
10.1016/j.energy.2018.03.061
中图分类号
O414.1 [热力学];
学科分类号
摘要
This paper proposes a global driving cycle construction method based on the real-time traffic information, which can realize online optimal energy management for plug-in hybrid electric vehicles (PHEVs). The construction method is mainly divided into three parts: the construction of velocity segments database; the construction of real-time traffic information tensor model database, and the construction of real-time global driving cycle. For the acquisition of the real-time traffic information, a two-step completion method is adopted to obtain the complete and accuracy traffic information; for the driving cycle construction, the velocity segment database, the road section velocity and the Markov transfer matrix with Monte Carlo are used to generate velocity segments which constitute the global driving cycle. With the updated real-time traffic information, the global driving cycle is reconstructed which further reflect the real-time road condition. The efficient dynamic programming (DP) algorithm is applied to realize online energy management in PHEVs. Its simulation shows that the fuel efficiency improves by at least 19.83% compared with charge depleting and charge sustain (CDCS) control strategy. Finally, the economy driving pro system (EDPS) is presented in this paper, and it contributes 5.79% fuel efficiency compared with non-EDPS. (C) 2018 Elsevier Ltd. All rights reserved.
引用
收藏
页码:95 / 107
页数:13
相关论文
共 33 条
[1]  
Anandkumar A, 2014, J MACH LEARN RES, V15, P2773
[2]  
[Anonymous], ENERGIES
[3]  
[Anonymous], P IEEE VTC SPRING MA
[4]   Optimal planning of electric vehicle charging station at the distribution system using hybrid optimization algorithm [J].
Awasthi, Abhishek ;
Venkitusamy, Karthikeyan ;
Padmanaban, Sanjeevikumar ;
Selvamuthukumaran, Rajasekar ;
Blaabjerg, Frede ;
Singh, Asheesh K. .
ENERGY, 2017, 133 :70-78
[5]   Energy management and design optimization for a series-parallel PHEV city bus [J].
Cai, Yuanchun ;
Ouyang, Minggao ;
Yang, Fuyuan .
INTERNATIONAL JOURNAL OF AUTOMOTIVE TECHNOLOGY, 2017, 18 (03) :473-487
[6]  
Chen C., 2003, Freeway performance measurement system (PeMS)
[7]   An on-line predictive energy management strategy for plug-in hybrid electric vehicles to counter the uncertain prediction of the driving cycle [J].
Chen, Zeyu ;
Xiong, Rui ;
Wang, Chun ;
Cao, Jiayi .
APPLIED ENERGY, 2017, 185 :1663-1672
[8]   TENSOR RANK AND THE ILL-POSEDNESS OF THE BEST LOW-RANK APPROXIMATION PROBLEM [J].
de Silva, Vin ;
Lim, Lek-Heng .
SIAM JOURNAL ON MATRIX ANALYSIS AND APPLICATIONS, 2008, 30 (03) :1084-1127
[9]  
Howard R.A., 1960, MATH GAZ, V3, P120
[10]  
Huang Y., 2016, IEEE T SMART GRID, V22, P1