Stochastic Optimal Control of Parallel Hybrid Electric Vehicles

被引：19
|
作者：
Qin, Feiyan [1 ,2 ]
Xu, Guoqing [1 ,3 ]
Hu, Yue [2 ]
Xu, Kun [1 ]
Li, Weimin [1 ,4 ]
机构：
[1] Chinese Acad Sci, Shenzhen Inst Adv Technol, Shenzhen 518055, Peoples R China
[2] Shenzhen Coll Adv Technol, Univ Chinese Acad Sci, Shenzhen 518055, Peoples R China
[3] Shanghai Univ, Sch Mech Engn & Automat, Shanghai 200072, Peoples R China
[4] Chinese Acad Sci, Jining Inst Adv Technol, Jining 272000, Peoples R China
来源：
ENERGIES | 2017年 / 10卷 / 02期
基金：
中国国家自然科学基金;
关键词：
parallel hybrid electric vehicle; energy management strategy; stochastic optimal control; stochastic dynamic programming; ENERGY MANAGEMENT STRATEGY; POWER MANAGEMENT; OPTIMIZATION;
D O I：
10.3390/en10020214
中图分类号：
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号：
0807 ; 0820 ;
摘要：
Energy management strategies (EMSs) in hybrid electric vehicles (HEVs) are highly related to the fuel economy and emission performances. However, EMS constitutes a challenging problem due to the complex structure of a HEV and the unknown or partially known driving cycles. To meet this problem, this paper adopts a stochastic dynamic programming (SDP) method for the EMS of a specially designed vehicle, a pre-transmission single-shaft torque-coupling parallel HEV. In this parallel HEV, the auto clutch output is connected to the transmission input through an electric motor, which benefits an efficient motor assist operation. In this EMS, demanded torque of driver is modeled as a one-state Markov process to represent the uncertainty of future driving situations. The obtained EMS has been evaluated with ADVISOR2002 over two standard government drive cycles and a self-defined one, and compared with a dynamic programming (DP) one and a rule-based one. Simulation results have shown the real-time performance of the proposed approach, and potential vehicle performance improvement relative to the rule-based one.
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收藏
页数：16
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