期刊
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
卷 87, 期 -, 页码 17-29出版社
ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
DOI: 10.1016/j.ymssp.2016.03.002
关键词
Smart hybrid electric vehicle; Multi-objective optimal problem; Fuel consumption optimization; Car-following; Model predictive control
资金
- National Science Fund for Excellent Young Scholars of the People's Republic of China [51422505]
- National Key Technology R&D Program of the Ministry of Science and Technology [2013BAG14B01]
Hybrid electric vehicles (HEVs) provide large potential to save energy and reduce emission, and smart vehicles bring out great convenience and safety for drivers. By combining these two technologies, vehicles may achieve excellent performances in terms of dynamic, economy, environmental friendliness, safety, and comfort. Hence, a smart hybrid electric vehicle (s-HEV) is selected as a platform in this paper to study a car-following process with optimizing the fuel consumption. The whole process is a multi-objective optimal problem, whose optimal solution is not just adding an energy management strategy (EMS) to an adaptive cruise control (ACC), but a deep fusion of these two methods. The problem has more restricted conditions, optimal objectives, and system states, which may result in larger computing burden. Therefore, a novel fuel consumption optimization algorithm based on model predictive control (MPC) is proposed and some search skills are adopted in receding horizon optimization to reduce computing burden. Simulations are carried out and the results indicate that the fuel consumption of proposed method is lower than that of the ACC+EMS method on the condition of ensuring car-following performances. (C) 2016 Elsevier Ltd. All rights reserved.
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