Fuel cell;
Hybrid power system;
Energy management;
Adaptive optimal control;
CELL;
BATTERY;
D O I:
10.1016/j.jpowsour.2010.11.127
中图分类号:
O64 [物理化学(理论化学)、化学物理学];
学科分类号:
070304 ;
081704 ;
摘要:
Energy management of a fuel cell/ultracapacitor hybrid power system aims to optimize energy efficiency while satisfying the operational constraints. The current challenges include ensuring that the non-linear dynamics and energy management of a hybrid power system are consistent with state and input constraints imposed by operational limitations. This paper formulates the requirements for energy management of the hybrid power system as a constrained optimal-control problem, and then transforms the problem into an unconstrained form using the penalty-function method. Radial-basis-function networks are organized in an adaptive optimal-control algorithm to synthesize an optimal strategy for energy management. The obtained optimal strategy was verified in an electric vehicle powered by combining a fuel-cell system and an ultracapacitor bank. Driving-cycle tests were conducted to investigate the fuel consumption, fuel-cell peak power, and instantaneous rate of change in fuel-cell power. The results show that the energy efficiency of the electric vehicle is significantly improved relative to that without using the optimal strategy. (c) 2010 Elsevier B.V. All rights reserved.
机构:
Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R ChinaChinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
Li, Chun-Yan
Liu, Guo-Ping
论文数: 0引用数: 0
h-index: 0
机构:
Univ Glamorgan, Fac Adv Technol, Pontypridd CF37 1DL, M Glam, Wales
Harbin Inst Technol, Ctr Control Theory & Guidance Technol, Harbin, Peoples R ChinaChinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
机构:
Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R ChinaChinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
Li, Chun-Yan
Liu, Guo-Ping
论文数: 0引用数: 0
h-index: 0
机构:
Univ Glamorgan, Fac Adv Technol, Pontypridd CF37 1DL, M Glam, Wales
Harbin Inst Technol, Ctr Control Theory & Guidance Technol, Harbin, Peoples R ChinaChinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China