Nonlinear model predictive control for efficient and robust airpath management in fuel cell vehicles

被引:2
|
作者
Mele, Agostino [1 ,2 ]
Dickinson, Paul [1 ]
Mattei, Massimiliano [3 ]
机构
[1] Garrett Mot, Brno, Czech Republic
[2] Univ Campania Luigi Vanvitelli, Dept Engn, I-81031 Aversa, Italy
[3] Univ Naples Federico II, DIETI, I-80125 Naples, Italy
关键词
Nonlinear Model Predictive Control (NMPC); Fuel Cell Electric Vehicle (FCEV); Compressor surge; Set-point steady state optimization; NMPC with preview information; Fuel cell airpath control; OXYGEN EXCESS RATIO; CONTROL SCHEME; AIR-FLOW; SYSTEM; MPC; PRESSURE; DRIVEN;
D O I
10.1016/j.ijhydene.2023.03.398
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
The fuel cell airpath multivariable control problem of optimally coordinating the electric compressor motor and the back-pressure valve to achieve efficient and safe conditions, for both steady state and transient operation, has not been completely addressed in the literature. This paper proposes a nonlinear model predictive control strategy, implemented via the Garrett Motion proprietary NMPC toolbox, to regulate the oxygen stoichiometry and the cathode pressure of an automotive fuel cell airpath system, while avoiding compressor surge and air starvation. The controller set-points are optimized, using the nonlinear model, to achieve the maximum system power as a function of the operating stack condition. The effectiveness and robustness of the proposed control strategy have been validated by means of a simulated World harmonized Light-duty vehicles Test Cycle (WLTC), under both state feedback and model parameters uncertainties.(c) 2023 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.
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页码:29295 / 29312
页数:18
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