A Long‐term Energy Management Strategy for Fuel Cell Electric Vehicles Using Reinforcement Learning

Fuel Cells ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 753-761
Author(s):  
Y. F. Zhou ◽  
L. J. Huang ◽  
X. X. Sun ◽  
L. H. Li ◽  
J. Lian
Energies ◽  
2021 ◽  
Vol 14 (13) ◽  
pp. 3810
Author(s):  
Laeun Kwon ◽  
Dae-Seung Cho ◽  
Changsun Ahn

The design of an energy management strategy is critical to improving the fuel efficiency of a vehicle system with an alternative powertrain system, such as hybrid electric vehicles or fuel cell electric vehicles. In particular, in fuel cell electric vehicles, the energy management strategy should consider system degradation and fuel savings because the hardware cost of the fuel cell system is much higher than that of a conventional powertrain system. In this paper, an easily implantable near-optimal energy management controller is proposed. The proposed controller distributes power generation between the fuel cell and the battery to simultaneously minimize system degradation and fuel usage. The controller is designed to consider the degradation cost and fuel cost in the framework of the equivalent consumption minimization strategy concept. The proposed controller was validated with a fuel cell electric vehicle model in MATLAB/Simulink (MathWorks, Natick, USA). The proposed control strategy showed significant overall cost reduction compared to a thermostat control strategy and a conventional Equivalent Consumption Minimization Strategy (ECMS) strategy.


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