Energy management of hybrid electric vehicles: A review of energy optimization of fuel cell hybrid power system based on genetic algorithm

2020 ◽  
Vol 205 ◽  
pp. 112474 ◽  
Author(s):  
Xueqin Lü ◽  
Yinbo Wu ◽  
Jie Lian ◽  
Yangyang Zhang ◽  
Chao Chen ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Fuel Cell ◽  
Power System ◽  
Energy Management ◽  
Electric Vehicles ◽  
Cell Hybrid ◽  
Hybrid Electric Vehicles ◽  
Energy Optimization ◽  
Hybrid Power System ◽  
Hybrid Power

scholarly journals Novel Fuzzy Control Energy Management Strategy for Fuel Cell Hybrid Electric Vehicles Considering State of Health

Energies ◽  
2021 ◽  
Vol 14 (20) ◽  
pp. 6481
Author(s):  
Xiao Hu ◽  
Shikun Liu ◽  
Ke Song ◽  
Yuan Gao ◽  
Tong Zhang
Keyword(s):  
Genetic Algorithm ◽  
Fuel Cell ◽  
Fuzzy Control ◽  
Energy Management ◽  
Electric Vehicles ◽  
Cell Hybrid ◽  
Hybrid Electric Vehicles ◽  
Fuzzy Controller ◽  
Rule Based ◽  
Hydrogen Consumption

Due to the low efficiency and high pollution of conventional internal combustion engine vehicles, the fuel cell hybrid electric vehicles are expected to play a key role in the future of clean energy transportation attributed to the long driving range, short hydrogen refueling time and environmental advantages. The development of energy management strategies has an important impact on the economy and durability, but most strategies ignore the aging of fuel cells and the corresponding impact on hydrogen consumption. In this paper, a rule-based fuzzy control strategy is proposed based on the constructed data-driven online estimation model of fuel cell health. Then, a genetic algorithm is used to optimize this fuzzy controller, where the objective function is designed to consider both the economy and durability by combining the hydrogen consumption cost and the degradation cost characterized by the fuel cell health status. Considering that the rule-based strategy is more sensitive to operating conditions, this paper uses an artificial neural network for predictive control. The results are compared with those obtained from the genetic algorithm optimized fuzzy controller and are found to be very similar, where the prediction accuracy is assessed using MAPE, RMSE and 10-fold cross-validation. Experiments show that the developed strategy has a good generalization capability for variable driving cycles.

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