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Issue title: Meta-Heuristic Techniques for Solving Computational Engineering Problems: Challenges and New Research Directions
Guest editors: Suresh Chandra Satapathy, Rashmi Agrawal and Vicente García Díaz
Article type: Research Article
Authors: Li, Wenguanga | Feng, Guoshenga; b; * | Jia, Sumeib
Affiliations: [a] School of Traffic and Transportation, Shijiazhuang Tiedao University, Shijiazhuang, China | [b] School of Mechanical Engineering, Shijiazhuang Tiedao University, Shijiazhuang, China
Correspondence: [*] Corresponding author. Guosheng Feng, School of Traffic and Transportation, Shijiazhuang Tiedao University, Shijiazhuang, 050043, China. E-mail: fgs2005@126.com.
Abstract: This paper studies a hybrid power system composed of fuel cells, super capacitors and batteries. Super capacitors are used as auxiliary energy sources to provide the required high power when the car starts and accelerate, while absorbing braking energy when the car is braking. Fuzzy control is used to optimize its energy management strategy. The fuzzy controller of the three-energy source system takes the battery, super capacitor, and bus demand power as the input of the fuzzy controller, and the battery demand power and the fuel cell demand power as the fuzzy controller output. The system realizes the energy distribution of super capacitors, fuel cells and storage batteries according to power requirements, thereby improving the performance of the system and extending the life of components. And with hydrogen consumption as the optimization goal, the particle swarm algorithm is used to optimize the parameters of the fuzzy membership function. Compared with the fuzzy control strategy without particle swarm optimization, the optimized fuzzy energy management strategy reduces the hydrogen consumption of fuel cell vehicles. 10 L/(100 km)-1, which improves the economy of the vehicle.
Keywords: Fuel cell vehicles, hybrid powertrain, energy management strategy, fuzzy control, particle swarm optimization
DOI: 10.3233/JIFS-189458
Journal: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 4, pp. 6205-6217, 2021
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