Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor | Department of Electrical and Electronic Engineering | en_US |
| dc.creator | Yu, Jianing | - |
| dc.identifier.uri | https://theses.lib.polyu.edu.hk/handle/200/14455 | - |
| dc.language | English | en_US |
| dc.publisher | Hong Kong Polytechnic University | en_US |
| dc.rights | All rights reserved | en_US |
| dc.title | Energy management strategy for fuel cell vehicles based on fuzzy control | en_US |
| dcterms.abstract | This study gives a fuzzy control strategy on account of sparrow search algorithm optimization. The purpose of this research is to improve the fuel economy of fuel cell electric vehicles. At the same time, the research in the essay can enable vehicles to adapt to more complex driving conditions. Under the premise of ensuring vehicle power, this strategy can control the battery SOC within the working range, so that the system equivalent fuel consumption per 100 kilometers is small. This study uses fuel cell electric vehicles as a reference object to match power parameters and establish a fuzzy control strategy model. The fuzzy control strategy model is built using Matlab/Simulink software, and a joint simulation analysis is performed under FTP75 conditions. | en_US |
| dcterms.abstract | This strategy dynamically adjusts the output power of fuel cells as well as the lithium batteries. This strategy has a good effect on decreasing the hydrogen consumption of the whole vehicle and suppressing the start-stop frequency of fuel cells. At the same time, this strategy can achieve a balance between reducing the large current impact of lithium batteries. Compared with traditional PID and Sigmoid function control strategies, hydrogen consumption is reduced by 2.8%. | en_US |
| dcterms.abstract | Further targeting the subjective experience dependence problem of fuzzy control, the sparrow search algorithm is introduced to iteratively optimize the membership function, and the objective correction of the rule base is achieved with the equivalent fuel consumption as the performance indicator, so that the energy consumption of the optimized dual fuzzy control is reduced by another 1.4%. | en_US |
| dcterms.abstract | The research results show that the fusion optimization of intelligent algorithms and traditional fuzzy control can improve the adaptability and economy of energy distribution effectively, and provide a solution with both theoretical innovation and engineering practicality for the coordinated control of the power system of fuel cell vehicles. | en_US |
| dcterms.extent | 1 volume (unpaged) : color illustrations | en_US |
| dcterms.isPartOf | PolyU Electronic Theses | en_US |
| dcterms.issued | 2025 | en_US |
| dcterms.educationalLevel | M.Sc. | en_US |
| dcterms.educationalLevel | All Master | en_US |
| dcterms.accessRights | restricted access | en_US |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 8884.pdf | For All Users (off-campus access for PolyU Staff & Students only) | 3.7 MB | Adobe PDF | View/Open |
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