Parameter Matching and Multi-objective Optimization of Fuel Cell Hybrid System

LI Qi; MENG Xiang; CHEN Weirong; ZHANG Guorui

doi:10.3969/j.issn.0258-2724.20170117

Volume 54 Issue 5

Oct. 2019

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Journal of Southwest Jiaotong University > 2019 > 54(5): 1079-1086.

ZHANG Wenbai, LIN Guobin, KANG Jinsong, ZHAO Yuanzhe, LIAO Zhiming. Sensorless Control Method of High-Frequency Injection for Long-Stator Synchronous Motor of Maglev Trains Considering Phase Shift Compensation[J]. Journal of Southwest Jiaotong University, 2025, 60(4): 1032-1041. doi: 10.3969/j.issn.0258-2724.20240310

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LI Qi, MENG Xiang, CHEN Weirong, ZHANG Guorui. Parameter Matching and Multi-objective Optimization of Fuel Cell Hybrid System[J]. Journal of Southwest Jiaotong University, 2019, 54(5): 1079-1086. doi: 10.3969/j.issn.0258-2724.20170117

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Parameter Matching and Multi-objective Optimization of Fuel Cell Hybrid System

doi: 10.3969/j.issn.0258-2724.20170117

Received Date: 22 Feb 2017
Rev Recd Date: 21 Apr 2018

Available Online: 11 Jul 2018

Publish Date: 01 Oct 2019

Abstract

Abstract

With the requirements of tram dynamic performance fulfilled, the weight and volume of the fuel cell hybrid system are optimized by the parameter matching method. Firstly, the vehicle dynamic model is built, and three peak powers are obtained, which corresponds three operational modes of trams including startup acceleration, climbing with uniform speeds and maximum speed operation. Secondly, based on the traditional method, the maximum peak power is set as an objective, and the supercapacitor-battery propotion is analyzed. Then, based on the traditional method, the improved particle swarm optimization (IPSO) is used to calculate the multi-objective optimal parameters in terms of the weight and volume of the system. Finally, the traditional method and multi-objective optimization method are compared and analyzed. The results show that both methods can meet the tram power demand. According to the multi-objective optimization method, the fuel cell hybrid tram is configured with two sets of 150 kW fuel cells, 124 supercapacitors (48 V, 165 F) and 337 power batteries (3.7 V, 9 A•h) . The tram can accelerate up to 70 km/h in 32.24 s, the maximum climbing capacity is 85.5‰, and the continuous climbing ability is 732.5 m. Compared with the traditional method, the optimization rate of its weight and volume reaches to 83.075% and 86.696% respectively.
- fuel cell hybrid tram,
- hybrid power system,
- parameter matching,
- multi-objective optimization,
- improved fast particle swarm optimization

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References(13)

References

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