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Volume 55 Issue 5
Oct.  2020
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Article Contents
Journal of Southwest Jiaotong University  > 2020  >  55(5): 903-911.
CHEN Weirong, HU Binbin, LI Qi, YAN Yu, MENG Xiang. Energy Management Method for Hybrid Electric Tram Based on Dynamic Programming Algorithm[J]. Journal of Southwest Jiaotong University, 2020, 55(5): 903-911. doi: 10.3969/j.issn.0258-2724.20180470
Citation: CHEN Weirong, HU Binbin, LI Qi, YAN Yu, MENG Xiang. Energy Management Method for Hybrid Electric Tram Based on Dynamic Programming Algorithm[J]. Journal of Southwest Jiaotong University, 2020, 55(5): 903-911. doi: 10.3969/j.issn.0258-2724.20180470
shu

Energy Management Method for Hybrid Electric Tram Based on Dynamic Programming Algorithm

doi: 10.3969/j.issn.0258-2724.20180470
  • Received Date: 07 Jun 2018
  • Rev Recd Date: 10 Oct 2018
    • Available Online: 15 Oct 2018
  • Publish Date: 01 Oct 2020
    Abstract
  • Aiming at the errors accumulation of traditional dynamic programming algorithm in energy distribution of the fuel cell hybrid electric system, an energy management method for the fuel cell hybrid electric tram was proposed based on improved dynamic programming algorithm, which aims to further improve the durability and fuel economy of the fuel cell hybrid electric tram. The improved dynamic programming algorithm adjusted the state transition equation based on the traditional dynamic programming by discretizing the system state quantities, which avoided the errors accumulation caused by interpolation calculation. At the same time, the equivalent hydrogen consumption of the system, the constraint of the state of charge (SOC) and the durability problems brought from loading and unloading of fuel cells were considered as optimization objectives to constitute a weighted penalty function, which made the system could take into account durability while achieved better fuel economy. The proposed management method was compared with power following and traditional dynamic programming. The results show that the proposed method reduces the final state SOC by 13.3% and the fuel economy by 78% compared with the power-following method. Moreover, the proposed method improves the fuel economy by 3.5%, and both the SOC variation range and the load-carrying condition of the fuel cell have significantly improved compared with the traditional dynamic programming algorithm.

     

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