A Novel Energy Management Strategy Considering Internal Loss and Hydrogen Consumption for Fuel Cell UAV

Hailong Sun, Rui Ma, Jian Song, Congcong Wang, Xiaoyue Chai, Zhi Feng

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

Energy management strategies (EMS) play an important role in improving system performance and fuel economy for fuel cell UA V hybrid power system. To effectively reduced the internal loss of the system and further improved the fuel economy, an energy management strategy based on model predictive control (MPC) was proposed. The internal loss and hydrogen consumption of the power system were constructed as a total energy consumption objective function. The output current of the fuel cell was controlled by solving the objective function to achieve reasonable energy distribution. Then the simulation was carried out in MATLAB/Simulink to verify the rationality and effectiveness of the proposed strategy. Compared with the equivalent consumption minimum strategy (ECMS), the result proved that the proposed strategy can significantly reduce the internal loss and hydrogen consumption of the power system. Besides, the strategy can also maintain the state of charge (SOC) of the battery and prolong the service life of the power system.

Original languageEnglish
Title of host publication2023 IEEE Transportation Electrification Conference and Expo, ITEC 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350397420
DOIs
StatePublished - 2023
Event2023 IEEE Transportation Electrification Conference and Expo, ITEC 2023 - Detroit, United States
Duration: 21 Jun 202323 Jun 2023

Publication series

Name2023 IEEE Transportation Electrification Conference and Expo, ITEC 2023

Conference

Conference2023 IEEE Transportation Electrification Conference and Expo, ITEC 2023
Country/TerritoryUnited States
CityDetroit
Period21/06/2323/06/23

Keywords

  • energy management strategy
  • Fuel Cell UA V
  • hybrid power system
  • predictive control strategy

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