Net Hydrogen Consumption Minimization of Fuel Cell Hybrid Trains Using a Time-Based Co-Optimization Model

Guangzhao Meng, Chaoxian Wu, Bolun Zhang, Fei Xue and Shaofeng Lu
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Guangzhao Meng: Shien-Ming Wu School of Intelligent Engineering, South China University of Technology, Guangzhou 511442, China
Chaoxian Wu: School of Systems Science and Engineering, Sun Yat-sen University, Guangzhou 510006, China
Bolun Zhang: Shien-Ming Wu School of Intelligent Engineering, South China University of Technology, Guangzhou 511442, China
Fei Xue: School of Advanced Technology, Xi’an Jiaotong-Liverpool University, Suzhou 215123, China
Shaofeng Lu: Shien-Ming Wu School of Intelligent Engineering, South China University of Technology, Guangzhou 511442, China

Energies, 2022, vol. 15, issue 8, 1-21

Abstract: With increasing concerns on transportation decarbonization, fuel cell hybrid trains (FCHTs) attract many attentions due to their zero carbon emissions during operation. Since fuel cells alone cannot recover the regenerative braking energy (RBE), energy storage devices (ESDs) are commonly deployed for the recovery of RBE and provide extra traction power to improve the energy efficiency. This paper aims to minimize the net hydrogen consumption (NHC) by co-optimizing both train speed trajectory and onboard energy management using a time-based mixed integer linear programming (MILP) model. In the case with the constraints of speed limits and gradients, the NHC of co-optimization reduces by 6.4% compared to the result obtained by the sequential optimization, which optimizes train control strategies first and then the energy management. Additionally, the relationship between NHC and employed ESD capacity is studied and it is found that with the increase of ESD capacity, the NHC can be reduced by up to 30% in a typical route in urban railway transit. The study shows that ESDs play an important role for FCHTs in reducing NHC, and the proposed time-based co-optimization model can maximize the energy-saving benefits for such emerging traction systems with hybrid energy sources, including both fuel cells and ESD.

Keywords: co-optimization; energy-efficient train control; optimal train control; energy management; energy storage devices; fuel-cell hybrid trains; mixed integer linear programming (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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