Influence of Longitudinal Wind on Hydrogen Leakage and Hydrogen Concentration Sensor Layout of Fuel Cell Vehicles

Xingmao Wang, Fengyan Yi (), Qingqing Su, Jiaming Zhou (), Yan Sun, Wei Guo and Xing Shu
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Xingmao Wang: School of Automotive Engineering, Shandong Jiaotong University, Jinan 250357, China
Fengyan Yi: School of Automotive Engineering, Shandong Jiaotong University, Jinan 250357, China
Qingqing Su: School of Automotive Engineering, Shandong Jiaotong University, Jinan 250357, China
Jiaming Zhou: School of Intelligent Manufacturing, Weifang University of Science and Technology, Weifang 262700, China
Yan Sun: School of Automotive Engineering, Shandong Jiaotong University, Jinan 250357, China
Wei Guo: School of Automotive Engineering, Shandong Jiaotong University, Jinan 250357, China
Xing Shu: School of Automotive Engineering, Shandong Jiaotong University, Jinan 250357, China

Sustainability, 2023, vol. 15, issue 13, 1-18

Abstract: Hydrogen has the physical and chemical characteristics of being flammable, explosive and prone to leakage, and its safety is the main issue faced by the promotion of hydrogen as an energy source. The most common scene in vehicle application is the longitudinal wind generated by driving, and the original position of hydrogen concentration sensors (HCSs) did not consider the influence of longitudinal wind on the hydrogen leakage trajectory. In this paper, the computational fluid dynamics (CFD) software STAR CCM 2021.1 is used to simulate the hydrogen leakage and diffusion trajectories of fuel cell vehicles (FCVs) at five different leakage locations the longitudinal wind speeds of 0 km/h, 37.18 km/h and 114 km/h, and it is concluded that longitudinal wind prolongs the diffusion time of hydrogen to the headspace and reduces the coverage area of hydrogen in the headspace with a decrease of 81.35%. In order to achieve a good detection effect of fuel cell vehicles within the longitudinal wind scene, based on the simulated hydrogen concentration–time matrix, the scene clustering method based on vector similarity evaluation was used to reduce the leakage scene set by 33%. Then, the layout position of HCSs was optimized according to the proposed multi-scene full coverage response time minimization model, and the response time was reduced from 5 s to 1 s.

Keywords: fuel cell vehicles; longitudinal wind; scene clustering; sensor layout optimization (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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