离心式压缩机
气体压缩机
燃料电池
计算机科学
机械工程
材料科学
汽车工程
工程类
化学工程
作者
Nathan Peters,S. Subramanyam,Michael Bunce,Alexander H. Taylor,Pavan Naik,Jens Semmelroggen,Simon Nibler
出处
期刊:SAE technical paper series
日期:2024-04-09
卷期号:1
摘要
<div class="section abstract"><div class="htmlview paragraph">Fuel cell electric vehicles offer an attractive option for decarbonizing long-haul on-road transport. However, there are still several barriers to widespread adoption of hydrogen-fueled fuel cells for this application including system durability and total cost of ownership compared to traditional diesel engines. A primary contributor to fuel cell system costs and maintenance requirements is the air management system. It is common for heavy duty fuel cell electric vehicles to use light-duty automotive air management components which are ill-suited for the requirements of larger, long-haul vehicles. This study focuses on the development of a durable and efficient air management system for heavy duty vehicle applications as part of a cooperative research project funded by the Department of Energy’s Hydrogen and Fuel Cell Technologies Office<span class="xref"><sup>1</sup></span>.</div><div class="htmlview paragraph">The proposed air management design incorporates a novel two stage filtration system, an innovative water-lubricated bearing and electrically-assisted variable turbine geometry turbocharger, charge air cooler, and humidifier. To achieve the ambitious Department of Energy project goals for efficiency and durability, a system-level optimization approach has been employed using a semi-empirical 1D model. Design optimization of the compressor and turbine wheel geometries yielded a large compressor wheel diameter and small trim to reduce mass flow capacity, resulting in a broad efficiency map and a relatively small turbine wheel diameter with non-radial inlet blade angle suitable for the low temperature exhaust. 1D simulations of the optimized system compared to a baseline e-compressor showed a >40% reduction in required e-motor power at steady-state conditions and a >30% reduction in e-motor energy consumption in a transient cycle.</div></div>
科研通智能强力驱动
Strongly Powered by AbleSci AI