دوره 16، شماره 4 - ( 9-1405 )
جلد 16 شماره 4 صفحات 5223-5208 |
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Ansari Laleh A, Shojaeefard M H. Comparative Energy and Hydrogen Performance of a Fuel-Cell Electric Vehicle Across Standard Driving Cycles. , ASE. 2026; 16 (4) :5208-5223
URL: http://ase.iust.ac.ir/article-1-757-fa.html
URL: http://ase.iust.ac.ir/article-1-757-fa.html
Comparative Energy and Hydrogen Performance of a Fuel-Cell Electric Vehicle Across Standard Driving Cycles. , Automotive Science and Engineering. 1405; 16 (4) :5208-5223
چکیده: (72 مشاهده)
The performance of fuel-cell electric vehicles (FCEVs) depends not only on stack efficiency but also on the dynamic interaction among hydrogen conversion, battery buffering, and vehicle-level power demand, which is strongly shaped by the imposed driving cycle. This study develops a forward-facing, multi-domain system-level model of a Toyota-Mirai-type FCEV in Simcenter Amesim, integrating the hydrogen storage and PEM fuel-cell subsystem, balance-of-plant auxiliaries, high- and low-voltage electrical buses, traction battery, power electronics, drivetrain, and a rule-based, stack-led energy-management strategy. Three standardized driving schedules WLTC, NEDC, and UDDS were simulated under identical powertrain parameters and initial conditions (80% initial SOC, ~5.0 kg hydrogen) to isolate the effect of driving-cycle dynamics on energy flow. Results show that the fuel-cell stack consistently supplies the base-load propulsion power, while the battery absorbs all regenerative-braking energy and covers transient peaks, never allowing reverse power into the stack. Although absolute hydrogen consumption and mean stack power varied substantially across cycles (166.52 g for WLTC versus 83.34 g for NEDC), the distance-normalized hydrogen economy converged to a narrow band of 0.748–0.781 kg H2/100 km, and peak stack power remained within 31.6–37.0 kW for all cases. These findings demonstrate that the stack-led energy-management strategy achieves cycle-independent efficiency and robust component utilization, providing a validated baseline for future optimization and degradation-aware control studies.
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