Comparative life cycle analysis of conventional and hybrid heavy-duty trucks

被引：0
作者：
Rupp M. [1 ]
Schulze S. [2 ]
Kuperjans I. [1 ]
机构：
[1] FH Aachen, NOWUM-Energy, Heinrich-Mußmann-Str. 1, Jülich
[2] FH Aachen, European Center for Sustainable Mobility (ECSM), Aachener-und-Münchener-Allee 1, Aachen
来源：
World Electric Vehicle Journal | 2018年 / 9卷 / 02期
关键词：
Environment; Heavy-duty truck; Hybrid electric vehicle; Life cycle assessment; Sustainability;
D O I：
10.3390/wevj9020033
中图分类号：
学科分类号：
摘要：
Heavy-duty trucks are one of the main contributors to greenhouse gas emissions in German traffic. Drivetrain electrification is an option to reduce tailpipe emissions by increasing energy conversion efficiency. To evaluate the vehicle's environmental impacts, it is necessary to consider the entire life cycle. In addition to the daily use, it is also necessary to include the impact of production and disposal. This study presents the comparative life cycle analysis of a parallel hybrid and a conventional heavy-duty truck in long-haul operation. Assuming a uniform vehicle glider, only the differing parts of both drivetrains are taken into account to calculate the environmental burdens of the production. The use phase is modeled by a backward simulation in MATLAB/Simulink considering a characteristic driving cycle. A break-even analysis is conducted to show at what mileage the larger CO2eq emissions due to the production of the electric drivetrain are compensated. The effect of parameter variation on the break-even mileage is investigated by a sensitivity analysis. The results of this analysis show the difference in CO2eq/t km is negative, indicating that the hybrid vehicle releases 4.34 g CO2eq/t km over a lifetime fewer emissions compared to the diesel truck. The break-even analysis also emphasizes the advantages of the electrified drivetrain, compensating the larger emissions generated during production after already a distance of 15,800 km (approx. 1.5 months of operation time). The intersection coordinates, distance, and CO2eq, strongly depend on fuel, emissions for battery production and the driving profile, which lead to nearly all parameter variations showing an increase in break-even distance. © 2018 by the authors.
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