Does Size Matter? The Influence of Size, Load Factor, Range Autonomy, and Application Type on the Life Cycle Assessment of Current and Future Medium- and Heavy-Duty Vehicles

The transparent, flexible, and open-source
Python library carculator_truck is introduced to
perform the life cycle
assessment of a series of medium- and heavy-duty trucks across different
powertrain types, size classes, fuel pathways, and years in a European
context. Unsurprisingly, greenhouse gas emissions per ton-km reduce
as size and load factor increase. By 2040, battery and fuel cell electric
trucks appear to be promising options to reduce greenhouse gas emissions
per ton-km on long distance segments, even where the required range
autonomy is high. This requires that various conditions are met, such
as improvements at the energy storage level and a drastic reduction
of the greenhouse gas intensity of the electricity used for battery
charging and hydrogen production. Meanwhile, these options may be
considered for urban and regional applications, where they have a
competitive advantage thanks to their superior engine efficiency.
Finally, these alternative options will have to compete against more
mature combustion-based technologies which, despite lower drivetrain
efficiencies, are expected to reduce their exhaust emissions via engine
improvements, hybridization of their powertrain, as well as the use
of biomass-based and synthetic fuels.

本研究介绍了一款透明、灵活且开源的Python库carculator_truck，用于在欧洲场景下，针对不同动力总成（Powertrain）类型、尺寸级别、燃料路径及生产年份的一系列中重型卡车开展全生命周期评估（Life Cycle Assessment）。不出所料，每吨公里的温室气体排放量会随着车辆尺寸与载荷率的提升而降低。到2040年，电池电动卡车与燃料电池电动卡车似乎是降低长途运输场景下每吨公里温室气体排放量的极具前景的方案，即便在高续航里程需求的场景中亦是如此。而要达成这一目标，需满足多项条件：例如提升储能技术水平，以及大幅降低电池充电与制氢环节用电的温室气体排放强度。与此同时，这两类电动卡车方案也可应用于城市及区域运输场景，凭借其优异的发动机效率具备竞争优势。最终，这些替代动力方案还需与更为成熟的内燃机动力技术展开竞争：尽管后者的动力总成效率较低，但可通过发动机优化、动力总成混动化以及使用生物质基燃料与合成燃料来降低尾气排放。