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

Abstract

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.

Perspective on the hydrogen economy as a pathway to reach net-zero CO2emissions in Europe

“…74,75 On the other hand, FCEV seem to be the most promising ''clean'' option in the heavy-duty road vehicles and (mid-range) maritime shipping sectors due to specific use profiles and associated requirements in terms of range, fuelling time and lifetime. 76,77 An overview of ICEV/FCEV/ BEV vehicles is shown in Table 2.…”

Life Cycle Assessment of Battery Electric and Internal Combustion Engine Vehicles Considering the Impact of Electricity Generation Mix: A Case Study in China

“…Tagliaferri et al [32] demonstrated that ICEVs indeed produce more GHG emissions than BEVs in the use phase, however, BEVs produce a double amount of GHG compared to ICEVs in the manufacturing phase. Sacchi et al [33] showed that the premise for the battery to reduce GHG emissions is a dramatic reduction of the GHG intensity of the electricity used for charging. Therefore, the electricity generation mix and the electricity carbon intensity need to be considered when evaluating the carbon emissions of BEVs [34,35].…”

Making the use of scenarios in LCA easier: the superstructure approach

“…It also improves reproducibility and transparency and reduces the effort to generate updates when new versions of the underlying models become available. This work is currently being continued in the context of the PREMISE, 2 a python package that aims at streamlining the approach to produce scenario databases for prospective LCA (Sacchi et al submitted), and which has already been applied in a number of studies (Pizzol et al 2021;Sacchi et al 2021). Despite this progress, there are still important challenges to be met for enabling a more widespread use of future background scenarios in LCA (see also our discussion).…”