4.7 Article

Comparing alternative heavy-duty drivetrains based on GHG emissions, ownership and abatement costs: Simulations of freight routes in British Columbia

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PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.trd.2019.08.031

关键词

Heavy-duty truck; Alternative drivetrains; Well to wheel GHG emissions; Total ownership cost; GHG abatement cost; Energy consumption

资金

  1. Pacific Institute for Climate Solution (PICS), Canada

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This study quantified the well-to-wheel GHG emissions, total ownership costs and abatement cost for 16 different heavy-duty truck (HDT) drivetrains, including those powered by natural gas, electricity, and hydrogen. Using the case of British Columbia, Canada, we employed GPS activity data for almost 1600 container tractor-trailer trucks to extract six distinct drive cycles. Monte Carlo simulation was used to evaluate the range of uncertainties in several critical model parameters. Results vary widely across drivetrains and across drive cycles. Using hydroelectricity, the battery electric catenary and pure battery electric drivetrains emit the lowest GHG per tonne km on short and long haul cycles respectively. On average over the variety of drive cycles, the plug-in parallel-hybrid diesel has the lowest total ownership cost (including infrastructure costs) for both short and long haul operations. We divided ownership costs by GHG reductions to calculate abatement costs ($/tonne). Plug-in parallel hybrid diesels have the lowest abatement cost, with negative costs on most drive cycles either using diesel or bio-diesel. Comparing drivetrains with their maximum cargo loads, plug-in parallel hybrid fuel cell and conventional diesel have the highest capacity on short and long haul routes respectively. Our analyses can help policymakers, industry and other stakeholders to compare different pathways to reduce GHG emissions in freight, including tradeoffs between the magnitude and cost of emissions reduction. Our conclusion highlights the implications and challenges of drivetrain choice, as well as recommending some areas for further improving the analysis.

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