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A probabilistic fleet analysis for energy consumption, life cycle cost and greenhouse gas emissions modelling of bus technologies

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  • Harris, Andrew
  • Soban, Danielle
  • Smyth, Beatrice M.
  • Best, Robert

Abstract

Introducing alternative bus fleet technologies requires investigation into life cycle impacts, risks and benefits. Previous modelling approaches comparatively assess individual vehicle energy demands and life cycle impacts, assuming alternative technologies can fulfil identical life cycle functions to a diesel baseline. This assumption neglects the influence that service frequency, capacity and range limitations have on daily operations and fleet and infrastructure sizing. The goal of this study was to develop a framework to investigate bus fleet operation in terms of the risk and uncertainty of an alternative drivetrain technology’s ability to mitigate life cycle costs and greenhouse gas emissions. Probabilistic simulation enabled risk and uncertainty quantification of diesel, micro-hybrid, mild-hybrid and battery-electric fleet scenarios for a UK case study. The fleet analysis approach revealed decreased potential to reduce life cycle costs and greenhouse gas emissions from battery-electric buses. Compared to a baseline single-deck diesel fleet at low risk levels, the micro-hybrid double-deck fleet delivers the largest life cycle cost savings (18.7%). The largest life cycle greenhouse gas emissions savings come from the mild-hybrid lithium-titanate single-deck fleet (20.8%). Double-deck micro and mild hybrid fleets are the most effective at saving both life cycle costs and greenhouse gas emissions. The modelling approach adds a novel probabilistic capability for making comparative fleet-wide assertions, supporting the decision-making process for implementing new sustainable fleet technologies.

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  • Harris, Andrew & Soban, Danielle & Smyth, Beatrice M. & Best, Robert, 2020. "A probabilistic fleet analysis for energy consumption, life cycle cost and greenhouse gas emissions modelling of bus technologies," Applied Energy, Elsevier, vol. 261(C).
    • Handle: RePEc:eee:appene:v:261:y:2020:i:c:s0306261919321099
    DOI: 10.1016/j.apenergy.2019.114422
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