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Life Cycle Assessment of Fuel Cell Vehicles - Dealing with Uncertainties


Research Report

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Suggested Citation:
Contadini, José F. (2002) Life Cycle Assessment of Fuel Cell Vehicles - Dealing with Uncertainties. Institute of Transportation Studies, University of California, Davis, Research Report UCD-ITS-RR-02-07

Life cycle assessment (LCA), or "well to wheels" in transportation terms, involves some subjectivity and uncertainty, especially with new technologies and future scenarios. To analyze lifecycle impacts of future fuel cell vehicles and fuels, I developed the Fuel Upstream Energy and Emission Model (FUEEM). The FUEEM project pioneered two specific new ways to incorporate and propagate uncertainty within an LCA analysis. First, the model uses probabilistic curves generated by experts as inputs and then employs Monte Carlo simulation techniques to propagate these uncertainties throughout the full chain of fuel production and use. Second, the FUEEM process explicitly involves the interested parties in the entire analysis process, not only in the critical final review phase.

To demonstrate the FUEEM process, an analysis has been made for the use of three different fuel cell vehicle technologies (direct hydrogen, indirect methanol, and indirect hydrocarbon) in 2010 within the South Coast Air Basin (SCAB) of California (Los Angeles). The analysis covered topics such as the requirement of non-renewable energy sources, emissions of CO2 and other greenhouse gases, and emissions of several criteria pollutants generated within SCAB and within other regions. The results obtained from this example show that the hydrogen option has the potential to have the most efficient energy life cycle for the SCAB, followed by the methanol and finally by the Fisher-Tropsch naphtha option. A similar pattern is observed for the greenhouse gas emissions. The results showing criteria pollutants emitted within SCAB highlight the importance of having a flexible model that is responsive to local considerations. This dissertation demonstrates that explicit recognition and quantitative analysis of the inherent uncertainty in the LCA process generates richer information, explains many of the discrepancies between results of previous studies, and enhances the robustness and credibility of LCA analyses.
Ph.D. Dissertation