Multi-Criteria Decision Analysis of Heuristically Designed Light-Duty Vehicles Today and in 2035

Paper #:
  • 2011-01-0727

Published:
  • 2011-04-12
Citation:
Wilhelm, E. and Wokaun, A., "Multi-Criteria Decision Analysis of Heuristically Designed Light-Duty Vehicles Today and in 2035," SAE Technical Paper 2011-01-0727, 2011, https://doi.org/10.4271/2011-01-0727.
Pages:
14
Abstract:
Evaluating options for reducing the environmental impact of light duty vehicles is complicated by the number of technologies being actively researched. In this work, various alternative transportation fuels such as ethanol, biodiesel, hydrogen, and electricity as well as advanced materials and drive-train concepts are analyzed for their environmental benefits compared to conventional diesel and gasoline vehicles. To objectively evaluate the impact of technology and fuel options on various stakeholder criteria under uncertainty (e.g. fuel and technology costs), it is necessary to consider how the technology may be applied to a broad set of vehicle designs. The heuristic vehicle design method uses rules from first principles and engineering practice to ensure vehicles in the design set (virtual fleet) are composed of self-consistent technology options. The resulting cost, performance, environmental, safety, and utility criteria are used as indicators for multi-criteria decision analysis (MCDA) based on responses from two general and one expert surveys on individual preferences for the various indicators. A sensitivity analysis on how stakeholder preference influences the optimal technology chosen by the MCDA algorithm is also performed to highlight the advantage of this approach in handling uncertainty by finding robust alternatives. The conclusions provide insight into the acceptance of present and future vehicle fuel and powertrain options for light duty transportation, and examine the underlying stakeholder preferences which need to be reconciled in order to reduce the environmental burden of personal transportation, with emphasis on finding technology that is robust across many stakeholder preference weightings.
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