Applying optimal choices for real powertrain and lightweighting technology options to passenger vehicles under uncertainty

Abstract

This paper illustrates how cost-constrained optimization based on a set of real lightweighting and powertrain efficiency options can be used to guide decision-making for automotive manufacturers. The paper provides a method for answering the question posed by Original Equipment Manufacturers (OEMs): ‘given a maximum amount additional cost which can be passed on to consumers for fuel-saving technology with uncertain manufacturing cost, to what degree should it be spent on lightweighting versus powertrain efficiency improving technology’. The optimization is formulated as a 0–1 knapsack problem, and dynamic programming is used to find the global optimum technology combination at various levels of maximum up-front technology cost. This paper builds on previous work, which showed that for continuous marginal cost functions under uncertainty, a decision heuristic to either implement lightweighting technology or efficiency technology but not both under cost constraints was preferable. This work extends that result to provide more quantitative strategies for dealing with uncertainty, and finds that, despite uncertainty, optimum lightweighting and efficiency technology selections can be made for the real discrete cases studied. It is found that while the optimum efficiency technology set is highly sensitive to the up-front cost a consumer is willing to pay for future operational savings, lightweighting options are often selected preferentially to efficiency reduction measures. In the same sense, although both technologies are very sensitive to discount rate, lightweighting technologies are less sensitive. Fully hybridized vehicles emerge as a robust option, and, surprisingly, rank highly together with fully electric powertrains.

Keyword : cost, fuel consumption, models and algorithms, uncertainty, optimization, lightweighting, powertrain efficiency, strategy development