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Research Papers: Design Automation

Probabilistic Graphical Modeling of Use Stage Energy Consumption: A Lightweight Vehicle Example1

[+] Author and Article Information
Cassandra Telenko

Mechanical Engineering Department,
Massachusetts Institute of Technology,
Cambridge, MA 02139;
Engineering Product Development Pillar,
Sinagore University of Technology and Design,
Singapore 130036
e-mail: cassandra@utexas.edu

Carolyn C. Seepersad

Mechanical Engineering Department,
The University of Texas at Austin,
Austin, TX 78712

This paper is a revised version of Paper Number DETC2013-13113, published in the 2013 ASME IDETC Design Automation Conference.

2Corresponding author.

Contributed by the Design Automation Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received September 17, 2013; final manuscript received June 19, 2014; published online July 31, 2014. Assoc. Editor: Bernard Yannou.

J. Mech. Des 136(10), 101403 (Jul 31, 2014) (11 pages) Paper No: MD-13-1411; doi: 10.1115/1.4027983 History: Received September 17, 2013; Revised June 19, 2014

Although energy consumption during product use can lead to significant environmental impacts, the relationship between a product's usage context and its environmental performance is rarely considered in design evaluations. Traditional analyses rely on broad, average usage conditions and do not differentiate between contexts for which design decisions are highly beneficial and contexts for which the same decision may offer limited benefits or even penalties in terms of environmental performance. In contrast, probabilistic graphical models (PGMs) provide the capability of modeling usage contexts as variable factors. This research demonstrates a method for representing the usage context as a PGM and illustrates it with a lightweight vehicle design example. Factors such as driver behavior, alternative driving schedules, and residential density are connected by conditional probability distributions derived from publicly available data sources. Unique scenarios are then defined as sets of conditions on these factors to provide insight into sources of variability in lifetime energy use. The vehicle example demonstrates that implementation of realistic usage scenarios via a PGM can provide a much higher fidelity investigation of use stage energy savings than commonly found in the literature and that, even in the case of a universally beneficial design decisions, distinct scenarios can have significantly different implications for the effectiveness of lightweight vehicle designs.

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References

Figures

Grahic Jump Location
Fig. 1

Example PGM of P(Y,X)

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Fig. 2

Flows of steel LCI

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Fig. 3

Flows of aluminum LCI

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Fig. 5

Fuel saved under specified conditions

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Fig. 6

Energy saved per km by scenario

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Fig. 7

Use stage energy savings by scenario

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