sensitivity analyses for cross-coupled parameters in automotive powertrain optimization

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ID: 192654
2014
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Abstract
When vehicle manufacturers are developing new hybrid and electric vehicles, modeling and simulation are frequently used to predict the performance of the new vehicles from an early stage in the product lifecycle. Typically, models are used to predict the range, performance and energy consumption of their future planned production vehicle; they also allow the designer to optimize a vehicle’s configuration. Another use for the models is in performing sensitivity analysis, which helps us understand which parameters have the most influence on model predictions and real-world behaviors. There are various techniques for sensitivity analysis, some are numerical, but the greatest insights are obtained analytically with sensitivity defined in terms of partial derivatives. Existing methods in the literature give us a useful, quantified measure of parameter sensitivity, a first-order effect, but they do not consider second-order effects. Second-order effects could give us additional insights: for example, a first order analysis might tell us that a limiting factor is the efficiency of the vehicle’s prime-mover; our new second order analysis will tell us how quickly the efficiency of the powertrain will become of greater significance. In this paper, we develop a method based on formal optimization mathematics for rapid second-order sensitivity analyses and illustrate these through a case study on a C-segment electric vehicle.
Reference Key
othaganont2014energiessensitivity Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Pongpun Othaganont;Francis Assadian;Daniel Auger
Journal acs combinatorial science
Year 2014
DOI
10.3390/en7063733
URL
http://www.mdpi.com/1996-1073/7/6/3733
Keywords
sensitivity analysis

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