Impact of Uncertainty Quantification on Design Decisions for a Hydraulic-Hybrid Powertrain Engine

The method for solving an optimal design problem under uncertainty depends on how the latter is quantified. When sufficient information is available the popular probabilistic approach can (and should) be adopted. In reality however, we often do not have sufficient data to infer appropriate probability distributions for the uncertain quantities modeled as random variables. The amount of available information about the uncertain quantities may be limited to ranges of values (intervals). In this case, the interval analysis approach can be employed to reformulate and solve the optimal design problem. In this study, we use both approaches to solve an engine design optimization problem that considers fuel economy and acceleration performance of a medium-sized truck with a hydraulic-hybrid powertrain. We then contrast the obtained results and comment on the characteristics and features of the two approaches. We also demonstrate an extension of the interval analysis approach to multilevel systems using a simple yet illustrative engine-related example.