Hybrid propulsion systems are one of the critical technologies on the roadmap to future ultra-efficient trucks. While there is a significant body of work related to hybrid passenger cars and light commercial trucks, there are many open issues related to hybridisation of heavier trucks intended for both on- and off-road use. This work addresses those questions through a systematic analysis of the proposed parallel hydraulic hybrid powertrain for the Family of Medium Tactical Vehicles (FMTV). A representative duty cycle for the FMTV is generated based on information about the typical vehicle mission. A methodology for sequential optimisation of hybrid propulsion and power management systems is applied to a hydraulic hybrid configuration with post-transmission motor location. This analysis is critical in evaluating the fuel economy and mobility potential of the hybrid propulsion system, as well as enhancing our understanding of the phenomena leading to predicted fuel economy values.

Analytical target cascading (ATC) is a methodology for hierarchical multilevel system design optimization. In previous work, the deterministic ATC formulation was extended to account for random variables represented by expected values to be matched among subproblems and thus ensure design consistency. In this work, the probabilistic formulation is augmented to allow the introduction and matching of additional probabilistic characteristics. A particular probabilistic analytical target cascading (PATC) formulation is proposed that matches the first two moments of interrelated responses and linking variables. Several implementation issues are addressed, including representation of probabilistic design targets, matching responses and linking variables under uncertainty, and coordination strategies. Analytical and simulation-based optimal design examples are used to illustrate the new formulation. The accuracy of the proposed PATC formulation is demonstrated by comparing PATC results to those obtained using a probabilistic all-in-one formulation.

The analytical target cascading process is applied to the redesign of a U.S. class VI truck. Necessary simulation and analysis models for predicting vehicle dynamics, powertrain, and suspension behaviour are developed. Vehicle design targets that include improved fuel economy, ride quality, driveability, and performance metrics are translated into system design specifications, and a consistent final design is obtained. Trade-offs between conflicting targets are identified. The study illustrates how the analytical target cascading process can reduce vehicle design cycle time while ensuring physical prototype matching, and how costly design iterations late in the development process can be avoided.

Computer modeling and simulation are the cornerstones of product design and development in the automotive industry. Computer-aided engineering tools have improved to the extent that virtual testing may lead to significant reduction in prototype building and testing of vehicle designs. In order to make this a reality, we need to assess our confidence in the predictive capabilities of simulation models. As a first step in this direction, this paper deals with developing measures and a metric to compare time histories obtained from simulation model outputs and experimental tests. The focus of the work is on vehicle safety applications. We restrict attention to quantifying discrepancy between time histories as the latter constitute the predominant form of responses of interest in vehicle safety considerations. First, we evaluate popular measures used to quantify discrepancy between time histories in fields such as statistics, computational mechanics, signal processing, and data mining. Three independent error measures are proposed for vehicle safety applications, associated with three physically meaningful characteristics (phase, magnitude, and slope), which utilize norms, cross-correlation measures, and algorithms such as dynamic time warping to quantify discrepancies. A combined use of these three measures can serve as a metric that encapsulates the important aspects of time history comparison. It is also shown how these measures can be used in conjunction with ratings from subject matter experts to build regression-based validation metrics.

This paper presents a methodology for design optimization of hierarchically decomposed systems under uncertainty. We propose an extended, probabilistic version of the deterministic analytical target cascading (ATC) formulation by treating uncertain quantities as random variables and posing probabilistic design constraints. A bottom-to-top coordination strategy is used for the ATC process. Given that first-order approximations may introduce unacceptably large errors, we use a technique based on the advanced mean value method to estimate uncertainty propagation through the multilevel hierarchy of elements that comprise the decomposed system. A simple yet illustrative hierarchical bilevel engine design problem is used to demonstrate the proposed methodology. The results confirm the applicability of the proposed probabilistic ATC formulation and the accuracy of the uncertainty propagation technique.