Search Results

An important benefit in adopting multi-mode hybrid powertrains is the possibility of downsizing the electric motor/generator (M/G) units. This paper discusses design optimization of M/G full-load operation envelopes. A three-dimensional operation space analysis is introduced and the full-load speed-torque envelopes of M/G units appear as constraints in the powertrain operation space. The analysis shows how the change in the M/G characteristics affects the powertrain full-load performance and how the M/G units can be downsized by combining multiple modes. When the acceleration performance of a two-mode hybrid vehicle is estimated using this analysis method, the trade-off between enhancing performance and reducing M/G full-load capacities is formulated as an optimization problem. The results show significant improvement in dynamic performance at reduced M/G cost.

Increasing functionality of electronic control units has enhanced our ability to control engine operation utilizing calibration static maps that provide the values of several controllable variables. State-of-the-art simulation-based calibration methods permit the development of these maps with respect to extensive steady-state and limited transient operation of particular driving cycles. However, each individual driving style is different and rarely meets those test conditions. An alternative approach was recently implemented that considers the derivation of these maps while the engine is running the vehicle. In this approach, a self-learning controller selects in real time the optimum values of the controllable variables for the sequences of engine operating point transitions, corresponding to the driver's driving style.

Existing methods for design optimization under uncertainty assume that a high level of information is available, typically in the form of data. In reality, however, insufficient data prevents correct inference of probability distributions, membership functions, or interval ranges. In this article we use an engine design example to show that optimal design decisions and reliability estimations depend strongly on uncertainty characterization. We contrast the reliability-based optimal designs to the ones obtained using worst-case optimization, and ask the question of how to obtain non-conservative designs with incomplete uncertainty information. We propose an answer to this question through the use of Bayesian statistics. We estimate the truck's engine reliability based only on available samples, and demonstrate that the accuracy of our estimates increases as more samples become available.

This paper investigates the effects of manufacturing variations in fuel injectors on the engine performance with emphasis on emissions. The variations are taken into consideration within a Reliability-Based Design Optimization (RBDO) framework. A reduced version of Multi-Zone Diesel engine Simulation (MZDS), MZDS-lite, is used to enable the optimization study. The numerical noise of MZDS-lite prohibits the use of gradient-based optimization methods. Therefore, surrogate models are developed to filter out the noise and to reduce computational cost. Three multi-objective optimization problems are formulated, solved and compared: deterministic optimization using MZDS-lite, deterministic optimization using surrogate models and RBDO using surrogate models. The obtained results confirm that manufacturing variation effects must be taken into account in the early product development stages.

The link between manufacturing process and product performance is studied in order to construct analytical, quantifiable criteria for the introduction of new engine technologies and processes. Cost associated with a new process must be balanced against increases in engine performance and thus demand for the particular vehicle. In this work, the effect of the Abrasive Flow Machining (AFM) technique on surface roughness is characterized through measurements of specimens, and a predictive engine simulation is used to quantify performance gains due to the new surface finish. Subsequently, economic cost-benefit analysis is used to evaluate manufacturing decisions based on their impact on firm's profitability. A demonstration study examines the use of AFM for finishing the inner surfaces of intake manifolds for two engines, one installed in a compact car and the other in an SUV.

Developing a new technology requires decision-makers to understand the technology's implications on an organization's objectives, which depend on user needs targeted by the technology. If these needs are common between two organizations, collaboration could result in more efficient technology development. For hybrid truck design, both commercial manufacturers and the military have similar performance needs. As the new technology penetrates the truck market, the commercial enterprise must quantify how the hybrid's superior fuel efficiency will impact consumer purchasing and, thus, future enterprise profits. The Army is also interested in hybrid technology as it continues its transformation to a more fuel-efficient force. Despite having different objectives, maximizing profit and battlefield performance, respectively, the commercial enterprise and Army can take advantage of their mutual needs.

The selection process of key engine design variables to maximize peak power subject to fuel economy and packaging objectives is formulated as an optimization problem readily solved with nonlinear programming. The merit of this approach lies not in finding a single optimal engine, but in identifying a family of optimal designs dependent on parameter changes in the constraint set. Sensitivity analysis of the optimum to packaging parameters, fuel economy parameters, and manufacturing parameters is presented and discussed in the context of product development decisions.