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Piston design is a challenging engineering problem which involves complex physics and requires satisfying multiple performance objectives. Uncertainty in piston operating conditions and variability in piston design variables are inevitable and must be accounted for. The piston assembly can be a major source of engine mechanical friction and cold start noise, if not designed properly. In this paper, an analytical piston model is used in a deterministic and probabilistic (reliability-based) multi-objective design optimization process to obtain an optimal piston design. The model predicts piston performance in terms of scuffing, friction and noise, In order to keep the computational cost low, efficient and accurate metamodels of the piston performance metrics are used. The Pareto set of all optimal solutions is calculated allowing the designer to choose the “best” solution according to trade-offs among the multiple objectives.

A new, 1-D analytical engine thermal management tool was developed to model piston, oil and coolant temperatures in the Ford 3.5L engine family. The model includes: a detailed lubrication system, including piston oil-squirters, which accurately represents oil flow rates, pressure drops and component heat transfer rates under non-isothermal conditions; a detailed coolant system, which accurately represents coolant flow rates, pressure drops and component heat transfer rates; a turbocharger model, which includes thermal interactions with coolant, oil, intake air and exhaust gases (modeled as air), and heat transfer to the surroundings; and lumped thermal models for engine components such as block, heads, pistons, turbochargers, oil cooler and cooling tower. The model was preliminarily calibrated for the 3.5L EcoBoost™ engine, across the speed range from 1500 to 5500 rpm, using wide-open-throttle data taken from an early heat rejection study.

The integration of electric motors into ground vehicle propulsion systems requires the effective removal of heat from the motor shell. As the torque demand varies based on operating cycles, the generated heat from the motor windings and stator slots must be rejected to the surroundings to ensure electric machine reliability. In this paper, an electric motor cooling system design will be optimized for a light duty autonomous vehicle. The design variables include the motor cradle volume, the number of heat pipes, the coolant reservoir dimensions, and the heat exchanger size while the cost function represents the system weight, overall size, and performance. The imposed requirements include the required heat transfer per operating cycle (6, 9, 12kW) and vehicle size, component durability requirement, and material selection. The application of a nonlinear optimization package enabled the cooling system design to be optimized.