Balancing the Speed and Fidelity of Automotive Powertrain Models Through Surrogation

Abstract

The growing use of model-based engineering for powertrain design and validation imposes conflicting modeling requirements. Vehicle powertrain models must be accurate enough for design optimization, and fast enough for real-time verification on hardware-in-the-loop platforms. Because these two requirements conflict, one finds two families of powertrain models today: high-fidelity models and real-time models. Model surrogation can bridge the gap between these two families by extracting powertrain models accurate enough for optimization and fast enough for real time. This paper systematically derives surrogate torque converter, clutch friction, and clutch hydraulics models. Using these models, the simulation speeds for two automatic transmissions improve significantly without appreciable loss of accuracy.