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Time to torque (TTT) is a quantity used to measure the transient torque response of turbocharged engines. It is referred as the time duration from an idle-to-full step torque command to the time when 95% of maximum torque is achieved. In this work, we seek to control multiple engine actuators in a collaborative way such that the TTT is minimized. We pose the TTT minimization problem as an optimization problem by parameterizing each engine actuator’s transient trajectory as Fourier series, followed by minimizing proper cost function with the optimization of those Fourier coefficients. We first investigate the problem in CAE environment by constructing an optimization framework that integrates high-fidelity GT (Gamma Technology) POWER engine model and engine actuators’ Simulink model into ModeFrontier computation platform. We conduct simulation optimization study on two different turbocharged engines under this framework with evolutionary computation algorithms.

Automotive cruise control systems are used to automatically maintain the speed of a vehicle at a desired speed set-point. It has been shown that fuel economy while in cruise control can be improved using advanced control methods. The objective of this paper is to validate an Adaptive Nonlinear Model Predictive Controller (ANLMPC) implemented in a vehicle equiped with standard production Powertrain Control Module (PCM). Application and analysis of Model Predictive Control utilizing road grade preview information has been reported by many authors, namely for commercial vehicles. The authors reported simulations and application of linear and nonlinear MPC based on models with fixed parameters, which may lead to inaccurate results in the real world driving conditions. The significant noise factors are namely vehicle mass, actual weather conditions, fuel type, etc.