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The automotive industry is subject to stringent regulations in emissions and growing customer demands for better fuel consumption and vehicle performance. Engine calibration, a process that optimizes engine performance by tuning engine controls (actuators), becomes challenging nowadays due to significant increase of complexity of modern engines. The traditional sweep-based engine calibration method is no longer sustainable. To tackle the challenge, this work considers two powerful global optimization methods: genetic algorithm (GA) and Bayesian optimization for steady-state engine calibration for single speed-load point. GA is a branch of meta-heuristic methods that has shown a great potential on solving difficult problems in automotive engineering. Bayesian optimization is an efficient global optimization method that solves problems with computationally expensive testing such as hyperparameter tuning in deep neural network (DNN), engine testing, etc.

Air-to-fuel (A/F) ratio is the mass ratio of the air-to-fuel mixture trapped inside a cylinder before combustion begins, and it affects engine emissions, fuel economy, and other performances. Using an A/F ratio and dual-fuel ratio control oriented engine model, a multi-input-multi-output (MIMO) sliding mode control scheme is used to simultaneously control the mass flow rate of both port fuel injection (PFI) and direct injection (DI) systems. The control target is to regulate the A/F ratio at a desired level (e.g., at stoichiometric) and fuel ratio (ratio of PFI fueling vs. total fueling) to any desired level between zero and one. A MIMO sliding mode controller was designed with guaranteed stability to drive the system A/F and fuel ratios to the desired target under various air flow disturbances.

Electro-pneumatic valve actuators are used to eliminate the cam shaft of a traditional internal combustion engine. They are used to control the opening timing, duration, and lift of both intake and exhaust valves. A physics based nonlinear mathematical model called the level one model was built using Newton's law, mass conservation and thermodynamic principles. A control oriented model, the level two model, was created by partially linearizing the level one model for model reference parameter identification. This model reduces computational throughput and enables real-time implementation. A model reference adaptive control system was used to identify the nonlinear parameters that were needed for generating a feedforward control signal. The closed-loop valve lift tracking, valve opening and closing timing control strategies were proposed.

Based on our error budget analysis, the urea SCR aftertreatment system is uncontrollable under EPA 2007-emission level without an effective closed-loop control strategy. The objective of the closed-loop control is to improve transient response as well as reduce the steady state control error. But the inherent large dead time in the urea SCR aftertreatment system makes the closed-loop control a challenge. In this paper, an innovative closed-loop control architecture is introduced, which combines model-based feedforward control with variable gain-scheduling feedback control. Transient response is improved with the inverse-dynamic feedforward control and the variable-gain closed-loop control. The steady-state response is improved with the closed-loop control. Based on this new strategy, a controller is designed and validated under the simulation and test cell environment. Comparison with the baseline open-loop controller is also conducted. Finally, some conclusions are presented.

Maximum Brake Torque (MBT) timing for an internal combustion engine is the minimum advance of spark timing for best torque. Traditionally, MBT timing is an open loop feedforward control whose values are experimentally determined by conducting spark sweeps at different speed, load points and at different environmental operating conditions. Almost every calibration point needs a spark sweep to see if the engine can be operated at the MBT timing condition. If not, a certain degree of safety margin is needed to avoid pre-ignition or knock during engine operation. Open-loop spark mapping usually requires a tremendous amount of effort and time to achieve a satisfactory calibration. This paper shows that MBT timing can be achieved by regulating a composite feedback measure derived from the in-cylinder ionization signal referenced to a top dead center crank angle position. A PI (proportional and integral) controller is used to illustrate closed-loop control of MBT timing.