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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.

Turbocharger compressors are susceptible to surge – the instability phenomena that impose limitations on the operation of turbocharged engines because of undesired noise, engine torque capability constraints, and hardware strain. Turbocharged engines are typically equipped with a binary compressor recirculation valve (CRV) whose primary function is to prevent compressor surge. Calibration of the associated control strategy requires in-vehicle tests and usually employs subjective criteria. This work aims to reduce the calibration effort for the strategy by developing a test procedure and data processing algorithms. An automated calibration for CRV control is developed that will generate a baseline calibration that avoids surge events. The effort to obtain the baseline calibration, which can be further fine-tuned, is thereby significantly reduced.