Multi-Objective Optimization Under the Hood: Engine Calibration via Metaheuristics and Probabilistic Methods

Abstract

The automotive industry faces the complex task of optimizing engine performance across diverse and often competing metrics, including fuel consumption and emissions. To effectively address this challenge and manage the necessary trade-offs, accurate engine calibration is essential. This thesis investigates the application of optimization methods, specifically Genetic Algorithms (GA) and Bayesian Optimization (BO), as a promising solution for engine calibration. Single-objective optimization targets the best solution for one goal, while multi-objective optimization balances trade-offs between conflicting goals to approximate the Pareto front, the set of optimal solutions where no objective can be improved without worsening another. This work explores both single-objective and multi-objective optimization implementations for GA and BO. In addition, a hybrid approach combining BO followed by GA is proposed for multi-objective optimization. The methods were evaluated in an experimental study. In the single-objective case, both GA and BO outperformed established internal benchmark values (provided by Volvo). For multi-objective optimization, GA, BO, and the hybrid method also achieved superior results. Across both single-objective and multi-objective problems, BO consistently delivered the best performance. These findings demonstrate that GA, BO, and the hybrid approach are viable strategies for engine calibration and provide a strong foundation for the development of more specialized calibration methods.