Yu, Jingjiang (Northeastern University), Chen, Ran (Northeastern University), Li, Yuzhe (Northeastern University), Kang, Mingxin (Northeastern University), Yu, Shengping (Northeastern University)

A Multi-Objective Optimization Algorithm for Air-Path System of Diesel Engines

Scheduled for presentation during the Invited session "Advanced Powertrain Control" (MoAT3), Monday, August 23, 2021, 15:40−16:00, Room T3

6th IFAC Conference on Engine and Powertrain Control, Simulation and Modeling, August 23-25, 2021, Tokyo, Japan

This information is tentative and subject to change. Compiled on April 25, 2024

Abstract

Modern engine is a typical multi-objective control system. This paper proposed a multi-objective Bayesian optimization strategy to deal with the performance optimization for diesel engines. Since the objective functions of diesel engines are complicated and computationally expensive, Gaussian processes(GPs) are constructed by using the data collected from the diesel engine to approximate the real objective functions. Non-dominated sorting genetic algorithm II(NSGAII) leverages the Gaussian process to generate the Pareto-optimal solutions. The Gaussian process will be updated iteratively by Bayesian posterior information, which increases the reliability of the models. The acquisition function Expected HyperVolume Improvement(EHVI), which can balance the trade-o between exploration and exploitation throughout the optimization process, is used to select the solutions for real computationally expensive multi-objective evaluation. The proposed algorithm is applied on a diesel engine, which shows its reliability and high efficiency. The metrics hypervolume(HV) and the control results demonstrate that the proposed algorithm has outstanding effects for performance optimization of diesel engine airpath.