A Multi-objective Optimization Algorithm for Air-path System of Diesel Engines⁎This work was supported by National Natural Science Foundation of China(NSFC) under Grant 61890924, 61803079, and in part by U1864206. Issue 10 (2021)

• Record Type: Journal Article
• Authors: Yu, Jingjiang; Chen, Ran; Li, Yuzhe; Kang, Mingxin; Yu, Shengping
• Is Part Of: IFAC-PapersOnLine. Volume 54:Issue 10(2021)
• Page Start: 90
• Page End: 95
• Publication Date: 2021
• Subjects: data-driven optimization -- Gaussian process -- multi-objective Bayesian optimization -- diesel engine performance optimization; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2021.10.146
• Languages: English
• ISSNs: 2405-8963

Abstract

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 Hyper Volume Improvement(EHVI), which can balance the trade-off 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.