基于代理模型的发动机标定优化

IF 1 Q4 AUTOMATION & CONTROL SYSTEMS
Anuj Pal, Yan Wang, Ling Zhu, G. Zhu
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引用次数: 5

摘要

由于全球变暖和能源消耗,柴油发动机的控制和校准变得越来越复杂,人们希望提高燃油经济性,减少排放(氮氧化物和烟尘)。随着控制特性的不断增加,利用传统的基于发动机映射的方法标定发动机控制参数变得越来越困难,因为标定时间不合理。因此,本研究的重点是如何在有限的预算范围内,通过有效优化三个控制参数,即可变几何涡轮增压器(VGT)位置、废气再循环(EGR)阀门位置和喷射启动(SOI),来完成发动机校准。在增压压力和发动机负荷(BMEP)的约束下,将发动机的油耗(BSFC)和排放(NOX)性能作为目标函数来考虑。由于发动机校准过程需要大量的高保真度评估,因此使用代理建模方法来快速执行校准,同时大大减少了计算预算。Kriging元建模用于这项工作,期望改进(EI)作为获取功能。结果表明,该算法的计算成本降低60%以上,计算结果接近实际的近Pareto最优集。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Engine Calibration Optimization Based on its Surrogate Models
Diesel engines are becoming increasingly complex to control and calibrate with the desire of improving fuel economy and reducing emissions (NOx and Soot) due to global warming and energy usage. With ever increased control features, it is becoming more and more difficult to calibrate engine control parameters using the traditional engine mapping based methods due to unreasonable calibration time required. Therefore, this research focuses on the problem of performing engine calibration within a limited budget by efficiently optimizing three control parameters: namely variable geometry turbocharger (VGT) position, exhaust gas recirculation (EGR) valve position, and start of injection (SOI). Engine performance in terms of fuel consumption (BSFC) and emissions (NOX) are considered as objective function here with the constraint on boost pressure and engine load (BMEP). Since the engine calibration process requires a large number of high-fidelity evaluations, surrogate modeling methods are used to perform calibration quickly with a significantly reduced computational budget. Kriging metamodeling is used for this work with Expected Improvement (EI) as acquisition function. Results show more than 60% decrease in computational cost with results close to actual near Pareto optimal set.
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来源期刊
Mechatronic Systems and Control
Mechatronic Systems and Control AUTOMATION & CONTROL SYSTEMS-
CiteScore
1.40
自引率
66.70%
发文量
27
期刊介绍: This international journal publishes both theoretical and application-oriented papers on various aspects of mechatronic systems, modelling, design, conventional and intelligent control, and intelligent systems. Application areas of mechatronics may include robotics, transportation, energy systems, manufacturing, sensors, actuators, and automation. Techniques of artificial intelligence may include soft computing (fuzzy logic, neural networks, genetic algorithms/evolutionary computing, probabilistic methods, etc.). Techniques may cover frequency and time domains, linear and nonlinear systems, and deterministic and stochastic processes. Hybrid techniques of mechatronics that combine conventional and intelligent methods are also included. First published in 1972, this journal originated with an emphasis on conventional control systems and computer-based applications. Subsequently, with rapid advances in the field and in view of the widespread interest and application of soft computing in control systems, this latter aspect was integrated into the journal. Now the area of mechatronics is included as the main focus. A unique feature of the journal is its pioneering role in bridging the gap between conventional systems and intelligent systems, with an equal emphasis on theory and practical applications, including system modelling, design and instrumentation. It appears four times per year.
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