文章基本信息

标题：Optimal Estimation of Gasoline LP-EGR via Unscented Kalman Filtering with Mixed Physics-based/Data-driven Components Modeling ⁎
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作者：Kwangmin Kim ; Jinsung Kim ; Oheun Kwon 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2020
卷号：53
期号：2
页码：144-151
DOI：10.1016/j.ifacol.2020.12.112
语种：English
出版社：Elsevier
摘要：AbstractWe propose a novel optimal estimation methodology for gasoline engine LP (low-pressure) EGR (exhaust gas recirculation) air-path system, which allows us to implement virtual sensors for oxygen mass fraction at the intake manifold and EGR mass flow rate at the LP-EGR valve, real sensors for them too expensive to deploy in production cars. We first decompose the LP-EGR air-path system into several sub-components; and opportunistically utilize physics-based modeling or data-driven modeling for each component depending on their model complexity. In particular, we apply the technique of MLP (multi-layer perceptron) as a means for data-driven modeling of LP-EGR/throttle valves and engine cylinder valve aspiration dynamics, all of which defy accurate physics-based modeling, that is also simple enough for real-time running. We further optimally combine these physics-based and data-driven modelings in the framework of UKF (unscented Kalman filtering), and also manifest via formal analysis that this mixed physics-based/data-driven modeling renders our estimator much faster to run as compared to the case of full data-driven MLP modeling. In doing so, we also extend the standard UKF theory to the more general case, where the system contains non-additive uncertainties both in the measurement and process models with cross-correlations and state-dependent variances, which stems from the inherent peculiar structure of our mixed physics-based/data-driven modeling approach, for the UKF formulation. Experiments are also performed to show the theory.
关键词：KeywordsGasoline engine low-pressure exhaust gas recirculation (Gasoline LP-EGR)mixed physics-based/data-driven modelingmulti-layer perceptron (MLP)unscented Kalman filtering (UKF)non-additive cross-correlated measurementprocess noises with state-dependent variacnes