A Control-Oriented Two-Zone Charge Mixing Model for HCCI Engines With Experimental Validation Using an Optical Engine

Yoon, Yongsoon; Sun, Zongxuan; Zhang, Shupeng; Zhu, Guoming

doi:10.1115/1.4026660

Cited by 6 publications

(3 citation statements)

References 20 publications

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“…Model-based engine studies offer significant time and cost savings in enhancing engine performance. Traditionally, physics-based models have been employed to represent the physical processes of engines, enabling the prediction of cylinder characteristics [1][2][3][4]. These physics-based models are also utilized for control design purposes [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

LSTM for Modeling of Cylinder Pressure in HCCI Engines at Different Intake Temperatures via Time-Series Prediction

Sontheimer,

Singh,

Verma

et al. 2023

Machines

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Modeling engines using physics-based approaches is a traditional and widely-accepted method for predicting in-cylinder pressure and the start of combustion (SOC). However, developing such intricate models typically demands significant effort, time, and knowledge about the underlying physical processes. In contrast, machine learning techniques have demonstrated their potential for building models that are not only rapidly developed but also efficient. In this study, we employ a machine learning approach to predict the cylinder pressure of a homogeneous charge compression ignition (HCCI) engine. We utilize a long short-term memory (LSTM) based machine learning model and compare its performance against a fully connected neural network model, which has been employed in previous research. The LSTM model’s results are evaluated against experimental data, yielding a mean absolute error of 0.37 and a mean squared error of 0.20. The cylinder pressure prediction is presented as a time series, expanding upon prior work that focused on predicting pressure at discrete points in time. Our findings indicate that the LSTM method can accurately predict the cylinder pressure of HCCI engines up to 256 time steps ahead.

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Section: Introductionmentioning

confidence: 99%

LSTM for Modeling of Cylinder Pressure in HCCI Engines at Different Intake Temperatures via Time-Series Prediction

Sontheimer,

Singh,

Verma

et al. 2023

Machines

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“…For instance, re-induction can be achieved by reopening the exhaust valve during the intake stroke, hence some of the exhaust gas is reinducted into the cylinder along with the fresh charge. 24,26,27 On the other hand, early closing of the exhaust valve and late opening of the intake valve, also known as negative valve overlap (NVO) [28][29][30][31][32][33] leads to retention of burned charge inside the cylinder. Stability analysis is conducted based on an HCCI engine with high dilution reinducted by a second opening of the exhaust valve during the intake stroke.…”

Section: Introductionmentioning

confidence: 99%

Physics-based modeling of homogeneous charge compression ignition engines with exhaust throttling

Chiang

Singh

2022

Proceedings of the Institution of Mechanical Engineers, Part D:

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Homogeneous charge compression ignition (HCCI) engines create a more efficient power source for either stationary power generators or automotive applications. Due to the absence of direct actuation of ignition, HCCI based combustion control is quite challenging. For the purpose of control synthesis and design, a crank-angle based dynamic model of an HCCI engine with internal exhaust gas recirculation (EGR) is proposed in this paper. The HCCI autoignition timing is controlled by the amount of internal EGR induced by the cost effective exhaust throttling strategy. The zero dimensional single zone model is developed based on conservation of mass and energy and ideal gas law. The model is comprised of five subsystems: cylinder volume, intake, and exhaust runners along with combustion and heat-transfer models. Inputs to the model include engine speed, intake temperature, fueling rate, intake, and exhaust throttle positions. Outputs of the model contain the pressure, temperature, mass and burned gas fraction in the intake, exhaust and cylinder, air-to-fuel ratio (AFR), heat release rate, combustion phasing, and the indicated mean effective pressure (IMEP). The model is developed based on MATLAB/Simulink and validated against experimental data under steady-state and transient conditions at various intake temperatures, fueling levels, and exhaust throttle positions. Results demonstrate that by changing the exhaust throttle position, the pressure in the exhaust runner is regulated. As a result, the residual gas fraction is altered, leading to changes in mixture temperature and thus control of the HCCI combustion phasing. In the end, closed loop simulation is conducted with a linear quadratic Gaussian (LQG) controller applied to the crank-angle based model for regulation of combustion timing CA50 using exhaust throttle during load (fueling level) changes. In the future, this model can be utilized for control development and hardware in the loop (HIL) simulation.

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“…2 Yoon et al experimented on an optical engine to validate the two-zone HCCI charge mixing model. 3 However, the control-oriented engine combustion model is traditionally based upon the so-called Wiebe function, 4 which models the mass fraction burned (MFB) with pre-calibrated combustion parameters. Although such a model is computationally efficient, the main disadvantage of this approach is that the Wiebe function is based on a set of predetermined parameters, such as start of combustion (SOC) and burning duration (BD) that are not directly related to engine control inputs, for instance, fuel injection rate and timing.…”

Section: Introductionmentioning

confidence: 99%

Model-based calibration of reaction-based diesel combustion dynamics

Men

Haskara

Wang

et al. 2017

Proceedings of the Institution of Mechanical Engineers, Part D:

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This paper presents a control-oriented, reaction-based diesel combustion model that predicts the time-based rate of combustion, in-cylinder gas temperature, and pressure over one engine cycle. The model, based on the assumption of a homogeneous thermodynamic combustion process, uses a two-step chemical reaction mechanism that consists of six species: diesel fuel (C10.8H18.7), oxygen (O2), carbon dioxide (CO2), water (H2O), nitrogen (N2), and carbon monoxide (CO). The temperature variation rate is calculated based on the rate of change of species concentrations; the heat loss correlation is also used to study the model performance. The accuracy of the model is evaluated using test data from a GM 6.6 L, eight-cylinder Duramax engine. The main contribution is the model calibration under different key operational conditions over a large engine speed and load range as well as different injection timings and exhaust gas recirculation rates by solving the optimal calibration problem. The calibrated reaction-based model accurately predicts the indicated mean effective pressure, while keeping the errors of in-cylinder pressure and temperature small, and, at the same time, significantly reduces the calibration effort, especially when the engine is operated under multiple fuel injection operations compared with Wiebe-based combustion models. The calibrated model parameters have a strong correlation to engine speed, load, and injection timings, and, as a result, a universal parameter calibration structure is proposed for entire operational conditions.

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A Control-Oriented Two-Zone Charge Mixing Model for HCCI Engines With Experimental Validation Using an Optical Engine

Cited by 6 publications

References 20 publications

LSTM for Modeling of Cylinder Pressure in HCCI Engines at Different Intake Temperatures via Time-Series Prediction

LSTM for Modeling of Cylinder Pressure in HCCI Engines at Different Intake Temperatures via Time-Series Prediction

Physics-based modeling of homogeneous charge compression ignition engines with exhaust throttling

Model-based calibration of reaction-based diesel combustion dynamics

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