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Research Papers

Modeling, Estimation, and Control of HCCI Engine With In-Cylinder Pressure Sensing

[+] Author and Article Information
Youngsun Nam

Interactive & Networked Robotics Laboratory,
Department of Mechanical &
Aerospace Engineering,
Seoul National University,
Seoul 08826, South Korea.

Jaehyun Kim, Han Ho Song

Advanced Energy System Laboratory,
Department of Mechanical &
Aerospace Engineering,
Seoul National University,
Seoul 08826, South Korea

Cheongyo Bahk, Inyoung Jang

Interactive & Networked Robotics Laboratory,
Department of Mechanical &
Aerospace Engineering,
Seoul National University,
Seoul 08826, South Korea

Dongjun Lee

Interactive & Networked Robotics Laboratory,
Department of Mechanical &
Aerospace Engineering,
Seoul National University,
Seoul 08826, South Korea
e-mail: djlee@snu.ac.kr

1Present address: Hyundai Motor Group, South Korea.

2Present address: Tenergy, South Korea.

3Corresponding author.

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT,AND CONTROL. Manuscript received April 16, 2017; final manuscript received January 21, 2018; published online March 19, 2018. Assoc. Editor: Azim Eskandarian.

J. Dyn. Sys., Meas., Control 140(6), 061015 (Mar 19, 2018) (12 pages) Paper No: DS-17-1196; doi: 10.1115/1.4039210 History: Received April 16, 2017; Revised January 21, 2018

We propose a novel modeling, estimation, and control framework for homogeneous charge compression ignition (HCCI) engines, which, by utilizing direct in-cylinder pressure sensing, can detect, and react to, the wide spectrum of combustion, thereby allowing for the prevention or even recovery from partial burn or misfire, while significantly improving the stability of transition control. For this, we first develop a discrete-time cyclic control-oriented model of the HCCI process, for which we completely replace the Arrhenius integral by quantities based on the in-cylinder pressure sensing. We then propose a nonlinear state feedback control based on the exact feedback linearization and the switching linear quadratic regulators (LQRs), and also present how the state and other quantities necessary for this control can be estimated by using the in-cylinder pressure sensing. We also provide a new modeling approach for heat transfer, which, through principal component analysis (PCA), can systematically allow us to choose most significant variables, thereby substantially improving control and estimation precision. Simulation studies using a continuous-time detailed HCCI engine model built on matlab/simulink and Cantera Toolbox are also performed to demonstrate the efficacy of our proposed framework for the scenarios of engine load transition and partial burn recovery with the enlarged regions-of-attraction with less stringent actuation limitation also shown.

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References

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Figures

Grahic Jump Location
Fig. 1

Illustration of HCCI engine cycle: control actuation (i.e., VkEVC, uf,k) is computed during the control decision period by using the state information (i.e., TkIVC, nf,kIVC) at IVC and the in-cylinder pressure sensing from the main combustion process

Grahic Jump Location
Fig. 2

Estimation of TkIVC (top), nf,kIVC (middle), and ηkm (bottom) using the procedures of Sec. 4 with in-cylinder pressure sensing (in red solid spheres or asterisks) and the continuous-time data from the simulation model of Sec. 7.1 (in solid blue line or circle), including (near) complete combustion (first and second cycles), partial burn (third cycle), (near) misfire (fourth and fifth cycles), and noticeable NVO reaction (third cycle). Also shown are TkEVO,TkIVO,nf,kEVO,nf,kEVC, and nf,kIVO (in red circles).

Grahic Jump Location
Fig. 3

Heat transfer versus available chemical energy of fuel of the main combustion (left) and the NVO process (right) from the continuous-time details simulation model of Sec. 7.1. For the main combustion, region with over 50J of heat transfer can be considered as complete combustion whereas that with around 20J as misfire. For the NVO process, regions with over 30J of heat transfer are associated with the NVO reaction.

Grahic Jump Location
Fig. 4

Continuous-time data nO2(t),T(t) of the simulation model and control input uf,k,VkEVC for the engine load transition under the open-loop control (thin red), and the proposed nonlinear control (thick blue): (a) high-to-low load transition and (b) low-to-high load transition

Grahic Jump Location
Fig. 5

Partial burn recovery simulation (with ηom≈3.15%) under the open-loop control (thin red) and the proposed nonlinear control (thick blue): (top) nO2(t) and T(t) from the continuous-time simulation model; (bottom) applied control uf,k and VkEVC

Grahic Jump Location
Fig. 6

Region of attraction with the open-loop control (top), our proposed nonlinear control with Δθmax=10CAD/cycle (middle) and with Δθmax=20CAD/cycle (bottom). The direction and the (log-scale) length of each arrow represent the state evolution after five cycles. Also shown are the integral curves of the velocity fields.

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