Estimation of In-Cylinder-Pressure Torque from Angular Speed by Kalman Filtering

Henn, Michael; Kiencke, Uwe

doi:10.1016/b978-0-08-042589-4.50007-8

Cited by 1 publication

(3 citation statements)

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“…Such techniques require a state-space model representation of the overall system, in which the state vector may include both variables of interest and model parameters to be estimated. The works [25][26][27] share a common framework in which the indicated torque is estimated from measurements of the engine speed, and the squared engine speed is chosen as a state variable. In [25], the authors choose directly the net torque as an additional state variable, modeling its dynamics as a discrete low pass filter excited by a white process.…”

Section: Introductionmentioning

confidence: 99%

“…The works [25][26][27] share a common framework in which the indicated torque is estimated from measurements of the engine speed, and the squared engine speed is chosen as a state variable. In [25], the authors choose directly the net torque as an additional state variable, modeling its dynamics as a discrete low pass filter excited by a white process. The incylinder pressure is then reconstructed from the net torque estimate provided by a Kalman filter.…”

Section: Introductionmentioning

confidence: 99%

“…are fed to the EKF. The measurement error, denoted by v(k) in (25), is modeled as a zeromean white process with variance R. Let x(k|k) denote the estimate of the state x at time k based on the observations y(h) of the output, for h = 0, . .…”

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confidence: 99%

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In-Cylinder Pressure Estimation from Rotational Speed Measurements via Extended Kalman Filter

Quartullo

Garulli

Giannitrapani

et al. 2023

Sensors

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Real-time estimation of the in-cylinder pressure of combustion engines is crucial to detect failures and improve the performance of the engine control system. A new estimation scheme is proposed based on the Extended Kalman Filter, which exploits measurements of the engine rotational speed provided by a standard phonic wheel sensor. The main novelty lies in a parameterization of the combustion pressure, which is generated by averaging experimental data collected in different operating points. The proposed approach is validated on real data from a turbocharged compression ignition engine, including both nominal and off-nominal working conditions. The experimental results show that the proposed technique accurately reconstructs the pressure profile, featuring a fit performance index exceeding 90% most of the time. Moreover, it can track changes in the engine operating conditions as well as detect the presence of cylinder-to-cylinder variations.

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