Dunne, Julian J and Bennett, Colin (2019) A crank-kinematics based engine cylinder pressure reconstruction model. International Journal of Engine Research. ISSN 1468-0874
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Abstract
A new inverse model is proposed for reconstructing steady-state and transient engine cylinder pressure using measured crank kinematics. An adaptive nonlinear time-dependent relationship is assumed between windowed-subsections of cylinder pressure and measured crank kinematics in a time-domain format (rather than in crank-angle-domain). This relationship comprises a linear sum of four separate nonlinear functions of crank jerk, acceleration, velocity, and crank angle. Each of these four nonlinear functions is obtained at each time instant by fitting separate m-term Chebychev polynomial expansions, where the total 4m instantaneous expansion coefficients are found using a standard (over-determined) linear least-square solution method. A convergence check on the calibration accuracy shows this initially improves as more Chebychev polynomial terms are used, but with further increase, the over-determined system becomes singular. Optimal accuracy Chebychev expansions are found to be of degree m=4, using 90 or more cycles of engine data to fit the model. To confirm the model accuracy in predictive mode, a defined measure is used, namely the ‘calibration peak pressure error’. This measure allows effective a priori exclusion of occasionally unacceptable predictions. The method is tested using varying speed data taken from a 3-cylinder DISI engine fitted with cylinder pressure sensors, and a high resolution shaft encoder. Using appropriately-filtered crank kinematics (plus the ‘calibration peak pressure error’), the model produces fast and accurate predictions for previously unseen data. Peak pressure predictions are consistently within 6.5% of target, whereas locations of peak pressure are consistently within ± 2.7˚ CA. The computational efficiency makes it very suitable for real-time implementation.
Item Type: | Article |
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Keywords: | IC Engine efficiency, combustion Control, Fast indirect pressure sensing, emissions reduction, Hybrid Electric Vehicles, Heavy Duty Vehicles |
Schools and Departments: | School of Engineering and Informatics > Engineering and Design |
Research Centres and Groups: | Dynamics, Control and Vehicle Research Group |
Depositing User: | Julian Dunne |
Date Deposited: | 01 Oct 2019 13:16 |
Last Modified: | 26 Nov 2019 12:30 |
URI: | http://sro.sussex.ac.uk/id/eprint/86457 |
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📧 Request an updateProject Name | Sussex Project Number | Funder | Funder Ref |
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Adaptive cylinder pressure reconstruction for production engines. | G0297 | EPSRC-ENGINEERING & PHYSICAL SCIENCES RESEARCH COUNCIL | EP/E03246X/1 |