A UNIFIED APPROACH TO CYLINDER PRESSURE RECONSTRUCTION.pdf (936.44 kB)
A unified approach to engine cylinder pressure reconstruction using time-delay neural networks with crank kinematics or block vibration measurements
journal contribution
posted on 2023-06-09, 02:32 authored by Stuart Trimby, Julian DunneJulian Dunne, Colin Bennett, Dave RichardsonClosed-loop combustion control (CLCC) in gasoline engines can improve efficiency, calibration effort, and performance using different fuels. Knowledge of in-cylinder pressures is a key requirement for CLCC. Adaptive cylinder pressure reconstruction offers a realistic alternative to direct sensing, which is otherwise necessary as legislation requires continued reductions in CO2 and exhaust emissions. Direct sensing however is expensive and may not prove adequately robust. A new approach is developed for in-cylinder pressure reconstruction on gasoline engines. The approach uses Time-Delay feed-forward Artificial Neural Networks trained with the standard Levenberg-Marquardt algorithm. The same approach can be applied to reconstruction via measured crank kinematics obtained from a shaft encoder, or measured engine cylinder block vibrations obtained from a production knock sensor. The basis of the procedure is initially justified by examination of the information content within measured data, which is considered to be equally important as the network architecture and training methodology. Key hypotheses are constructed and tested using data taken from a 3-cylinder (DISI) engine to reveal the influence of the data information content on reconstruction potential. The findings of these hypotheses tests are then used to develop the methodology. The approach is tested by reconstructing cylinder pressure across a wide range of steady-state engine operation using both measured crank kinematics and block accelerations. The results obtained show a very marked improvement over previously published reconstruction accuracy for both crank kinematics and cylinder block vibration based reconstruction using measurements obtained from a multi-cylinder engine. The paper shows that by careful processing of measured engine data, a standard neural network architecture and a standard training algorithm can be used to very accurately reconstruct engine cylinder pressure with high levels of robustness and efficiency.
Funding
Adaptive cylinder pressure reconstruction for production engines.; G0297; EPSRC-ENGINEERING & PHYSICAL SCIENCES RESEARCH COUNCIL; EP/E03246X/1
History
Publication status
- Published
File Version
- Accepted version
Journal
International Journal of Engine ResearchISSN
1468-0874Publisher
SageExternal DOI
Issue
3Volume
18Page range
256-272Department affiliated with
- Engineering and Design Publications
Full text available
- Yes
Peer reviewed?
- Yes