Unified approach to engine cylinder pressure reconstruction using time-delay neural networks with crank kinematics or block vibration measurements

Abstract

Closed-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. 27 main-section pages (double spaced) 23 references Figures 1-13 No appendices

Tdnn-Based Engine In-Cylinder Pressure Estimation from Shaft Velocity Spectral Representation

“…Then, the TDNN efficiency around the pressure peaks is also computed. In particular, the normalized peak error (Pmax) is computed as the ratio (as a percentage) of the error between the reconstructed and recorded peak divided by the maximum measured cylinder pressure [ 28 ]. Besides, the peak localization error (Ploc) is computed as the difference in degrees between the location of peak pressure in the reconstructed and the measured signal.…”

“…Besides, the peak-based measures are not computed, lacking an appropriate insight into the method’s capability to follow pressure fluctuations around the main peaks. In contrast, the work exposed by authors in [ 28 ] deals with two predictors (input signals) and studies a three-cylinder engine within nine states. Indeed, such a method obtains the best peak-based measures.…”

“…Similarly, the work presented in [ 27 ] developed a model to estimate the pressure in a single-cylinder HCCI engine using a multilayer neural network, operating as predictors the crank angle and the engine speed. In [ 28 ], the authors introduced a unified approach to reconstruct engine cylinder pressure using time-delay neural networks with crank kinematics or block vibration measurements. The authors in [ 29 ] estimate a four-cylinder engine pressure using a Kalman Filter-based approach from structural vibration signals.…”

A crank-kinematics-based engine cylinder pressure reconstruction model

“…These varying engine speed results in figure 12 confirm that the nonlinear time-dependent model described by equation (1) is sufficiently flexible to be able to adapt to the needs of an inverse dynamic model suitable for fast cylinder pressure reconstruction. (The model was also extended to include both delay-time and preview data (such as adopted in [28]) but it actually reduced the accuracy of predictions). The use of the appropriately easy-to-compute calibration peak pressures errors tested against the 3% acceptability criterion, appears to be an effective way of retaining high confidence predictions.…”

“…At different test points, particularly at different speeds, predictions deteriorated. In a return to feed-forward ANN-based prediction, Trimby et al 28 used a delay-future predictions of cylinder pressure using both crank kinematics and block vibrations taken from a DISI engine. By careful pre-processing of measured data, crank-based prediction accuracies were P max within 3% and θ max within ± 3 ° .…”