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

Abstract

Closed-loop combustion control in gasoline engines can improve efficiency, calibration effort, and performance using different fuels. Knowledge of in-cylinder pressures is a key requirement for closed-loop combustion control. 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 feedforward 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 three-cylinder (direct injected spark ignition) 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. This article 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.