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In homogeneous spark-ignition (SI) engines, ignition timing is used to control the combustion phasing (crank angle of fifty percent of fuel burned, CA50), which affects fuel economy, engine torque output, and emissions. This paper presents a model-based adaptive ignition timing prediction strategy using a control-oriented dynamic combustion model for real-time closed-loop combustion phasing control. The combustion model predicts the burn duration from ignition timing to CA50 (ΔθIGN-CA50) at Intake Valve Closing (IVC) for the upcoming cycle based on current engine operating conditions, including variable valve timing, predicted ignition timing, air-fuel ratio, engine speed, and engine load. To maintain the accuracy of combustion model and ignition timing prediction during the engine lifetime, a Recursive-Least-Square (RLS) with Variable Forgetting Factor (VFF) based adaptation algorithm is developed to handle both short term (operating-point-dependent) and long term (engine aging) model errors. Due to short term model errors and stochastic characteristics of cycle-to-cycle combustion variations, large model errors may occur during severe transient operating conditions (tip-in/tip-out), which can result in wrong adjustments and excessive adaptations. Since on-road SI engines are always operating in transient conditions, the ‘Heavy Transient Detection’ algorithm is developed to avoid fault adaptation and assist the adaptation algorithm to be stable. On-road vehicle testing data is used to evaluate the performance of the entire model-based adaptive burn duration and ignition timing prediction algorithm. With only 64 calibration points, a mean ignition timing prediction error of 0.2 Crank Angle Degree (CAD) and average iteration number of 2 shows the capability of adaptive ignition timing prediction, a significant reduction of calibration efforts, and potential of real-time application of the developed adaptive ignition timing prediction algorithm.

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The in-cylinder trapped air, residual gas, and temperature directly impact Spark Ignition (SI) engine operation and control. However, estimation of these variables dynamically is difficult. This study proposes a dynamic cycle-by-cycle model for estimation of the in-cylinder mixture temperature at different events such as Intake Valve Closed (IVC), as well as mass of trapped air and residual gas. In-cylinder, intake and exhaust pressure traces are the primary inputs to the model. The mass of trapped residual gas is affected by valve overlap increase due to the exhaust gas backflow. Of importance to engines with Variable Valve Timing (VVT), the compressible ideal-gas flow correlations were applied to predict the exhaust gas backflow into the cylinder. Furthermore, 1D GT-Power Three Pressure Analysis (TPA) was used to calibrate and validate the designed model under steady-state conditions. To minimize the calibration efforts, Design of Experiments (DOE) analysis methodology was used. The transient behavior of the model was validated using dynamometer dynamic driving cycle. The cycle-based output parameters of the developed model are in good agreement with transient experimental data with minimal delay and overshoot. The predicted parameters follow the input dynamics propagated in the in-cylinder, intake and exhaust pressure traces with a 1.5% average relative steady-state prediction error.