Air-Fuel Ratio Control of Spark Ignition Engines With Unknown System Dynamics Estimator: Theory and Experiments

This brief addresses the emission reduction of spark ignition engines by proposing a new control to regulate the air-fuel ratio (AFR) around the ideal value. After revisiting the engine dynamics, the AFR regulation is represented as a tracking control of the injected fuel amount. This allows to take the fuel film dynamics into consideration and simplify the control design. The lumped unknown engine dynamics in the new formulation are online estimated by suggesting a new effective unknown system dynamics estimator. The estimated variable can be superimposed on a commercially configured, well-calibrated gain scheduling like proportional-integral-differential (PID) control to achieve a better AFR response. The salient feature of this proposed control scheme lies in its simplicity and the small number of required measurements, that is, only the air mass flow rate, the pressure and temperature in the intake manifold, and the measured AFR value are used. Practical experiments on a Tata Motors Limited two-cylinder gasoline engine are carried out under a realistic driving cycle. The comparative results show that the proposed control can achieve an improved AFR control response and reduced emissions

Predictive Control Strategies for Automotive Engine Coldstart Emissions

In this study, a comprehensive investigation is carried out to study the effectiveness of model-based predictive control strategies to solve a formidable automotive control problem, that is, reducing the amount of cumulative hydrocarbon (HC) tailpipe emissions or HCcum over the first few minutes of an automotive engine operation which is known as the coldstart period. More than 80% of the total HC emissions for a typical driving cycle are generated during the coldstart period. There is a physical trade-off between increasing the exhaust gas temperature (Texh) and reducing engine-out hydrocarbon emission (HCraw-c), which are two key variables affecting the engine performance during the coldstart operation. The design of an effective coldstart controller is associated with lots of difficulties because the behavior of the engine in the coldstart period is highly transient, uncertain, and nonlinear, and also, the key factors are in confliction with each other. In the light of promising reports on the performance of model predictive controllers (MPCs), here, different variants of MPCs are taken into account to find out whether they can effectively cope with the difficulties associated with the coldstart problem for a given automotive engine. The major advantage of MPCs refers to their power to handle different constraints while trying to minimize an objective function to come up with optimal controlling signals. Other than the standard version of MPCs, in this work, some novel versions of such controllers are proposed, which are best suited for the considered control problem. The considered versions of MPCs are: nonlinear MPC (NMPC), preference-based model predictive controller (PBNMPC), and receding horizon sliding controller (RHSC). Also, a powerful classical optimal controller based on the Pontryagin’s minimum principle (PMP) is taken into account to ascertain the veracity of the considered predictive controlling methods. Through an exhaustive simulation, the efficacy of proposed predictive controlling techniques is demonstrated, and also, it is indicated how well such controllers can optimize the related objective function at the heart of coldstart control problem while handling a set of the operating constraints