An internal combustion engine (ICE) is a highly nonlinear dynamic and complex engineering system whose operation is constrained by operational limits, including emissions, noise, peak in-cylinder pressure, combustion stability, and actuator constraints. To optimize today’s ICEs, seven to ten control actuators and 10–20 feedback sensors are often used, depending on the engine applications and target emission regulations. This requires extensive engine experimentation to calibrate the engine control module (ECM), which is both cumbersome and costly. Despite these efforts, optimal operation, particularly during engine transients and to meet real driving emission (RDE) targets for broad engine speed and load conditions, has still not been obtained. Methods of model predictive control (MPC) have shown promising results for real-time multi-objective optimal control of constrained multi-variable nonlinear systems, including ICEs. This paper reviews the application of MPC for ICEs and analyzes the recent developments in MPC that can be utilized in ECMs. ICE control and calibration can be enhanced by taking advantage of the recent developments in the field of Artificial Intelligence (AI) in applying Machine Learning (ML) to large-scale engine data. Recent developments in the field of ML-MPC are investigated, and promising methods for ICE control applications are identified in this paper.

The cyclic variations of homogeneous charge compression ignition (HCCI) ignition timing is studied for a range of charge properties by varying the equivalence ratio, intake temperature, intake pressure, exhaust gas recirculation (EGR) rate, engine speed, and coolant temperature. Characterization of cyclic variations of ignition timing in HCCI at over 430 operating points on two single-cylinder engines for five different blends of primary reference fuel (PRF), (iso-octane and n-heptane) is performed. Three distinct patterns of cyclic variation for the start of combustion (SOC), combustion peak pressure ( P_max), and indicated mean effective pressure (i.m.e.p.) are observed. These patterns are normal cyclic variations, periodic cyclic variations, and cyclic variations with weak/misfired ignitions. Results also show that the position of SOC plays an important role in cyclic variations of HCCI combustion with less variation observed when SOC occurs immediately after top dead centre (TDC). Higher levels of cyclic variations are observed in the main (second) stage of HCCI combustion compared with that of the first stage for the PRF fuels studied. The sensitivity of SOC to different charge properties varies. Cyclic variation of SOC increases with an increase in the EGR rate, but it decreases with an increase in equivalence ratio, intake temperature, and coolant temperature.

A comprehensive control strategy for an automotive solenoid variable valve timing actuator is presented that addresses the issues of feedback sensors, soft seating or landing control, disturbance rejection and feedforward design. In particular, the motion control of the engine exhaust valve actuator subject to large cycle-to-cycle gas force variations is successfully demonstrated in simulation and on an experimental test-bench. Also provided is a method of characterization and online cycle-to-cycle identification of combustion gas force disturbances. The identified gas forces are used in energy-based feedforward and flatness-based landing algorithms. Simulated and experimental results indicate the proposed control methodology is capable of compensating for the combustion gas force disturbances experienced by exhaust valve solenoid actuators.

This brief considers the control of solenoid valve actuators used for gas exchange in internal combustion engines. Although solenoid valves offer performance benefits over traditional camshaft-based valve systems, maintaining low impact velocity is a critical performance requirement. Flatness provides a convenient framework for meeting a number of performance specifications on the valve's end motion. The proposed control design incorporates voltage constraints, nonlinear magnetic effects, and various motion planning requirements. A flat output acts as a design parameter and is parameterized with a spline basis. A nonlinear feasibility problem is solved to obtain optimal spline coefficients such that performance requirements are met. The resulting flat output provides an open-loop control which is augmented with feedback so that a linear stable tracking error system results. The proposed control scheme is demonstrated in simulation and on an experimental testbed. The performance of a proportional-integral controller is compared experimentally to the flatness-based method