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The number of control actuators available on spark-ignition engines is rapidly increasing to meet demand for improved fuel economy and reduced exhaust emissions. The added complexity greatly complicates control strategy development because there can be a wide range of potential actuator settings at each engine operating condition, and map-based actuator calibration becomes challenging as the number of control degrees of freedom expand significantly. Many engine actuators, such as variable valve actuation and flow control valves, directly influence in-cylinder combustion through changes in gas exchange, mixture preparation, and charge motion. The addition of these types of actuators makes it difficult to predict the influences of individual actuator positioning on in-cylinder combustion without substantial experimental complexity.

A supervisory controller strategy for a hybrid vehicle coordinates the operation of the two power sources onboard of a vehicle to maximize objectives like fuel economy. In the past, various control strategies have been developed using heuristics as well as optimal control theory. The Stochastic Dynamic Programming (SDP) has been previously applied to determine implementable optimal control policies for discrete time dynamic systems whose states evolve according to given transition probabilities. However, the approach is constrained by the curse of dimensionality, i.e. an exponential increase in computational effort with increase in system state space, faced by dynamic programming based algorithms. This paper proposes a novel approach capable of overcoming the curse of dimensionality and solving policy optimization for a system with very large design state space.

Boosted Homogeneous Charge Compression Ignition (HCCI) has been modeled and has demonstrated the potential to extend the engine's upper load limit. A commercially available engine simulation software (GT-PowerÖ) coupled to the University of Michigan HCCI combustion and heat transfer correlations was used to model a 4-cylinder boosted HCCI engine with three different boosting configurations: turbocharging, supercharging and series turbocharging. The scope of this study is to identify the best boosting approach in order to extend the HCCI engine's operating range. The results of this study are consistent with the literature: Boosting helps increase the HCCI upper load limit, but matching of turbochargers is a problem. In addition, the low exhaust gas enthalpy resulting from HCCI combustion leads to high pressures in the exhaust manifold increasing pumping work. The series turbocharging strategy appears to provide the largest load range extension.