Modeling and Model-Based Control Of Multi-Mode Combustion Engines for Closed-Loop SI/HCCI Mode Transitions with Cam Switching Strategies.

Homogeneous charge compression ignition (HCCI) combustion has been investigated by many researchers as a way to improve gasoline engine fuel economy through highly dilute unthrottled operation while maintaining acceptable tailpipe emissions. A major concern for successful implementation of HCCI is that it's feasible operating region is limited to a subset of the full engine regime, which necessitates mode transitions between HCCI and traditional spark ignition (SI) combustion when the HCCI region is entered/exited. The goal of this dissertation is to develop a methodology for control-oriented modeling and model-based feedback control during such SI/HCCI mode transitions. The model-based feedback control approach is sought as an alternative to those in the SI/HCCI transition literature, which predominantly employ open-loop experimentally derived actuator sequences for generation of control input trajectories. A model-based feedback approach has advantages both for calibration simplicity and controller generality, in that open-loop sequences do not have to be tuned, and that use of nonlinear model-based calculations and online measurements allows the controller to inherently generalize across multiple operating points and compensate for case-by-case disturbances. In the dissertation, a low-order mean value modeling approach for multi-mode SI/HCCI combustion that is tractable for control design is described, and controllers for both the SI to HCCI (SI-HCCI) and HCCI to SI (HCCI-SI) transition are developed based on the modeling approach. The model is shown to fit a wide range of steady-state actuator sweep data containing conditions pertinent to SI/HCCI mode transitions, and is extended to capture transient SI-HCCI transition data through using an augmented residual gas temperature parameter. The mode transition controllers are experimentally shown to carry out SI-HCCI and HCCI-SI transitions in several operating conditions with minimal tuning, though the validation in the SI-HCCI direction is more extensive. The model-based control architecture is also equipped with an online parameter updating routine, to attenuate error in model-based calculations and improve robustness to engine aging and cylinder to cylinder variability. Experimental examples at multiple operating conditions illustrate the ability of the parameter update routine to improve controller performance by using transient data to tune the model parameters for enhanced accuracy during SI-HCCI mode transitions.PhDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/113351/1/pgoz_1.pd

Thermodynamic Modeling of HCCI Combustion with Recompression and Direct Injection.

Homogeneous Charge Compression Ignition (HCCI) engines have the potential to reduce pollutant emissions while achieving diesel-like thermal efficiencies. The absence of direct control over the start and rate of auto-ignition and a narrow load range makes implementation of HCCI engines into production vehicles a challenging affair. Effective HCCI combustion control can be achieved by manipulating the amount of residual gases trapped from the previous cycle by means of variable valve actuation. In turn, the temperature at intake valve closing and hence auto-ignition phasing can be controlled. Intake charge boosting can be used to increase HCCI fueling rates and loads, while other technologies such as direct injection provide means for achieving cycle to cycle phasing control. Thermodynamic zero-dimensional (0D) models are a computationally inexpensive tool for defining systems and strategies suitable for the implementation of new HCCI engine technologies. These models need to account for the thermal and compositional stratification in HCCI that control combustion rates. However these models are confined to a narrow range of engine operation given that the fundamental factors governing the combustion process are currently not well understood. CFD has therefore been used to understand the effect of operating conditions and input variables on pre-ignition charge stratification and combustion, allowing the development and use of a more accurate ignition model, which is proposed and validated here. A new empirical burn profile model is fit with mass fraction burned profiles from a large HCCI engine data set. The combined ignition model and burn correlation are then exercised and are shown capable of capturing the trends of a diverse range of transient HCCI experiments. However, the small cycle to cycle variations in combustion phasing are not captured by the model, possibly due to recompression heat release effects associated with variable valve actuation. Multi-cycle CFD simulations are therefore performed to gain physical insight into recompression heat release phenomena and the effect of these phenomena on the next cycle. Based on the understanding derived from this CFD work, a simple model of recompression heat release has been implemented in the 0D HCCI modeling framework.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/113499/1/sunand_1.pd

Experimental Analysis and Control of Recompression Homogeneous Charge Compression Ignition Combustion at the High Cyclic Variability Limit.

The automotive industry currently faces many challenges pertaining to strict emissions and fuel consumption constraints for a sustainable society. These regulations have motivated the investigation of low temperature combustion modes such as homogeneous charge compression ignition (HCCI) as a potential solution to meet these demands. HCCI combustion is characterized by high efficiency and low engine-out emissions. However, this advanced combustion mode is limited in the speed-load operating space due to high pressure rise rates for increased loads. Often higher loads are run at later combustion phasings to reduce pressure rise rates, however high cyclic variability (CV) can also be a limiting factor for late combustion phasings. This work presents advancements in the understanding of high variability dynamics in recompression HCCI as well as methods for control of CV and load transitions which typically encounter regions of high variability. Standard in-cylinder pressure based analysis methods are extended for use on high variability data. This includes a method of determining the trapped residual mass in real time. Determination of the residual mass is critical in recompression HCCI because of the combustion's sensitivity to the thermal energy contained within the residual charge. Trapping too much or little residuals can lead to ringing or misfires and CV, respectively. Various levels of CV are studied using large experimental data sets to ensure statistical relevance. The cycle resolved analysis of this data has allowed for the development of a predictive model of the variability associated with lean late phasing combustion. This model is used to develop control which can suppress cyclic variability at steady state. Knowledge about steady state control of CV and its oscillatory dynamics is further applied to the development of an adaptive controller. The adaptive controller uses a parameter estimation scheme in the feedforward component of a baseline midranging structure. The adaptive feedforward component enables the ability to correct for modeling errors and reduces parameterization effort. Experimental results demonstrate that the control is effective at navigating through large load transients while avoiding excess amounts of variability. Additionally, the actuators spend more time in a region of high authority when compared to non-adaptive control.PhDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/107231/1/larimore_1.pd

Transient Load-Speed Control in Multi-Cylinder Recompression HCCI Engines

Strict proposed fuel economy and emissions standards for automotive internal combustion engines have motivated the study of advanced low-temperature combustion modes that promise higher combustion efficiencies with low engine-out emissions. This work presents modeling and control results for one such combustion mode -- recompression homogeneous charge compression ignition (HCCI) combustion. Regulating desired charge properties in recompression HCCI involves the retention of a large amount of the residual charge between engine cycles, thus introducing significant inter-cycle feedback in the system. This work considers a baseline controller from literature, and proposes two improved model-based control strategies. The controllers use exhaust valve timing and fuel injection timings to track combustion phasings during transitions in the HCCI region of the multi-cylinder engine load-speed operating map. Fast and stable control of these transitions is demonstrated, which maximizes the length of stay in the HCCI region, and hence the efficiency benefit of advanced combustion. The baseline controller, which is a feedback-feedforward controller adapted from literature, is tuned using a low-order, discrete-time, control-oriented model that describes the stable, high efficiency HCCI region. The first improved control strategy augments the baseline controller with a reference or fuel governor that modifies transient fuel mass commands during large load transitions, when the possibility of future actuator constraint violations exists. This approach is shown in experiments to improve the combustion phasing and load responses, as well as prevent engine misfires. Issues with high cyclic variability during late phasing and low load conditions, and their impact on transient performance, are discussed. These issues are physically explained through recompression heat release caused due to unburned and recycled fuel. The control-oriented model is augmented with recompression heat release to predict the onset of the oscillatory, high variability region. The second improved control strategy uses this physical understanding to improve combustion phasing tracking performance. Transitions tested on a multicylinder HCCI engine include load transitions at fixed engine speeds, engine speed ramps at fixed load, simultaneous load and speed transitions, and select FTP75 drive-cycle transitions with high load slew rates. This improved model-based control strategy is proposed as a solution for the HCCI transient control problem.PhDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/107072/1/sjade_1.pd

Analysis and Control of Multimode Combustion Switching Sequence.

Highly dilute, low temperature combustion technologies, such as homogeneous charge compression ignition (HCCI), show significant improvements in internal combustion engine fuel efficiency and engine-out NOx emissions. These improvements, however, occur at limited operating range and conventional spark ignition (SI) combustion is still required to fulfill the driver's high torque demands. In consequence, such multimode engines involve discrete switches between the two distinct combustion modes. Such switches unfortunately require a finite amount of time, during which they exhibit penalties in efficiency. Along with its challenges, the design of such a novel system offers new degrees of freedom in terms of engine and aftertreatment specifications. Prior assessments of this technology were based on optimistic assumptions and neglected switching dynamics. Furthermore, emissions and driveability were not fully addressed. To this end, a comprehensive simulation framework, which accounts for above-mentioned penalties and incorporates interactions between multimode engine, driveline, and three-way catalyst (TWC), has been developed. Experimental data was used to parameterize a novel mode switch model, formulated as finite-state machine. This model was combined with supervisory controller designs, which made the switching decision. The associated drive cycle results were analyzed and it was seen that mode switches have significant influence on overall fuel economy, and the issue of drivability needs to be addressed within the supervisory strategy. After expanding the analysis to address emissions assuming a TWC, it was shown that, in practice, HCCI operation requires the depletion of the TWC's oxygen storage capacity (OSC). For large OSCs the resulting lean-rich cycling nullifies HCCI's original efficiency benefits. In addition, future emissions standards are still unlikely to be fulfilled, deeming a system consisting of such a multimode engine and TWC with generous OSC unfavorable. In view of these difficulties, the modeling framework was extended to a mild hybrid electric vehicle (HEV) allowing a prolonged operation in HCCI mode with associated fuel economy benefits during city driving. Further analysis on how to reduce NOx while maintaining fuel economy resulted in a counterintuitive suggestion. It was deemed beneficial to constrain the HCCI operation to a small region, exhibiting lowest NOx, while reducing instead of increasing the OSC.PhDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/116660/1/snuesch_1.pd

Adaptive Machine Learning for Modeling and Control of Non-Stationary, Near Chaotic Combustion in Real-Time.

Fuel efficient Homogeneous Charge Compression Ignition (HCCI) engine combustion phasing predictions must contend with non-linear chemistry, non-linear physics, near chaotic period doubling bifurcation(s), turbulent mixing, model parameters that can drift day-to-day, and air-fuel mixture state information that cannot typically be resolved on a cycle-to-cycle basis, especially during transients. Unlike many contemporary modeling approaches, this work does not attempt to solve for the myriad of combustion processes that are in practice unobservable in a metal engine. Instead, this work treads closely to physically measurable quantities within the framework of an abstract discrete dynamical system that is explicitly designed to capture many known combustion relationships, without ever explicitly solving for them. This abstract dynamical system is realized with an Extreme Learning Machine (ELM) that is extended to adapt to the combustion process from cycle-to-cycle with a new Weighted Ring-ELM algorithm. Combined, the above techniques are shown to provide unprecedented cycle-to-cycle predictive capability during transients, near chaotic combustion, and at steady-state, right up to complete misfire. These predictions only require adding an in-cylinder pressure sensor to production engines, which could cost as little as $13 per cylinder. By design, the framework is computationally efficient, and the approach is shown to predict combustion in sub-millisecond real-time using only an iPhone generation 1 processor (the$35 Raspberry Pi). This is in stark contrast to supercomputer approaches that model down to the minutiae of individual reactions but have yet to demonstrate such fidelity against cycle-to-cycle experiments. Finally, the feasibility of cycle-to-cycle model predictive control with this real-time framework is demonstrated.PhDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/111333/1/vaughana_1.pd