Control-oriented modeling, validation, and analysis of a natural gas engine architecture

In order to improve performance and meet increasingly tight emissions regulations, engine manufacturers must improve algorithms used to control the engine. One possible strategy is to utilize centralized control algorithms that take into account the coupled interactions between inputs and outputs. Implementing a centralized control strategy often requires some kind of dynamic model of the system, which is a primary motivation for modeling efforts in this thesis. In a methodical fashion, this thesis derives a control model for a natural gas engine architecture and validates this control model against reference data in simulation. Additionally, this thesis performs control-oriented analysis on a state-space model provided by Caterpillar to determine the engine’s suitability to decentralized control. Based on the results of the control-oriented analysis, the thesis demonstrates how a decentralized control framework can be implemented. The first study declares a set of seven state variables that characterize the operation of the engine. The engine of interest runs on natural gas and is used in power generation applications. Additionally, this study models all mass flow rates and power terms as functions of the selected state variables. These models are then validated against truth-reference data. This study also explicitly states the assumptions and simplifications that correspond to each of the models. The second study derives dynamic equations for each of the seven state variables via a first-principles approach. The dynamic state equations contain expressions for mass flow rates and power that were modeled in the first study. This study then numerically validates the entire state-space model by exercising control inputs from reference data on it. Together, the seven state equations effectively serve as a control model that can be used for controller synthesis. The goal of the first two studies is to demonstrate a procedure for obtaining a control model for an engine architecture, not to obtain a high-fidelity simulation model. The third study demonstrates control-oriented analysis on a state-space model provided by Caterpillar. The relative gain array (RGA) is used to show that the system is well-suited is for decentralized control. This study implements a decentralized control structure on the state-space model provided by Caterpillar and validates, in simulation, its ability to achieve reference tracking for desired outputs

Model Predictive Control of Modern High-Degree-of-Freedom Turbocharged Spark Ignited Engines with External Cooled EGR

The efficiency of modern downsized SI engines has been significantly improved using cooled Low-Pressure Exhaust Gas Recirculation, Turbocharging and Variable Valve Timing actuation. Control of these sub-systems is challenging due to their inter-dependence and the increased number of actuators associated with engine control. Much research has been done on developing algorithms which improve the transient turbocharged engine response without affecting fuel-economy. With the addition of newer technologies like external cooled EGR the control complexity has increased exponentially. This research proposes a methodology to evaluate the ability of a Model Predictive Controller to coordinate engine and air-path actuators simultaneously. A semi-physical engine model has been developed and analyzed for non-linearity. The computational burden of implementing this control law has been addressed by utilizing a semi-physical engine system model and basic analytical differentiation. The resulting linearization process requires less than 10% of the time required for widely used numerical linearization approach. Based on this approach a Nonlinear MPC-Quadratic Program has been formulated and solved with preliminary validation applied to a 1D Engine model followed by implementation on an experimental rapid prototyping control system. The MPC based control demonstrates the ability to co-ordinate different engine and air-path actuators simultaneously for torque-tracking with minimal constraint violation. Avenues for further improvement have been identified and discussed

MODEL-BASED CONTROL OF AN RCCI ENGINE

Reactivity controlled compression ignition (RCCI) is a combustion strategy that offers high fuel conversion efficiency and near zero emissions of NOx and soot which can help in improving fuel economy in mobile and stationary internal combustion engine (ICE) applications and at the same time lower engine-out emissions. One of the main challenges associated with RCCI combustion is the difficulty in simultaneously controlling combustion phasing, engine load, and cyclic variability during transient engine operations. This thesis focuses on developing model based controllers for cycle-to-cycle combustion phasing and load control during transient operations. A control oriented model (COM) is developed by using mean value models to predict start of combustion (SOC) and crank angle of 50% mass fraction burn (CA50). The COM is validated using transient data from an experimental RCCI engine. The validation results show that the COM is able to capture the experimental trends in CA50 and indicated mean effective pressure (IMEP). The COM is then used to develop a linear quadratic integral (LQI) controller and model predictive controllers (MPC). Premixed ratio (PR) and start of injection (SOI) are the control variables used to control CA50, while the total fuel quantity (FQ) is the engine variable used to control load. The selection between PR and SOI is done using a sensitivity based algorithm. Experimental validation results for reference tracking using LQI and MPC show that the desired CA50 and IMEP can be attained in a single cycle during step-up and step-down transients and yield an average error of less than 1.6 crank angle degrees (CAD) in the CA50 and less than 35 kPa in the IMEP. This thesis presents the first study in the literature to design and implement LQI and MPC combustion controllers for RCCI engines

Flexible and robust control of heavy duty diesel engine airpath using data driven disturbance observers and GPR models

Diesel engine airpath control is crucial for modern engine development due to increasingly stringent emission regulations. This thesis aims to develop and validate a exible and robust control approach to this problem for speci cally heavy-duty engines. It focuses on estimation and control algorithms that are implementable to the current and next generation commercial electronic control units (ECU). To this end, targeting the control units in service, a data driven disturbance observer (DOB) is developed and applied for mass air ow (MAF) and manifold absolute pressure (MAP) tracking control via exhaust gas recirculation (EGR) valve and variable geometry turbine (VGT) vane. Its performance bene ts are demonstrated on the physical engine model for concept evaluation. The proposed DOB integrated with a discrete-time sliding mode controller is applied to the serial level engine control unit. Real engine performance is validated with the legal emission test cycle (WHTC - World Harmonized Transient Cycle) for heavy-duty engines and comparison with a commercially available controller is performed, and far better tracking results are obtained. Further studies are conducted in order to utilize capabilities of the next generation control units. Gaussian process regression (GPR) models are popular in automotive industry especially for emissions modeling but have not found widespread applications in airpath control yet. This thesis presents a GPR modeling of diesel engine airpath components as well as controller designs and their applications based on the developed models. Proposed GPR based feedforward and feedback controllers are validated with available physical engine models and the results have been very promisin

Low Complexity Model Predictive Control of a Diesel Engine Airpath.

The diesel air path (DAP) system has been traditionally challenging to control due to its highly coupled nonlinear behavior and the need for constraints to be considered for driveability and emissions. An advanced control technology, model predictive control (MPC), has been viewed as a way to handle these challenges, however, current MPC strategies for the DAP are still limited due to the very limited computational resources in engine control units (ECU). A low complexity MPC controller for the DAP system is developed in this dissertation where, by "low complexity," it is meant that the MPC controller achieves tracking and constraint enforcement objectives and can be executed on a modern ECU within 200 microseconds, a computation budget set by Toyota Motor Corporation. First, an explicit MPC design is developed for the DAP. Compared to previous explicit MPC examples for the DAP, a significant reduction in computational complexity is achieved. This complexity reduction is accomplished through, first, a novel strategy of intermittent constraint enforcement. Then, through a novel strategy of gain scheduling explicit MPC, the memory usage of the controller is further reduced and closed-loop tracking performance is improved. Finally, a robust version of the MPC design is developed which is able to enforce constraints in the presence of disturbances without a significant increase in computational complexity compared to non-robust MPC. The ability of the controller to track set-points and enforce constraints is demonstrated in both simulations and experiments. A number of theoretical results pertaining to the gain scheduling strategy is also developed. Second, a nonlinear MPC (NMPC) strategy for the DAP is developed. Through various innovations, a NMPC controller for the DAP is constructed that is not necessarily any more computationally complex than linear explicit MPC and is characterized by a very streamlined process for implementation and calibration. A significant reduction in computational complexity is achieved through the novel combination of Kantorovich's method and constrained NMPC. Zero-offset steady state tracking is achieved through a novel NMPC problem formulation, rate-based NMPC. A comparison of various NMPC strategies and developments is presented illustrating how a low complexity NMPC strategy can be achieved.PhDAerospace EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/120832/1/huxuli_1.pd

Air-management and fueling strategy for diesel engines from multi-layer control perspective

This paper proposes a novel control design procedure for air management and fueling strategy (AMFS) of diesel engines in lights of a multi-layer control structure (MLCS). Furthermore, novel sufficient stability conditions in the form of linear matrix inequalities are derived (using slack variables to reduce the conservativeness) for grid-based linear parameter-varying systems. The gain-scheduled controller for AMFS is designed to track a reference torquetrajectory requested by higher control layers from MLCS, with the objective of minimizing diesel consumption and pollutants\u27 emissions. For controller design a reduced order grid-based linear parameter-varying model is obtained from the detailed benchmark model published by Eriksson et al. (2016). The controller is validated on the benchmark model using the road profile S\uf6der\ue4lje-Norrk\uf6ping

Thermodynamic analysis, modelling and control of a novel hybrid propulsion system

Stringent emission regulations imposed by governments and depleting fossil fuel reserves have promoted the development of the automotive industry towards novel technologies. Various types of hybrid power plants for transport and stationary applications have emerged. The methodology of design and development of such power plants varies according to power producing components used in the systems. The practical feasibility of such power plants is a pre-requisite to any further development. This work presents thermodynamic analysis and modelling of such a novel power plant, assesses its feasibility and further discusses the development of a suitable control system. The proposed system consists of a hybrid configuration of a solid oxide fuel cell and IC engine as the main power producing components. A reformer supplies fuel gas to the fuel cell while the IC engine is supplied with a liquid fuel. The excess fuel from the fuel cell anode and the oxygen-depleted air from cathode of the fuel cell are also supplied to the engine. This gas mixture is aspirated into the engine with the balance of energy provided by the liquid fuel. The fuel cell exhaust streams are used to condition the fuel in the engine to ensure minimum pollutants and improved engine performance. Both, fuel cell and engine share the load on the system. The fuel cell operates on a base load while the engine handles majority of the transient load. This system is particularly suitable for a delivery truck or a bus cycle. Models of the system components reformer, solid oxide fuel cell, IC engine and turbocharger were developed to understand their steady state and dynamic behaviour. These models were validated against sources of literature and used to predict the effect of different operating conditions for each component. The main control parameters for each component were derived from these models. A first law analysis of the system at steady state was conducted to identify optimum operating region, verify feasibility and efficiency improvement of the system. The results suggested reduced engine fuel consumption and a 10 % improvement in system efficiency over the conventional diesel engines. Further, a second law analysis was conducted to determine the key areas of exergy losses and the rational efficiency of the system at full load operating conditions. The results indicate a rational efficiency of 25.4 % for the system. Sensitivity to changes in internal exergy losses on the system work potential was also determined. The exergy analysis indicates a potential for process optimisation as well as design improvements. This analysis provides a basis for the development of a novel control strategy based on exergy analysis and finite-time thermodynamics. A dynamic simulation of the control oriented system model identified the transient response and control parameters for the system. Based on these results, control systems were developed based on feedback control and model predictive control theories. These controllers mainly focus on air and fuel path management within the system and show an improved transient response for the system. In a hierarchical control structure for the system, the feedback controllers or the model predictive controller can perform local optimisation for the system, while a supervisory controller can perform global optimisation. The objective of the supervisory controller is to determining the load distribution between the fuel cell and the engine. A development strategy for such a top-level supervisory controller for the system is proposed. The hybrid power plant proposed in this thesis shows potential for application for transport and stationary power production with reduced emissions and fuel consumption. The first and second law of thermodynamics can both contribute to the development of a comprehensive control system. This work integrates research areas of powertrain design, thermodynamic analysis and control design. The development and design strategy followed for such a novel hybrid power plant can be useful to assess the potential of other hybrid systems as well