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

MODELLING AND CONTROL OF COMBUSTION PHASING OF AN RCCI ENGINE

Reactivity controlled compression ignition (RCCI) is a novel combustion strategy introduced to achieve near-zero NOx and soot emissions while maintaining diesel-like efficiencies. Meanwhile, precise control of combustion phasing is a key in realization of high fuel conversion efficiency as well as meeting stringent emission standards. Model-based control of RCCI combustion phasing is a great tool for real-time control during transient operation of the engine, which requires a computationally efficient combustion model that encompasses factors such as, injection timings, fuel blend composition and reactivity. In this thesis, physics-based models are developed to predict the combustion metrics of an RCCI engine. A mean value control-oriented model (COM) of RCCI is then developed by combining the auto-ignition model, the burn duration model, and a Wiebe function to predict combustion phasing. Development of a model-based controller requires a dynamic model which can predict engine operation, i.e., estimation of combustion metrics, on a cycle-to-cycle basis. Hence, the mean-value model is extended to encompass the full-cycle engine operation by including the expansion and exhaust strokes. In addition, the dynamics stemming from the thermal coupling between cycles are accounted for, that results in a dynamic RCCI control-oriented model capable of predicting the transient operation of the engine. This model is then simplified and linearized in order to develop a linear observer-based feedback controller to control the combustion phasing using the premixed ratio (the ratio of the PFI fuel to the total fuel injected) of the gasoline/diesel fuel. The designed controller depicts an accurate tracking performance of the desired combustion phasing and successfully rejects external disturbances in engine operating conditions

Predictive Control of HCCI Engines Using Physical Models

Homogeneous Charge Compression Ignition (HCCI) is a promising internal combustion engine concept. It holds promise of combining low emission levels with high efficiency. However, as ignition timing in HCCI operation lacks direct actuation and is highly sensitive to operating conditions and disturbances, robust closed-loop control is necessary. To facilitate control design and allow for porting of both models and the resulting controllers between different engines, physics-based mathematical models of HCCI are of interest. This thesis presents work on a physical model of HCCI including cylinder wall temperature and evaluates predictive controllers based on linearizations of the model. The model was derived using first principles modeling and is given on a cycle-to-cycle basis. Measurement data including cylinder wall temperature measurements was used for calibration and validation of the model. A predictive controller for combined control of work output and combustion phasing was designed and evaluated in simulation. The resulting controller was validated on a real engine. The last part of the work was an experimental evaluation of predictive combustion phasing control. The control performance was evaluated in terms of response time and steady-state output variance

A Study Model Predictive Control for Spark Ignition Engine Management and Testing

Pressure to improve spark-ignition (SI) engine fuel economy has driven thedevelopment and integration of many control actuators, creating complex controlsystems. Integration of a high number of control actuators into traditional map basedcontrollers creates tremendous challenges since each actuator exponentially increasescalibration time and investment. Model Predictive Control (MPC) strategies have thepotential to better manage this high complexity since they provide near-optimal controlactions based on system models. This research work focuses on investigating somepractical issues of applying MPC with SI engine control and testing.Starting from one dimensional combustion phasing control using spark timing(SPKT), this dissertation discusses challenges of computing the optimal control actionswith complex engine models. A nonlinear optimization is formulated to compute thedesired spark timing in real time, while considering knock and combustion variationconstraints. Three numerical approaches are proposed to directly utilize complex high-fidelity combustion models to find the optimal SPKT. A model based combustionphasing estimator that considers the influence of cycle-by-cycle combustion variations isalso integrated into the control system, making feedback and adaption functions possible.An MPC based engine management system with a higher number of controldimensions is also investigated. The control objective is manipulating throttle, externalEGR valve and SPKT to provide demanded torque (IMEP) output with minimum fuelconsumption. A cascaded control structure is introduced to simplify the formulation and solution of the MPC problem that solves for desired control actions. Sequential quadratic programming (SQP) MPC is applied to solve the nonlinear optimization problem in real time. A real-time linearization technique is used to formulate the sub-QP problems with the complex high dimensional engine system. Techniques to simplify the formulation of SQP and improve its convergence performance are also discussed in the context of tracking MPC. Strategies to accelerate online quadratic programming (QP) are explored. It is proposed to use pattern recognition techniques to “warm-start” active set QP algorithms for general linear MPC applications. The proposed linear time varying (LTV) MPC is used in Engine-in-Loop (EIL) testing to mimic the pedal actuations of human drivers who foresee the incoming traffic conditions. For SQP applications, the MPC is initialized with optimal control actions predicted by an ANN. Both proposed MPC methods significantly reduce execution time with minimal additional memory requirement

Low-Pressure EGR in Spark-Ignition Engines: Combustion Effects, System Optimization, Transients & Estimation Algorithms

Low-displacement turbocharged spark-ignition engines have become the dominant choice of auto makers in the effort to meet the increasingly stringent emission regulations and fuel efficiency targets. Low-Pressure cooled Exhaust Gas Recirculation introduces important efficiency benefits and complements the shortcomings of highly boosted engines. The main drawback of these configurations is the long air-path which may cause over-dilution limitations during transient operation. The pulsating exhaust environment and the low available pressure differential to drive the recirculation impose additional challenges with respect to feed-forward EGR estimation accuracy. For these reasons, these systems are currently implemented through calibration with less-than-optimum EGR dilution in order to ensure stable operation under all conditions. However, this technique introduces efficiency penalties. Aiming to exploit the full potential of this technology, the goal is to address these challenges and allow operation with near-optimum EGR dilution. This study is focused on three major areas regarding the implementation of Low-Pressure EGR systems: Combustion effects, benefits and constraints System optimization and transient operation Estimation and adaptation Results from system optimization show that fuel efficiency benefits range from 2% – 3% over drive cycles through pumping and heat loss reduction, and up to 16% or more at higher loads through knock mitigation and fuel enrichment elimination. Soot emissions are also significantly reduced with cooled EGR. Regarding the transient challenges, a methodology that correlates experimental data with simulation results is developed to identify over-dilution limitations related to the engine’s dilution tolerance. Different strategies are proposed to mitigate these issues, including a Neural Network-actuated VVT that controls the internal residual and increases the over-dilution tolerance by 3% of absolute EGR. Physics-based estimation algorithms are also developed, including an exhaust pressure/temperature model which is validated through real-time transient experiments and eliminates the need for exhaust sensors. Furthermore, the installation of an intake oxygen sensor is investigated and an adaptation algorithm based on an Extended Kalman Filter is created. This algorithm delivers short-term and long-term corrections to feed-forward EGR models achieving a final estimation error of less than 1%. The combination of the proposed methodologies, strategies and algorithms allows the implementation of near-optimum EGR dilution and translates to fuel efficiency benefits ranging from 1% at low-load up to 10% at high-load operation over the current state-of-the-art

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

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-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

Employment of Real-Time/FPGA Architectures for Test and Control of Automotive Engines

Nowadays the production of increasingly complex and electrified vehicles requires the implementation of new control and monitoring systems. This reason, together with the tendency of moving rapidly from the test bench to the vehicle, leads to a landscape that requires the development of embedded hardware and software to face the application effectively and efficiently. The development of application-based software on real-time/FPGA hardware could be a good answer for these challenges: FPGA grants parallel low-level and high-speed calculation/timing, while the Real-Time processor can handle high-level calculation layers, logging and communication functions with determinism. Thanks to the software flexibility and small dimensions, these architectures can find a perfect collocation as engine RCP (Rapid Control Prototyping) units and as smart data logger/analyser, both for test bench and on vehicle application. Efforts have been done for building a base architecture with common functionalities capable of easily hosting application-specific control code. Several case studies originating in this scenario will be shown; dedicated solutions for protype applications have been developed exploiting a real-time/FPGA architecture as ECU (Engine Control Unit) and custom RCP functionalities, such as water injection and testing hydraulic brake control