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

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

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

Model predictive emissions control of a diesel engine airpath: Design and experimental evaluation

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/163480/2/rnc5188.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/163480/1/rnc5188_am.pd

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

Review of air fuel ratio prediction and control methods

Air pollution is one of main challenging issues nowadays that researchers have been trying to address.The emissions of vehicle engine exhausts are responsible for 50 percent of air pollution. Different types of emissions emit from vehicles including carbon monoxide, hydrocarbons, NOX, and so on. There is a tendency to develop strategies of engine control which work in a fast way. Accomplishing this task will result in a decrease in emissions which coupled with the fuel composition can bring about the best performance of the vehicle engine.Controlling the Air-Fuel Ratio (AFR) is necessary, because the AFR has an enormous impact on the effectiveness of the fuel and reduction of emissions.This paper is aimed at reviewing the recent studies on the prediction and control of the AFR, as a bulk of research works with different approaches, was conducted in this area.These approaches include both classical and modern methods, namely Artificial Neural Networks (ANN), Fuzzy Logic, and Neuro-Fuzzy Systems are described in this paper.The strength and the weakness of individual approaches will be discussed at length

Model-Based Control of Gasoline Partially Premixed Combustion

Partially Premixed Combustion (PPC) is an internal combustion engine concept that aims to yield low NOx and soot emission levels together with high engine efficiency. PPC belongs to the class of low temperature combustion concepts where the ignition delay is prolonged in order to promote the air-fuel-mixture homogeneity in the combustion chamber at the start of combustion. A more homogeneous combustion process in combination with high exhaust-gas recirculation (EGR) ratios gives lower combustion temperatures and thus decreased NOx and soot formation. The ignition delay is mainly controlled by temperature, gas-mixture composition, fuel type and fuel-injection timing. It has been shown that PPC run on gasoline fuel can provide sufficient ignition delays in conventional compression-ignition engines. The PPC concept differs from conventional direct-injection diesel combustion because of its increased sensitivity to intake conditions, its decreased combustion-phasing controllability and its high pressure-rise rates related to premixed combustion, this puts higher demands on the engine control system. This thesis investigates model predictive control (MPC) of PPC with the use of in-cylinder pressure sensors. Online heat-release analysis is used for the detection of the combustion phasing and the ignition delay that function as combustion-feedback signals. It is shown that the heat-release analysis could be automatically calibrated using nonlinear estimation methods, the heat-release analysis is also a central part of a presented online pressure-prediction method which can be used for combustion-timing optimization. Low-order autoignition models are studied and compared for the purpose of model-based control of the ignition-delay, the results show that simple mathematical models are sufficient when anipulating the intake-manifold conditions. The results also show that the relation between the injection timing and the ignition delay is not completely captured by these types of models when the injection timing is close to top-dead-center. Simultaneous control of the ignition delay and the combustion phasing using a dual-path EGR system, thermal management and fuel injection timings is studied and a control design is presented and evaluated experimentally. Closed-loop control of the pressure-rise rate using a pilot fuel injection is also studied and the multiple fuel-injection properties are characterized experimentally. Experiments show that the main-fuel injection controls the combustion timing and that the pilot-injection fuel could be used to decrease the main fuel injection ignition delay and thus the pressure-rise rate. The controllability of the pressure-rise rate was shown to be higher when the pilot injection was located close to the main-fuel injection. A pressure-rise-rate controller is presented and evaluated experimentally. All experiments presented in this thesis were conducted on a Scania D13 production engine with a modified gas-exchange system, the fuel used was a mixture of 80 % gasoline and 20 % N-heptane (by volume)

Optimal Control for Automotive Powertrain Applications

Optimal Control (OC) is essentially a mathematical extremal problem. The procedure consists on the definition of a criterion to minimize (or maximize), some constraints that must be fulfilled and boundary conditions or disturbances affecting to the system behavior. The OC theory supplies methods to derive a control trajectory that minimizes (or maximizes) that criterion. This dissertation addresses the application of OC to automotive control problems at the powertrain level, with emphasis on the internal combustion engine. The necessary tools are an optimization method and a mathematical representation of the powertrain. Thus, the OC theory is reviewed with a quantitative analysis of the advantages and drawbacks of the three optimization methods available in literature: dynamic programming, Pontryagin minimum principle and direct methods. Implementation algorithms for these three methods are developed and described in detail. In addition to that, an experimentally validated dynamic powertrain model is developed, comprising longitudinal vehicle dynamics, electrical motor and battery models, and a mean value engine model. OC can be utilized for three different purposes: 1. Applied control, when all boundaries can be accurately defined. The engine control is addressed with this approach assuming that a the driving cycle is known in advance, translating into a large mathematical problem. Two specific cases are studied: the management of a dual-loop EGR system, and the full control of engine actuators, namely fueling rate, SOI, EGR and VGT settings. 2. Derivation of near-optimal control rules, to be used if some disturbances are unknown. In this context, cycle-specific engine calibrations calculation, and a stochastic feedback control for power-split management in hybrid vehicles are analyzed. 3. Use of OC trajectories as a benchmark or base line to improve the system design and efficiency with an objective criterion. OC is used to optimize the heat release law of a diesel engine and to size a hybrid powertrain with a further cost analysis. OC strategies have been applied experimentally in the works related to the internal combustion engine, showing significant improvements but non-negligible difficulties, which are analyzed and discussed. The methods developed in this dissertation are general and can be extended to other criteria if appropriate models are available.El Control Óptimo (CO) es esencialmente un problema matemático de búsqueda de extremos, consistente en la definición de un criterio a minimizar (o maximizar), restricciones que deben satisfacerse y condiciones de contorno que afectan al sistema. La teoría de CO ofrece métodos para derivar una trayectoria de control que minimiza (o maximiza) ese criterio. Esta Tesis trata la aplicación del CO en automoción, y especialmente en el motor de combustión interna. Las herramientas necesarias son un método de optimización y una representación matemática de la planta motriz. Para ello, se realiza un análisis cuantitativo de las ventajas e inconvenientes de los tres métodos de optimización existentes en la literatura: programación dinámica, principio mínimo de Pontryagin y métodos directos. Se desarrollan y describen los algoritmos para implementar estos métodos así como un modelo de planta motriz, validado experimentalmente, que incluye la dinámica longitudinal del vehículo, modelos para el motor eléctrico y las baterías, y un modelo de motor de combustión de valores medios. El CO puede utilizarse para tres objetivos distintos: 1. Control aplicado, en caso de que las condiciones de contorno estén definidas. Puede aplicarse al control del motor de combustión para un ciclo de conducción dado, traduciéndose en un problema matemático de grandes dimensiones. Se estudian dos casos particulares: la gestión de un sistema de EGR de doble lazo, y el control completo del motor, en particular de las consignas de inyección, SOI, EGR y VGT. 2. Obtención de reglas de control cuasi-óptimas, aplicables en casos en los que no todas las perturbaciones se conocen. A este respecto, se analizan el cálculo de calibraciones de motor específicas para un ciclo, y la gestión energética de un vehículo híbrido mediante un control estocástico en bucle cerrado. 3. Empleo de trayectorias de CO como comparativa o referencia para tareas de diseño y mejora, ofreciendo un criterio objetivo. La ley de combustión así como el dimensionado de una planta motriz híbrida se optimizan mediante el uso de CO. Las estrategias de CO han sido aplicadas experimentalmente en los trabajos referentes al motor de combustión, poniendo de manifiesto sus ventajas sustanciales, pero también analizando dificultades y líneas de actuación para superarlas. Los métodos desarrollados en esta Tesis Doctoral son generales y aplicables a otros criterios si se dispone de los modelos adecuados.El Control Òptim (CO) és essencialment un problema matemàtic de cerca d'extrems, que consisteix en la definició d'un criteri a minimitzar (o maximitzar), restriccions que es deuen satisfer i condicions de contorn que afecten el sistema. La teoria de CO ofereix mètodes per a derivar una trajectòria de control que minimitza (o maximitza) aquest criteri. Aquesta Tesi tracta l'aplicació del CO en automoció i especialment al motor de combustió interna. Les ferramentes necessàries són un mètode d'optimització i una representació matemàtica de la planta motriu. Per a això, es realitza una anàlisi quantitatiu dels avantatges i inconvenients dels tres mètodes d'optimització existents a la literatura: programació dinàmica, principi mínim de Pontryagin i mètodes directes. Es desenvolupen i descriuen els algoritmes per a implementar aquests mètodes així com un model de planta motriu, validat experimentalment, que inclou la dinàmica longitudinal del vehicle, models per al motor elèctric i les bateries, i un model de motor de combustió de valors mitjans. El CO es pot utilitzar per a tres objectius diferents: 1. Control aplicat, en cas que les condicions de contorn estiguen definides. Es pot aplicar al control del motor de combustió per a un cicle de conducció particular, traduint-se en un problema matemàtic de grans dimensions. S'estudien dos casos particulars: la gestió d'un sistema d'EGR de doble llaç, i el control complet del motor, particularment de les consignes d'injecció, SOI, EGR i VGT. 2. Obtenció de regles de control quasi-òptimes, aplicables als casos on no totes les pertorbacions són conegudes. A aquest respecte, s'analitzen el càlcul de calibratges específics de motor per a un cicle, i la gestió energètica d'un vehicle híbrid mitjançant un control estocàstic en bucle tancat. 3. Utilització de trajectòries de CO com comparativa o referència per a tasques de disseny i millora, oferint un criteri objectiu. La llei de combustió així com el dimensionament d'una planta motriu híbrida s'optimitzen mitjançant l'ús de CO. Les estratègies de CO han sigut aplicades experimentalment als treballs referents al motor de combustió, manifestant els seus substancials avantatges, però també analitzant dificultats i línies d'actuació per superar-les. Els mètodes desenvolupats a aquesta Tesi Doctoral són generals i aplicables a uns altres criteris si es disposen dels models adequats.Reig Bernad, A. (2017). Optimal Control for Automotive Powertrain Applications [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/90624TESI

Model-Guided Data-Driven Optimization and Control for Internal Combustion Engine Systems

The incorporation of electronic components into modern Internal Combustion, IC, engine systems have facilitated the reduction of fuel consumption and emission from IC engine operations. As more mechanical functions are being replaced by electric or electronic devices, the IC engine systems are becoming more complex in structure. Sophisticated control strategies are called in to help the engine systems meet the drivability demands and to comply with the emission regulations. Different model-based or data-driven algorithms have been applied to the optimization and control of IC engine systems. For the conventional model-based algorithms, the accuracy of the applied system models has a crucial impact on the quality of the feedback system performance. With computable analytic solutions and a good estimation of the real physical processes, the model-based control embedded systems are able to achieve good transient performances. However, the analytic solutions of some nonlinear models are difficult to obtain. Even if the solutions are available, because of the presence of unavoidable modeling uncertainties, the model-based controllers are designed conservatively

A STUDY OF MODEL-BASED CONTROL STRATEGY FOR A GASOLINE TURBOCHARGED DIRECT INJECTION SPARK IGNITED ENGINE

To meet increasingly stringent fuel economy and emissions legislation, more advanced technologies have been added to spark-ignition (SI) engines, thus exponentially increase the complexity and calibration work of traditional map-based engine control. To achieve better engine performance without introducing significant calibration efforts and make the developed control system easily adapt to future engines upgrades and designs, this research proposes a model-based optimal control system for cycle-by-cycle Gasoline Turbocharged Direct Injection (GTDI) SI engine control, which aims to deliver the requested torque output and operate the engine to achieve the best achievable fuel economy and minimum emission under wide range of engine operating conditions. This research develops a model-based ignition timing prediction strategy for combustion phasing (crank angle of fifty percent of the fuel burned, CA50) control. A control-oriented combustion model is developed to predict burn duration from ignition timing to CA50. Using the predicted burn duration, the ignition timing needed for the upcoming cycle to track optimal target CA50 is calculated by a dynamic ignition timing prediction algorithm. A Recursive-Least-Square (RLS) with Variable Forgetting Factor (VFF) based adaptation algorithm is proposed to handle operating-point-dependent model errors caused by inherent errors resulting from modeling assumptions and limited calibration points, which helps to ensure the proper performance of model-based ignition timing prediction strategy throughout the entire engine lifetime. Using the adaptive combustion model, an Adaptive Extended Kalman Filter (AEKF) based CA50 observer is developed to provide filtered CA50 estimation from cyclic variations for the closed-loop combustion phasing control. An economic nonlinear model predictive controller (E-NMPC) based GTDI SI engine control system is developed to simultaneously achieve three objectives: tracking the requested net indicated mean effective pressure (IMEPn), minimizing the SFC, and reducing NOx emissions. The developed E-NMPC engine control system can achieve the above objectives by controlling throttle position, IVC timing, CA50, exhaust valve opening (EVO) timing, and wastegate position at the same time without violating engine operating constraints. A control-oriented engine model is developed and integrated into the E-NMPC to predict future engine behaviors. A high-fidelity 1-D GT-POWER engine model is developed and used as the plant model to tune and validate the developed control system. The performance of the entire model-based engine control system is examined through the software-in-the-loop (SIL) simulation using on-road vehicle test data