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

Optimal control of a motor-integrated hybrid powertrain for a two-wheeled vehicle suitable for personal transportation

The present research aims to propose an optimized configuration of the motor integrated power-train with an optimal controller suitable for small power-train based two wheeler automobile which can increase the system level efficiency without affecting drivability. This work will be the foundation for realizing the system in a production ready vehicle for the two wheeler OEM TVS Motor Company in India. A detailed power-train model is developed (from first principles) for the scooter vehicle, which is powered by a 110 cc spark ignition (SI) engine and coupled with two types of transmission, a continuous variable transmission (CVT) and a 4-speed manual transmission (MT). Both models are capable of simulating torque and NOx emission output of the SI engine and dynamic response of the full power-train. The torque production and emission outputs of the model are compared with experimental results available from TVS Motor Company. The CVT gear ratio model is developed using an indirect method and an analytical model. Both types of powertrain models are applied to perform a simulated study of fuel consumption, NOx emission and drivability study for a particular vehicle platform. In the next stage of work, the mathematical model for a brush-less direct current machine (BLDC) with the drive system and Li-Ion battery are developed. The models are verified and calibrated with the experimental results from TVS Motor Company. The BLDC machine is integrated with both the CVT and MT powertrain models in parallel hybrid configurations and a drive cycle simulation is conducted for different static assist levels by the electrical machines. The initial test confirms the need of optimal sizing of the powertrain components as well as an optimal control system. The detailed model of the powertrain is converted to a control-oriented model which is suitable for optimal control. This is followed by multi-objective optimization of different components of the motor-integrated powertrain using a single function as well as Pareto-Optimal methods. The objective function for the multi-objective optimization is proposed to reduce the fuel consumption with battery charge sustainability with least impact on the increase of financial cost and weight of the vehicle. The optimization is conducted by a nested methodology that involves Particle Swarm Optimization and a Non-dominated sorting genetic algorithm where, concurrently, a global optimal control is developed corresponding to the multi-objective design. The global optimal controller is designed using dynamic programming. The research is concluded with an optimal controller developed using the hp-collocation method. The objective function of the dynamic programming method and hp-collocation method is proposed to reduce fuel consumption with battery charge sustainability.Open Acces

Neuro_Dynamic Programming and Reinforcement Learning for Optimal Energy Management of a Series Hydraulic Hybrid Vehicle Considering Engine Transient Emissions.

Sequential decision problems under uncertainty are encountered in various fields such as optimal control and operations research. In this dissertation, Neuro-Dynamic Programming (NDP) and Reinforcement Learning (RL) are applied to address policy optimization problems with multiple objectives and large design state space. Dynamic Programming (DP) is well suited for determining an optimal solution for constrained nonlinear model based systems. However, DP suffers from curse of dimensionality i.e. computational effort grows exponentially with state space. The new algorithms address this problem and enable practical application of DP to a much broader range of problems. The other contribution is to design fast and computationally efficient transient emission models. The power management problem for a hybrid vehicle can be formulated as an infinite time horizon stochastic sequential decision-making problem. In the past, policy optimization has been applied successfully to design optimal supervisory controller for best fuel economy. Static emissions have been considered too but engine research has shown that transient operation can have significant impact on real-world emissions. Modeling transient emissions results in addition of more states. Therefore, the problem with multiple objectives i.e. minimize fuel consumption and transient particulate and NOX emissions, becomes computationally intractable by DP. This research captures the insight with models and brings it into the supervisory controller design. A self-learning supervisory controller is designed based on the principles of NDP and RL. The controller starts “naïve” i.e. with no knowledge to control the onboard power but learns to do so in an optimal manner after interacting with the system. The controller tries to minimize multiple objectives and continues to evolve until a global solution is achieved. Virtual sensors for predicting real-time transient particulate and NOX emissions are developed using neuro-fuzzy modeling technique, which utilizes a divide-and-conquer strategy. The highly nonlinear engine operating space is partitioned into smaller subspaces and a separate local model is trained to for each subspace. Finally, the supervisory controller along with virtual emission sensors is implemented and evaluated using the Engine-In-the-Loop (EIL) setup. EIL is a unique facility to systematically evaluate control methodologies through concurrent running of real engine and a virtual hybrid powertrain.Ph.D.Mechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/89829/1/rajit_1.pd

In-Cylinder Pressure-Based Control of Premixed Dual-Fuel Combustion

[ES] La actual crisis climática ha instado a la comunidad investigadora y a los fabricantes a brindar soluciones para hacer que el sector del transporte sea más sostenible. De entre las diversas tecnologías propuestas, la combustión a baja temperatura ha sido objeto de una extensa investigación. La combustión premezclada dual-fuel es uno de los conceptos que abordan el compromiso de NOx-hollín en motores de encendido por compresión manteniendo alta eficiencia térmica. Esta combustión hace uso de dos combustibles con diferentes reactividades para mejorar la controlabilidad de este modo de combustión en un amplio rango de funcionamiento. De manera similar a todos los modos de combustión premezclados, esta combustión es sensible a las condiciones de operación y suele estar sujeta a variabilidad cíclica con gradientes de presión significativos. En consecuencia, se requieren estrategias de control avanzadas para garantizar un funcionamiento seguro y preciso del motor. El control en bucle cerrado es una herramienta eficaz para abordar los desafíos que plantea la combustión premezclada dual-fuel. En este tipo de control, para mantener el funcionamiento deseado, las acciones de control se adaptan y corrigen a partir de una retroalimentación con las señales de salida del motor. Esta tesis presenta estrategias de control basadas en la medición de la señal de presión en el cilindro, aplicadas a motores de combustión premezclada dual-fuel. En ella se resuelven diversos aspectos del funcionamiento del motor mediante el diseño de controladores dedicados, haciéndose especial énfasis en analizar e implementar estas soluciones a los diferentes niveles de estratificación de mezcla considerados en estos motores (es decir, totalmente, altamente y parcialmente premezclada). Inicialmente, se diseñan estrategias de control basadas en el procesamiento de la señal de presión en el cilindro y se seleccionan acciones proporcionales-integrales para asegurar el rendimiento deseado del motor sin exceder las limitaciones mecánicas del motor. También se evalúa la técnica extremum seeking para realizar una supervisión de una combustión eficiente y la reducción de emisiones de NOx. Luego se analiza la resonancia de la presión en el cilindro y se implementa un controlador similar a aquel usado para el control de knock para garantizar el funcionamiento seguro del motor. Finalmente, se utilizan modelos matemáticos para diseñar un modelo orientado a control y un observador que tiene como objetivo combinar las señales medidas en el motor para mejorar las capacidades de predicción y diagnóstico en dicha configuración de motor. Los resultados de este trabajo destacan la importancia de considerar el control en bucle cerrado para abordar las limitaciones encontradas en los modos de combustión premezclada. En particular, el uso de la medición de presión en el cilindro muestra la relevancia y el potencial de esta señal para desarrollar estrategias de control complejas y precisas.[CA] L'actual crisi climàtica ha instat a la comunitat investigadora i als fabricants a brindar solucions per a fer que el sector del transport siga més sostenible. D'entre les diverses tecnologies proposades, la combustió a baixa temperatura ha sigut objecte d'una extensa investigació. La combustió premesclada dual-fuel és un dels conceptes que aborden el compromís de NOx-sutge en motors d'encesa per compressió mantenint alta eficiència tèrmica. Aquesta combustió fa ús de dos combustibles amb diferents reactivitats per a millorar la controlabilitat d'aquest tipus de combustió en un ampli rang de funcionament. De manera similar a tots els tipus de combustió premesclada, aquesta combustió és sensible a les condicions d'operació i sol estar subjecta a variabilitat cíclica amb gradients de pressió significatius. En conseqüència, es requereixen estratègies de control avançades per a garantir un funcionament segur i precís del motor. El control en bucle tancat és una eina eficaç per a abordar els desafiaments que planteja la combustió premesclada dual-fuel. En aquesta mena de control, per a mantindre el funcionament desitjat, les accions de control s'adapten i corregeixen a partir d'una retroalimentació amb els senyals d'eixida del motor. Aquesta tesi presenta estratègies de control basades en el mesurament del senyal de pressió en el cilindre, aplicades a motors de combustió premesclada dual-fuel. En ella es resolen diversos aspectes del funcionament del motor mitjançant el disseny de controladors dedicats, fent-se especial èmfasi a analitzar i implementar aquestes solucions als diferents nivells d'estratificació de mescla considerats en aquests motors (és a dir, totalment, altament i parcialment premesclada). Inicialment, es dissenyen estratègies de control basades en el processament del senyal de pressió en el cilindre i se seleccionen accions proporcionals-integrals per a assegurar el rendiment desitjat del motor sense excedir les limitacions mecàniques del motor. També s'avalua la tècnica extremum seeking per a realitzar una supervisió d'una combustió eficient i la reducció d'emissions de NOx. Després s'analitza la ressonància de la pressió en el cilindre i s'implementa un controlador similar a aquell usat per al control de knock per a garantir el funcionament segur del motor. Finalment, s'utilitzen models matemàtics per a dissenyar un model orientat a control i un observador que té com a objectiu combinar els senyals mesurats en el motor per a millorar les capacitats de predicció i diagnòstic en aquesta configuració de motor. Els resultats d'aquest treball destaquen la importància de considerar el control en bucle tancat per a abordar les limitacions trobades en la combustió premesclada. En particular, l'ús del mesurament de pressió en el cilindre mostra la rellevància i el potencial d'aquest senyal per a desenvolupar estratègies de control complexes i precises.[EN] The current climate crisis has urged the research community and manufacturers to provide solutions to make the transportation sector cleaner. Among the various technologies proposed, low temperature combustion has undergone extensive investigation. Premixed dual-fuel combustion is one of the concepts addressing the NOx-soot trade-off in compression ignited engines, while maintaining high thermal efficiency. This combustion makes use of two fuels with different reactivities in order to improve the controllability of this combustion mode over a wide range of operation. Similarly to all premixed combustion modes, this combustion is nevertheless sensitive to the operating conditions and traditionally exhibits cycle-to-cycle variability with significant pressure gradients. Consequently, advanced control strategies to ensure a safe and accurate operation of the engine are required. Feedback control is a powerful approach to address the challenges raised by the premixed dual-fuel combustion. By measuring the output signals from the engine, strategies can be developed to adapt and correct the control actions to maintain the desired operation. This thesis presents control strategies, based on the in-cylinder pressure signal measurement, applied to premixed dual-fuel combustion engines. Various objectives were addressed by designing dedicated controllers, where a special emphasis was made towards analyzing and implementing these solutions to the different levels of mixture stratification considered in these engines (i.e., fully, highly and partially premixed). At first, feedback control strategies based on the in-cylinder pressure signal processing were designed. Proportional-integral actions were selected to ensure the desired engine performance without exceeding the mechanical constraints of the engine. Extremum seeking was evaluated to track efficient combustion phasing and NOx emissions reduction. The in-cylinder pressure resonance was then analyzed and a knock-like controller was implemented to ensure safe operation of the engine. Finally, mathematical models were used to design a control-oriented model and a state observer that aimed to leverage the signals measured in the engine to improve the prediction and diagnostic capabilities in such engine configuration. The results from this work highlighted the importance of considering feedback control to address the limitations encountered in premixed combustion modes. Particularly, the use of the in-cylinder pressure measurement showed the relevance and potential of this signal to develop complex and accurate control strategies.This thesis was financially supported by the Programa Operativo del Fondo Social Europeo (FSE) de la Comunitat Valenciana 2014-2020 through grant ACIF/2018/141.Barbier, ARS. (2022). In-Cylinder Pressure-Based Control of Premixed Dual-Fuel Combustion [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/18327

Development of a Simulation based Powertrain Design Framework for Evaluation of Transient Soot Emissions from Diesel Engine Vehicles.

This dissertation presents the development of a modeling and simulation framework for diesel engine vehicles to enable soot emissions as a constraint in powertrain design and control. To this end, numerically efficient models for predicting temporallyresolved transient soot emissions are identified in the form of a third-order dual-input single-output (DISO) Volterra series from transient soot data recorded by integrating real-time (RT) vehicle level models in Engine-in-the-loop (EIL) experiments. It is shown that the prediction accuracy of transient soot significantly improves over the steady-state maps, while the model remains computationally efficient for systemslevel work. The evaluation of powertrain design also requires a systematic procedure for dealing with the issue that drivers potentially adapt their driving styles to a given design. In order to evaluate the implications of different powertrain design changes on transient soot production it is essential to compare these design changes on a consistent basis. This problem is explored in the context of longitudinal motion of a vehicle following a standard drive-cycle repeatedly. This dissertation develops a proportional-derivative (PD) type iterative learning based algorithm to synthesize driver actuator inputs that seek to minimize soot emissions using the Volterra series based transient soot models. The solution is compared to the one obtained using linear programming. Results show that about 19% reduction in total soot can be achieved for the powertrain design considered in about 40 iterations. The two contributions of this dissertation: development of computationally efficient system level transient soot models and the synthesis of driver inputs via iterative learning for reducing soot, both contribute to improving the art of modeling and simulation for diesel powertrain design and control.Ph.D.Mechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/86386/1/ahlawatr_1.pd