With the increase of world population and industrial growth of developing countries, demand for energy, in particular electric power, has gone up at an unprecedented rate over the last decades. To meet the demand, electric power generation by use of fossil fuel has increased enormously thereby producing increased quantity of greenhouse gases. This contributes more and more to atmospheric pollution, which climate scientists believe can adversly affect the global climate, as well as health and the welfare of the world population. In view of these issues, there is global awareness to look for alternate sources of energy such as natural gas, hydropower, wind, solar, geothermal and biomass. It is recognized that this requires replacement of existing infrastructure with new systems, which cannot be achieved overnight. Optimal control theory has been widely used in diverse areas of physical sciences, medicine, engineering and economics. The main motivation of this thesis is to use this theory to find the optimum strategy for integration of all currently available renewable energy sources with the existing electric power generating systems. The ultimate goal is to eliminate fossil fuels. Eight main energy sources namely, Coal, Petroleum, Natural Gas, Conventional Hydro, Wind, Solar, Geothermal and Biomass are considered in a dynamic model. The state of the dynamic model represents the level of energy generation from each of the sources. Different objective functions are proposed in this thesis. These range from meeting the desired target level of power generation from each of the available sources at the end of a given plan period, to reducing the implementation and investment costs; from minimizing the production from polluted energy sources to meeting the electricity demand during a whole plan period. Official released data from the U.S. Energy Information Administration have been used as a case study. Based on real life data and the mathematics of optimal control theory, we present an optimal policy for integration of renewable energy sources to the national power grid.

With the increase of world population and industrial growth of developing countries, demand for energy, in particular electric power, has gone up at an unprecedented rate over the last decades. To meet the demand, electric power generation by use of fossil fuel has increased enormously thereby producing increased quantity of greenhouse gases. This contributes more and more to atmospheric pollution, which climate scientists believe can adversly affect the global climate, as well as health and the welfare of the world population. In view of these issues, there is global awareness to look for alternate sources of energy such as natural gas, hydropower, wind, solar, geothermal and biomass. It is recognized that this requires replacement of existing infrastructure with new systems, which cannot be achieved overnight. Optimal control theory has been widely used in diverse areas of physical sciences, medicine, engineering and economics. The main motivation of this thesis is to use this theory to find the optimum strategy for integration of all currently available renewable energy sources with the existing electric power generating systems. The ultimate goal is to eliminate fossil fuels. Eight main energy sources namely, Coal, Petroleum, Natural Gas, Conventional Hydro, Wind, Solar, Geothermal and Biomass are considered in a dynamic model. The state of the dynamic model represents the level of energy generation from each of the sources. Different objective functions are proposed in this thesis. These range from meeting the desired target level of power generation from each of the available sources at the end of a given plan period, to reducing the implementation and investment costs; from minimizing the production from polluted energy sources to meeting the electricity demand during a whole plan period. Official released data from the U.S. Energy Information Administration have been used as a case study. Based on real life data and the mathematics of optimal control theory, we present an optimal policy for integration of renewable energy sources to the national power grid.

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]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/90624

La gestion énergétique (EMS) pour véhicules hybrides a pour objectif de déterminer la répartition de puissance entre les différentes sources d'énergie de manière à minimiser la consommation de carburant et/ou les émissions polluantes. L'objectif de cette thèse est de développer un EMS en prenant en compte des températures internes (la température du moteur et/ou la température du système de post-traitement). Dans une première partie et en utilisant une connaissance préalable du cycle de conduite, le calcul d'un EMS est formulé comme un problème de commande optimale. Ensuite, le principe du minimum de Pontryagin (PMP) est utilisé pour résoudre ce problème d'optimisation.~En se basant sur les résultats numériques obtenus, un compromis entre les performances de la stratégie de commande et de la complexité du modèle utilisé pour la calculer est établi. Les différents problèmes étudiés dans cette thèse sont des exemples des simplifications successives de modèle qui peuvent être regroupées dans le concept des perturbations régulières en contrôle optimal sous contrainte de commande discuté ici. Dans une deuxième partie, la formulation de l'ECMS a été généralisée pour inclure les dynamiques thermiques. Ces extensions définissent des stratégies sous-optimales que nous avons testées numériquement et expérimentalement. / Energy management system (EMS) for hybrid vehicles consists on determining the power split between the different energy sources in order to minimize the overall fuel consumption and/or pollutant emissions of the vehicle. The objective of this thesis is to develop an EMS taking into account the internal temperatures (engine temperature and/or catalyst temperature). In a first part and using a prior knowledge of vehicle driving cycle, the EMS design is formulated as an optimal control problem. Then, the PMP is used to solve this optimization problem. Based on the obtained numerical results, some trade-off between performance of the control strategy and complexity of the model used to calculate this strategy is established. The various problems studied in this thesis are examples of successive model simplifications which can be recast in the concept of regular perturbations in optimal control under input constraints discussed here. In a second part, the feedback law of ECMS is generalized to include thermal dynamics. This defines sub-optimal feedback strategies which we have tested numerically and experimentally.

Ce mémoire présente une étude comparative de quatre stratégies de gestion énergétique temps réel, appliquées d'une part à un véhicule hybride thermique-électrique, et d'autre part à un véhicule électrique à pile à combustible : contrôle basé sur des règles empirique (RBS), minimisation de la consommation équivalente (A-ECMS), loi de commande optimale (OCL) établie à partir d'une modélisation analytique du système et programmation dynamique stochastique (SDP) associée à une modélisation des cycles de conduite par chaîne de Markov. Le principe du minimum de Pontryaguin et la programmation dynamique, applicables hors ligne, sont mis en œuvre pour fournir des résultats de référence. Les problèmes d’implémentation numérique et de paramétrage des stratégies sont discutés. Une analyse statistique effectuée sur la base de cycles aléatoires générés par chaînes de Markov permet d’évaluer la robustesse des stratégies étudiées. Les résultats obtenus en simulation, puis sur un dispositif expérimental montrent que les méthodes les plus simples (RBS ou OCL) conduisent à des consommations élevées. SDP aboutit aux meilleures performances avec en moyenne la plus faible consommation de carburant dans les conditions réelles de conduite et un état énergétique final du système de stockage parfaitement maîtrisé. Les résultats d’A-ECMS sont comparables à ceux de SDP en moyenne, mais avec une plus grande dispersion, en particulier pour l'état de charge final. Afin d'améliorer les performances des méthode, des jeux de paramètres dédiés aux différents contextes de conduite sont considérés. / This thesis presents a comparative study between four recent real-time energy management strategies (EMS) applied to a hybrid electric vehicle and to a fuel cell vehicle applications: rule-based strategy (RBS), adaptive equivalent consumption minimization strategy (A-ECMS), optimal control law (OCL) and stochastic dynamic programming (SDP) associated to driving cycle modeling by Markov chains. Pontryagin’s minimum principle and dynamic programming are applied to off-line optimization to provide reference results. Implementation and parameters setting issues are discussed for each strategy and a genetic algorithm is employed for A-ECMS calibration.The EMS robustness is evaluated using different types of driving cycles and a statistical analysis is conducted using random cycles generated by Markov process. Simulation and experimental results lead to the following conclusions. The easiest methods to implement (RBS and OCL) give rather high fuel consumption. SDP has the best overall performance in real-world driving conditions. It achieves the minimum average fuel consumption while perfectly respecting the state-sustaining constraint. A-ECMS results are comparable to SDP’s when using parameters well-adjusted to the upcoming driving cycle, but lacks robustness. Using parameter sets adjusted to the type of driving conditions (urban, road and highway) did help to improve A-ECMS performances.