This work focuses on the development and testing of new driving data pattern recognition intelligent system techniques to support driver adaptive, real-time optimal power control and energy management of hybrid electric vehicles (HEVs) and plug-in hybrid electric vehicles (PHEVs). A novel, intelligent energy management approach that combines vehicle operation data acquisition, driving data clustering and pattern recognition, cluster prototype based power control and energy optimization, and real-time driving pattern recognition and optimal energy management has been introduced. The method integrates advanced machine learning techniques and global optimization methods form the driver adaptive optimal power control and energy management. Fuzzy C-Means clustering algorithm is used to identify the representative vehicle operation patterns from collected driving data. Dynamic Programming (DA) based off-line optimization is conducted to obtain the optimal control parameters for each of the identified driving patterns. Artificial Neural Networks (ANN) are trained to associate each of the identified operation patterns with the optimal energy management plan to support real-time optimal control. Implementation and advantages of the new method are demonstrated using the 2012 California household travel survey data, and driver-specific data collected from the city of Victoria, BC Canada. / Graduate

Conventionally, to support varying power demand, the utility company must prepare to supply more electricity than actually needed, which causes inefficiency and waste. With the increasing penetration of renewable energy which is intermittent and stochastic, how to balance the power generation and demand becomes even more challenging. Demand response, which reschedules part of the elastic load in users' side, is a promising technology to increase power generation efficiency and reduce costs. However, how to coordinate all the distributed heterogeneous elastic loads efficiently is a major challenge and sparks numerous research efforts. In this thesis, we investigate different methods to provide demand response and improve power grid efficiency. First, we consider how to schedule the charging process of all the Plugged-in Hybrid Electrical Vehicles (PHEVs) so that demand peaks caused by PHEV charging are flattened. Existing solutions are either centralized which may not be scalable, or decentralized based on real-time pricing (RTP) which may not be applicable immediately for many markets. Our proposed PHEV charging approach does not need complicated, centralized control and can be executed online in a distributed manner. In addition, we extend our approach and apply it to the distribution grid to solve the bus congestion and voltage drop problems by controlling the access probability of PHEVs. One of the advantages of our algorithm is that it does not need accurate predictions on base load and future users' behaviors. Furthermore, it is deployable even when the grid size is large. Different from PHEVs, whose future arrivals are hard to predict, there is another category of elastic load, such as Heating Ventilation and Air-Conditioning (HVAC) systems, whose future status can be predicted based on the current status and control actions. How to minimize the power generation cost using this kind of elastic load is also an interesting topic to the power companies. Existing work usually used HVAC to do the load following or load shaping based on given control signals or objectives. However, optimal external control signals may not always be available. Without such control signals, how to make a tradeoff between the fluctuation of non-renewable power generation and the limited demand response potential of the elastic load, and to guarantee user comfort level, is still an open problem. To solve this problem, we first model the temperature evolution process of a room and propose an approach to estimate the key parameters of the model. Then, based on the model predictive control, a centralized and a distributed algorithm are proposed to minimize the fluctuation and maximize the user comfort level. In addition, we propose a dynamic water level adjustment algorithm to make the demand response always available in two directions. Extensive simulations based on practical data sets show that the proposed algorithms can effectively reduce the load fluctuation. Both randomized PHEV charging and HVAC control algorithms discussed above belong to direct or centralized load shaping, which has been heavily investigated. However, it is usually not clear how the users are compensated by providing load shaping services. In the last part of this thesis, we investigate indirect load shaping in a distributed manner. On one hand, we aim to reduce the users' energy cost by investigating how to fully utilize the battery pack and the water tank for the Combined Heat and Power (CHP) systems. We first formulate the queueing models for the CHP systems, and then propose an algorithm based on the Lyapunov optimization technique which does not need any statistical information about the system dynamics. The optimal control actions can be obtained by solving a non-convex optimization problem. We then discuss when it can be converted into a convex optimization problem. On the other hand, based on the users' reaction model, we propose an algorithm, with a time complexity of O(log n), to determine the RTP for the power company to effectively coordinate all the CHP systems and provide distributed load shaping services. / Graduate

Ce travail de thèse propose une méthodologie systématique d’évaluation et de comparaison des gains en émissions de CO2 de véhicules hybrides électriques de différentes architectures et intégrant différentes fonctionnalités. Une méthodologie de dimensionnement a été mise en place, elle se base sur la définition d’un cahier des charges en performances dynamiques des véhicules, la mise en place d’algorithmes de mise à l’échelle afin de générer les données des composants de la chaîne de traction (batterie, machine électrique, moteur thermique), et l’utilisation de procédures de dimensionnement du véhicule sous contrainte de minimisation des émissions de CO2. L’évaluation énergétique des différentes configurations de véhicule ainsi dimensionnées s’articule autour de la définition de différents usages du véhicule et sur l’implémentation d’une loi de gestion optimale de l’énergie de type Principe du Minimum de Pontriaguine. Ces méthodologies ont été appliquées à une architecture conventionnelle, servant de référence pour les performances dynamiques et les consommations énergétiques, et d’une architecture hybride parallèle pré-transmission, pour laquelle une configuration hybride rechargeable et une configuration hybride non rechargeable ont été implémentées. / This thesis work proposes a systematic methodology dedicated to the evaluation and comparison of CO2 emissions’ reduction for hybrid electric vehicles with different architectures and different levels of functionality. A sizing methodology has been developed, which is based on the definition of the requirements for the dynamic performances of vehicles, on the development of scaling algorithms in order to generate the dataset for the powertrain components (battery, electric motor, engine), and on the application of procedures for the sizing of a vehicle under CO2 emissions’ minimization constraint. The energy consumption evaluation of the different vehicle configurations, which were previously sized, is founded on the definition of a variety of vehicle’s type of use, as well as on the implementation of an optimal energy management strategy, the Pontryaguin’s Minimum Principle. These methodologies have been applied to a conventional vehicle architecture, which has been used as a reference for dynamic performances and energy consumption, and to a hybrid parallel pre-transmission architecture, which has been defined in two configurations, a plug-in hybrid and a non plug-in full-hybrid.

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