Motivated by the goal of transition to a zero-carbon-emission-based economy for climate change mitigation, electrification opportunities are more promising in the transportation sector. Electric Vehicles (EVs) are at the forefront of the energy transition at an expanded rapid pace in the transportation sector. To enable and enhance the energy efficiency, advanced control and optimization will play an important role in EV systems and infrastructure. However, there are also some difficulties and limitations subject to the imperfection of management and control for EVs. Overall, to further the widespread adoption of EVs, the dissertation mainly includes two parts: 1) Power management for Plug-in Hybrid Electric Vehicles (PHEVs); 2) Charging control for Plug-in Electric Vehicles (PEVs). Chapter 2 deals with the power management and route planning problems for PHEVs, which aims to properly design the control algorithm to find the route that leads to the minimum energy consumption. Chapter 3 pays attention to the high workloads of the PEV in the electric power grids, which concentrates on studying a control algorithm leading to possible reductions in both computation and communication. Chapter 4 focuses on the charging control for PEVs, which explores how to improve the PEV charging efficiency while satisfying safety concerns. Chapter 5 modifies the results in Chapter 4 by taking battery capacity degradation into the optimization problem. This dissertation proceeds with Chapter 1 by reviewing the state-of-the-art control methods for PEVs and PHEVs. Chapter 2 studies a novel control scheme of route planning with power management for PHEVs. By considering the power management of PHEVs, we aim to find the route that leads to the minimum energy consumption. The scheme adopts a two-loop structure to achieve the control objective. Specifically, in the outer loop, the minimum energy consumption route is obtained by minimizing the difference between the value function of current round and the best value from all previous rounds. In the inner loop, the energy consumption index with respect to PHEV power management for each feasible route is trained with Reinforcement Learning (RL). Under the RL framework, a nonlinear approximator structure, which consists of an actor approximator and a critic approximator, is built to approximate control actions and energy consumption. In addition, the convergence of value function for PHEV power management in the inner loop and asymptotical stability of the closed-loop system are rigorously guaranteed. Chapter 3 investigates the self-triggered Model Predictive Control (MPC) with Integral Sliding Mode (ISM) method of a networked nonlinear continuous-time system subject to state and input constraints with additive disturbances and uncertainties. Compared with the standard MPC strategy, the proposed control scheme is designed for PEV charging to reduce the high communication loads caused by a large-scale population of vehicles under centralized charging control architecture. In the proposed scheme, the constrained optimization problem is solved aperiodically to generate control signals and the next execution time, leading to possible reductions in both computation and communication. The motivation of using ISM approach is to reject matched uncertainties. A self-triggered condition that involves a comparison between the cost function values with different execution periods is derived. Besides, the robust MPC with ISM control strategy is rigorously studied depending on the self-triggered scheme. Chapter 4 proposes a charging control algorithm for the valley-filling problem, while it meets individual charging requirements. We study a decentralized framework of PEV charging problem with a coordination task. An iterative learning-based model predictive charging control algorithm is developed to achieve the valley-filling performance. The design of the decentralized MPC meets individual charging requirements. The iterative learning method approximates the electricity price function and the system state sampled safe set to improve the accuracy of optimization problem calculations. The decentralized problem, in which the individual PEV minimizes its own charging cost, is formulated based on the sum of all power loads. Chapter 5 studies a modified charging control algorithm based on the previous charging control algorithm in Chapter 4. We propose a charging control algorithm for PEVs using a decentralized MPC framework supplemented by the iterative learning method. By considering the battery aging of PEVs, we aim to find the optimal charging rate that leads to valley-filling performance. The scheme adopts the iterative learning-based method to solve the optimal control problem with the battery aging model. Specifically, the sampled safe set and price function are updated accordingly as the iteration number increases. The battery aging model involves the cost function to approach the real charging scenario. In addition, the recursive feasibility of the proposed optimal control problem for PEV charging with battery aging and asymptotical stability of the closed-loop system are rigorously studied. Finally, in Chapter 6, the conclusions of the dissertation and some avenues for future potential research are presented. / Graduate / 2023-04-07

Un modèle décrivant les phénomènes physiques internes de batteries lithium-ion est développé pour une détection précise de leur état, avec application au domaine de l'industrie automobile. Pour pouvoir utiliser le modèle à des fins de contrôle de charge rapide, un observateur de vieillissement est tout d'abord conçu et intégré au modèle de batterie. Dans un second temps, une stratégie de contrôle de charge rapide robuste est conçue. Elle est basée sur un contrôleur Crone capable de gérer les grandes incertitudes paramétriques du modèle de batterie tout en atteignant l'objectif de charge rapide. Enfin, quelques simplifications du modèle de batterie, de la technique d'optimisation et de la définition des profils de charge rapide sont proposées et évaluées afin de rendre l'ensemble de la stratégie de recharge rapide applicable à un système embarqué de gestion de batterie. / A physics-based battery model is developed for an accurate state-detection of batteries in the automotive industry. In order to use the model for the purpose of fast charging control an aging observer is designed and integrated to the battery model. In a subsequent step a robust fast charging control is introduced to design a controller able to deal with large parametric uncertainties of the battery model while achieving the fast charging target. Finally some simplifications in the battery model structure, in the optimization technique and in the definition of fast charging profiles are proposed and evaluated to make the whole model applicable for an onboard battery management system.

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This thesis develops an advanced battery monitoring and control system based on the electrochemical principles that govern lithium-ion battery dynamics. This work is motivated by the need of having safer and better energy storage systems for all kind of applications, from small scale portable electronics to large scale renewable energy storage. In this context, lithium-ion batteries have become the enabling technology for energy autonomy in appliances (e.g. mobile phone, electric vehicle) and energy self-consumption in households. However, batteries are oversized and pricey, might be unsafe, are slow to charge and may not equalize the lifetime of the application they are intended to power. This work tackles these different issues.This document first introduces the general context of the battery management problem, as well as the particular issues that arise when modeling, supervising and controlling the battery short-term and long-term operation. Different solutions coming from the literature are reviewed, and several standard tools borrowed from control theory are exposed. Then, starting by well-known contributions in electrochemical modeling, we proceed to develop reduced-order models for the battery operation including degradation mechanisms, that are highly descriptive of the real phenomena taking place. This modeling framework is the cornerstone of all the monitoring and control development that follows.Next, we derive a battery diagnosis system with a twofold objective. First, indicators for internal faults affecting the battery state-of-health are obtained. Secondly, detection and isolation of sensor faults is achieved. Both tasks rely on state observers designed from electrochemical models to perform state estimation and residual generation. Whereas the former solution resorts to system identification techniques for health monitoring, the latter solution exploits fault diagnosis for instrumentation assessment.We then develop a feedback battery charge strategy able to push in performance while accounting for constraints associated to battery degradation. The fast and safe charging capabilities of the proposed approach are ultimately validated through long-term cycling experiments. This approach outperforms widely used commercial charging strategies in terms of both charging speed and degradation.The main contribution of this thesis is the exploitation of first principles models to develop battery management strategies towards improving safety, charging time and lifetime of battery systems without jeopardizing performance. The obtained results show that system and control theory offer opportunities to improve battery operation, aside from the material sciences contributions to this field. / Doctorat en Sciences de l'ingénieur et technologie / info:eu-repo/semantics/nonPublished

<p dir="ltr">The transportation industry is undergoing a significant transformation with the widespread adoption of electric vehicles (EVs). This transition has spurred the development of innovative methodologies aimed at addressing operational challenges and optimizing infrastructure planning for EV mobility. However, the availability of comprehensive EV trajectory datasets remains limited, leading to potentially biased solutions. Moreover, persistent consumer concerns such as range anxiety and inadequate charging infrastructure hinder widespread EV adoption. Therefore, to accelerate the adoption of EVs and improve the efficiency of electrified mobility operations, there is a pressing need for a comprehensive framework that encompasses dataset preparation, system performance evaluation, and the development of EV operational strategies.</p><p dir="ltr">This dissertation endeavors to advance the field of energy-efficient EV mobility systems through a comprehensive approach integrating data analysis, machine learning & reinforcement learning techniques, and optimization methodologies. Initially, a framework is proposed to overcome the challenge of limited and unreliable EV trajectory datasets by accurately detecting and generating EV trips from mixed vehicle trajectory datasets. Subsequently, the study investigates the impact of EVs on existing mobility systems, particularly within the ride-hailing systems where EVs play a significant role. The analysis includes consideration of taxi drivers' charging costs in queueing models to reveal the influence of electricity rates on system performance, such as driver supply and passenger demand. Additionally, practical algorithms are developed to optimize EV efficiency operations, including energy consumption estimation, energy-efficient routing, and charging control strategies. Specifically, this dissertation analyzes correlations between EV energy consumption and various factors to devise prediction intervals that accommodate uncertainties in road-level energy consumption estimation. Besides, a novel model for EV online energy-efficient routing is proposed, facilitating the identification of minimal expected energy consumption paths for multiple origin-destination pairs simultaneously. These algorithms, augmented with a path elimination mechanism and variance-covariance information, exhibit superior performance compared to traditional methods, significantly reducing energy consumption and enhancing overall operational efficiency. Finally, a framework for EV charging control recommendation is designed under a power-sharing strategy, considering multiple objectives, such as charging time & costs and the pressure on the power grid.</p><p dir="ltr">The methodologies and insights presented in this dissertation advance the understanding and implementation of energy-efficient EV operations and lay the scientific foundations of a more sustainable and efficient EV mobility system. Furthermore, this dissertation offers valuable guidance for policymakers, urban planners, and industry stakeholders in developing more efficient and environmentally friendly transportation networks.</p><p><br></p>

Submitted by Marlucy Farias Medeiros (marlucy.farias@unipampa.edu.br) on 2017-10-31T16:36:39Z No. of bitstreams: 1 John Jefferson Antunes Saldanha - 2017.pdf: 2295770 bytes, checksum: 7d9b5b6835d3e02633dca3155dd44fe7 (MD5) / Approved for entry into archive by Marlucy Farias Medeiros (marlucy.farias@unipampa.edu.br) on 2017-10-31T18:24:47Z (GMT) No. of bitstreams: 1 John Jefferson Antunes Saldanha - 2017.pdf: 2295770 bytes, checksum: 7d9b5b6835d3e02633dca3155dd44fe7 (MD5) / Made available in DSpace on 2017-10-31T18:24:47Z (GMT). No. of bitstreams: 1 John Jefferson Antunes Saldanha - 2017.pdf: 2295770 bytes, checksum: 7d9b5b6835d3e02633dca3155dd44fe7 (MD5) Previous issue date: 2017-09-28 / Uma difusão considerável p elo uso dos veículos elétricos plug-in (VEPs) tem sido promovida, de modo a reduzir as emissões poluentes dos veículos movidos a combustão, bem como preservar as fontes de energia fóssil. Entretanto, cabe ressaltar que os VEPs necessitam se conectar a rede elétrica para recarregar suas baterias. Nesse contexto, caso uma quantidade significativa de veículos elétricos plug-in solicitem recarga ao mesmo tempo, a operação do sistema elétrico de potência (SEP) será comprometida. Em contrapartida, os VEPs também podem auxiliar a rede elétrica através do controle da taxa de recarga e injeção de energia ativa. Assim, é importante realizar o controle da recarga dos VEPs. Dessa forma, este trabalho propõe um sistema inteligente fundamentado em duas interfaces para controlar a taxa de recarga dos VEPs. A primeira interface visa controlar a taxa de recarga de uma frota de veículos com base em um controlador lógico fuzzy projetado e posteriormente ajustado. Nesta interface, buscam-se atender os requisitos do consumidor. Na segunda, gerenciam-se diversas frotas de VEPs visando minimizar perdas de energia e desvios de tensão na rede elétrica. Os resultados da primeira interface mostram que ambos os controladores projetado e ajustado respondem ao cálculo da taxa de recarga levando em consideração as informações inseridas pelo consumidor. Em adição, a resposta do controlador ajustado é mais próxima da resposta desejada, comparando com o controlador projetado. Os resultados da segunda interface mostram que o método de otimização reduziu as perdas de energia elétrica e os desvios de tensão no sistema teste estudado. Concomitantemente, a energia entregue para os VEPs aumentou de maneira significativa. Desta forma, com o sistema desenvolvido, espera-se reduzir o impacto no sistema elétrico de potência e otimizar sua operação, beneficiando a concessionária local, a rede elétrica e o consumidor. / In the present work we investigated experimentally and theoretically the photophysical characterization of organic compounds of the type benzothiazoles, targeting applications in optoelectronic devices, mainly in organic light emitting diodes and photoelectric devices. The study was developed to identify the optical and structural properties of the compounds and the effect of the addition of an amine radical on the ring PhO (benzene-bound benzene) of the benzothiazole compound. Other variations were analyzed, such as changes in the positions of the amine radical added to said compound and absence of the hydroxyl radical. Absorption and photoluminescence experiments were carried out with the purpose of verifying the excitation and fluorescence energies of the compounds, as well as Stokes displacement. The photophysical characterization was also investigated theoretically by means of an ab initio or first principles computational model based on the Density Functional Theory (DFT), implemented in the Gaussian® program, which uses quantum mechanics to calculate the molecular structures and their vibrational properties. We investigated the molecular geometric structure, obtaining the interatomic distances, structure of electronic orbitals, diagrams of energy bands, molecular vibrations and frequency of vibrational modes. By means of Raman spectroscopy, the frequencies of the active Raman vibrational modes were obtained, allowing the comparison with the theoretical results of the simulations. The compounds 4HBS, 4HBSN and 5HBS have their first theoretical characterization from the study of this dissertation.