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Following the positive reception of electric and hybrid transport solutions in the market, manufacturers keep developing their vehicles further, while facing previously undertaken challenges. Knowing the way lithium-ion batteries behave is still one of the key factors for hybrid electric vehicles (HEVs) development, especially for the requirements of the battery management system during their operation. Hence, this project focuses on the necessity of robust yet reasonably simple and cost-effective models of the battery for estimating the health status during the operation of the vehicles. With this aim, the procedure and models to calculate the state-of-health (SOH) indicators, internal resistance and capacity, are proposed and the results discussed. Two machine-learning based models are presented, a support vector machine (SVM) and a neural network (NN), together with one equivalent circuit model (ECM). The data used for training and validating the models comes from testing the batteries in the laboratory with standard performance tests and real driving cycles along the battery lifespan. However, data sets measured in actual heavy-duty vehicles during their operation for three years is also analysed and compared. With respect to this matter, a study of the battery materials, behaviour and operation attributes is carried out, highlighting the main aspects and issues that affect the development of the models. The inputs for the models are signals that can be measured on-board in the vehicles, as current, voltage or temperature, and other derived from them as the state-of-charge (SOC) calculated by the internal battery management unit. Time-series of the variables are used for simulation purposes. The management of signals and implementation of the models is done in the environment of Matlab-Simulink, using some of its in-built functions and other specifically developed. The models are evaluated and compared by means of the normalized root mean squared error (NRMSE) of the voltage output profile compared to that of the tested batteries, but also the error of the internal resistance calculations calculated from the voltage profile for the three models, and the internal parameters in case of the ECM. While despite the difficulties faced with the data, the models can eventually perform accurate estimations of the resistance, the results of the capacity estimations are omitted in the document due to the lack of useful information derived. Nevertheless, the calculation procedure and other considerations to take into account regarding the capacity estimation and data sets are undertaken. Finally, the conclusions about the data used, battery materials and methods evaluated are drawn, laying down recommendations as to design the performance tests following the conditions of the driving cycles, and indicating the higher general performance of the SVM respect the other two methods, while asserting the usefulness of the ECM. Moreover, the battery with NMC material composition is observed to be easier to predict by the models than LFP, also showing different evolution of its internal resistance.

Lithium-ion (Li-ion) batteries are amongst the most commonly used types in Electric (EVs) and Hybrid Electric (HEVs) Vehicles due to their high energy and power densities, as well as long lifetime. A battery is one of the most important components of an EV and hence it needs to be monitored and controlled accurately. The safety, and reliability of battery packs must then, be ensured by accurate management, control, and monitoring functions by using a Battery Management System (BMS). A BMS is also responsible for accurate real-time estimation of the State of Charge (SoC), State of Health (SoH) and State of Power (SoP) of the battery. The battery SoC provides information on the amount of energy left in the battery. The SoH determines the remaining capacity and health of a pack, and the SoP represents the maximum available power. These critical battery states cannot be directly measured. Therefore, they have to be inferred from measurable parameters such as the current delivered by the battery as well as its terminal voltage. Consequently, in order to offer accurate monitoring of SoC, SoH and SoP, advanced numerical estimation methods need to be deployed. In the estimation process, the states and parameters of a system are extracted from measurements. The objective is to reduce the estimation errors in the presence of uncertainties and noise under different operating conditions. This thesis uses and provides different enhancements to a robust estimation strategy referred to as the Smooth Variable Structure Filter (SVSF) for condition monitoring of batteries. The SVSF is a predictor-corrector method based on sliding mode control that enhances the robustness in the presence of noise and uncertainties. The methods are proposed to provide accurate estimates of the battery states of operation and can be implemented in real-time in BMS. To improve the performance of battery condition monitoring, a measurement-based SoC estimation method called coulomb counting is paired with model-based state estimation strategy. Important considerations in parameter and state estimation are model formulation and observability. In this research, a new model formulation that treats coulomb counting as an added measurement is proposed. It is shown that this formulation enhanced information extraction, leading to a more accurate state estimation, as well as an increase in the number of parameters and variables that can be estimated while maintaining observability. This model formulation is used for characterizing the battery in a range of operating conditions. In turn, the models are integral to a proposed adaptive filter that is a combination of the Interacting Multiple Model (IMM) concept and the SVSF. It is shown that this combined strategy is an efficient estimation approach that can effectively deal with battery aging. The proposed method provides accurate estimation for various SoH of a battery. Further to battery aging adaptation, measurement errors such as sensor noise, drift, and bias that affect estimation performance, are considered. To improve the accuracy of battery state estimation, a noise covariance adaptation scheme is developed for the SVSF method. This strategy further improves the robustness of the SVSF in the presence of unknown physical disturbances, noise, and initial conditions. The proposed estimation strategies are also considered for their implementation on battery packs. An important consideration in pack level battery management is cell-to-cell variations that impact battery safety. This study considers online battery parametrization to update the pack’s model over time and to detect cell-to-cell variability in parallel-connected battery cells configurations. Experimental data are used to validate and test the efficacy of the proposed methods in this thesis. / Thesis / Doctor of Philosophy (PhD) / To address the critical issue of climate change, it is necessary to replace fossil-fuel vehicles with battery-powered electric vehicles. Despite the benefits of electric vehicles, their popularity is still limited by the range anxiety and the cost determined by the battery pack. The range of an electric vehicle is determined by the amount of charge in its battery pack. This is comparable to the amount of gasoline in a gasoline vehicle’s tank. In consideration of the need for methods to address range anxiety, it is necessary to develop advanced algorithms for continuous monitoring and control of a battery pack to maximize its performance. However, the amount of charge and health of a battery pack cannot be measured directly and must be inferred from measurable variables including current, voltage and temperature. This research presents several algorithms for detecting the range and health of a battery pack under a variety of operating conditions. With a more accurate algorithm, a battery pack can be monitored closely, resulting in lower long-term costs. Adaptive methods for determining a battery’s state of charge and health in uncertain and noisy conditions have been developed to provide an accurate measure of available charge and capacity. Methods are then extended to improve the determination of state of charge and health for a battery module.

Building on the conceptual lens of Hero and Tolbert (1999), this study examines differences between policy restrictiveness in states with high minority populations and states with low minority populations for three policies areas: felony voting policies, Unemployment Insurance (UI) and the State Children's Health Insurance Program (SCHIP). This study examines whether states with minority populations greater than the national average have public policies that are more restrictive than states with minority populations at or below their national average and the patterns that emerged. Overall, I found higher levels of restrictive policies for states with high minority populations in the instances of felony voting policies and the Unemployment Insurance program. The findings imply a need for accountability and uniformity from the state to improve the outcomes for racial and ethnic minorities. / Ph. D.

As Lithium-ion batteries (LiBs) emerge as pivotal energy storage solutions for automotive applications, maintaining their performance and longevity presents challenges due to power and capacity fade influenced by environmental and usage conditions. Thus, to estimate battery degradation, estimating the state of health (SOH) or predicting remaining useful life (RUL) without considering future operational loads, can limit accurate SOH forecasting. Meanwhile, machine learning (ML) models including deep neural networks (DNNs), have become effective techniques for SOH forecasting of LiBs due to their capability to handle various regression problems without relying on physics-based models. The methodology used in this study, helps battery developers link different operational strategies to battery aging. We use inputs such as temperature (T), current (I), and state of charge (SOC) and utilize a feature transformation technique which generates histogram-based stressor features representing the time that the battery cells spend under operational conditions, then investigate the performance of DNN models along with explainable machine learning (XML) techniques (e.g., SHapley Additive exPlanations) in predicting LiB SOH. The comparative analysis leverages an extensive open-source dataset to evaluate the performance of deep learning models such as LSTM, GRU, and FNN. The forecasting is executed in two distinct modes: one capping the forecasted cycles at 520, and another extending the predictions to the end of the battery’s first life (SOH=80%).Furthermore, this study explores the practicality of a lightweight model, e.g., support vector regression (SVR) model, to compare against DNN models for scenarios with constrained computational and memory resources. The results show that utilizing a feature refinement to ensure the coverage of critical features can lead to performance comparable with the DNN (e.g., LSTM) for the SVR model.

Le projet Européen « GENIUS » ambitionne de développer les méthodologies génériques pour le diagnostic de systèmes piles à combustible à haute température de type oxyde solide (SOFC). Le travail de cette thèse s’intègre dans ce projet ; il a pour objectif la mise en oeuvre d’un outil de diagnostic en utilisant le stack comme capteur spécial pour détecter et identifierles défaillances dans les sous-systèmes du stack SOFC.Trois algorithmes de diagnostic ont été développés, se basant respectivement sur la méthode de classification k-means, la technique de décomposition du signal en ondelettes ainsi que la modélisation par réseau Bayésien. Le premier algorithme sert au diagnostic ex-situ et est appliqué pour traiter les donnés issues des essais de polarisation. Il permet de déterminer les variables de réponse significatives qui indiquent l’état de santé du stack. L’indice Silhouette a été calculé comme mesure de qualité de classification afin de trouver le nombre optimal de classes dans la base de données.La détection de défaut en temps réel peut se réaliser par le deuxième algorithme. Puisque le stack est employé en tant que capteur, son état de santé doit être vérifié préalablement. La transformée des ondelettes a été utilisée pour décomposer les signaux de tension de la pile SOFC dans le but de chercher les variables caractéristiques permettant d’indiquer l’état desanté de la pile et également assez discriminatives pour différentier les conditions d’opération normales et anormales.Afin d’identifier le défaut du système lorsqu’une condition d’opération anormale s’est détectée, les paramètres opérationnelles réelles du stack doivent être estimés. Un réseau Bayésien a donc été développé pour accomplir ce travail.Enfin, tous les algorithmes ont été validés avec les bases de données expérimentales provenant de systèmes SOFC variés, afin de tester leur généricité. / The EU-project “GENIUS” is targeted at the investigation of generic diagnosis methodologies for different Solid Oxide Fuel Cell (SOFC) systems. The Ph.D study presented in this thesis was integrated into this project; it aims to develop a diagnostic tool for SOFC system fault detection and identification based on validated diagnostic algorithms, through applying theSOFC stack as a sensor.In this context, three algorithms, based on the k-means clustering technique, the wavelet transform and the Bayesian method, respectively, have been developed. The first algorithm serves for ex-situ diagnosis. It works on the classification of the polarization measurements of the stack, aiming to figure out the significant response variables that are able to indicate the state of health of the stack. The parameter “Silhouette” has been used to evaluate the classification solutions in order to determine the optimal number of classes/patterns to retain from the studied database.The second algorithm allows the on-line fault detection. The wavelet transform has been used to decompose the SOFC’s voltage signals for the purpose of finding out the effective feature variables that are discriminative for distinguishing the normal and abnormal operating conditions of the system. Considering the SOFC as a sensor, its reliability must be verifiedbeforehand. Thus, the feature variables are also required to be indicative to the state of health of the stack.When the stack is found being operated improperly, the actual operating parameters should be estimated so as to identify the system fault. To achieve this goal, a Bayesian network has been proposed serving as a meta-model of the stack to accomplish the estimation. At the end, the databases originated from different SOFC systems have been used to validate these three algorithms and assess their generalizability.

Thesis (D.P.H.)--University of Michigan.

Dissertation (D.P.H.)--University of Michigan.

Orientador: Germana Fernandes Barata / Dissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Estudos da Linguagem / Made available in DSpace on 2018-08-23T09:36:49Z (GMT). No. of bitstreams: 1 Gorgulho_Guilherme_M.pdf: 4859511 bytes, checksum: 5fb97b25ed97e089a196b5d00b53ec6e (MD5) Previous issue date: 2013 / Resumo: A endemia de hanseníase que atingiu o Estado de São Paulo nas primeiras décadas do século XX levou o governo paulista a optar pela política pública de internação compulsória dos doentes em asilos-colônia. Inspirada no modelo implantado na Noruega no final do século XIX, essa estratégia não foi resultado de consenso científico, mas de decisões do grupo político que galgou o poder a partir de 1930. Com poucas vozes dissonantes no meio acadêmico, a política isolacionista perseverou por cerca de quatro décadas em São Paulo com o apoio fundamental da imprensa e da sociedade. Esta pesquisa analisou como a imprensa paulista retratou o problema da endemia e a situação dos doentes internados compulsoriamente e como os jornais contribuíram para reforçar a manutenção da política sanitária. A hipótese que se coloca é a de que a mídia impressa se engajou na defesa dos interesses do governo estadual, apesar dos descontentamentos entre pacientes e das vozes divergentes dos cientistas. O estudo selecionou 199 textos dos jornais O Estado de S.Paulo, Folha da Noite, Folha da Manhã e Folha de S.Paulo, entre 1933 e 1967. Os métodos empregados foram análise de conteúdo quantitativa e qualitativa e entrevistas semiestruturadas. Ao longo do período estudado, 37% dos textos apoiaram a política de isolamento compulsório, enquanto apenas 16% foram contrários e 47% foram considerados neutros. Nas décadas de 1930 e 1940, a posição foi majoritariamente pró-internação obrigatória; nas décadas de 1950 e 1960, houve uma mudança de postura e os jornais começaram a publicar textos criticando a política profilática paulista. Defendendo a crença de que São Paulo cumpria no Brasil um papel de "grande potência", os jornais paulistas se mostraram comprometidos com as medidas de controle social que queriam excluir a parcela doente da sociedade. Até os anos 1950, as críticas ao modelo não tiveram espaço nos veículos de São Paulo, tendo sido publicadas principalmente na imprensa do Rio de Janeiro. A defesa da internação compulsória ou a omissão sobre os problemas vividos pelos doentes, principalmente na Era Vargas, ajudou a manter o status quo do sanitarismo de São Paulo. O apoio dos jornais à política de isolamento contribuiu para que o degredo dos doentes em São Paulo fosse extinto apenas em 1967, com cinco anos de atraso em relação à decisão federal / Abstract: The endemic leprosy that hit São Paulo state in the early decades of the twentieth century led the state government to opt for public policy of compulsory segregation of patients in asylums colonies. Inspired by the model implemented in Norway in the late nineteenth century, this strategy was not the result of scientific consensus, but the decisions of the political group that came to power in 1930. With few dissenting voices in academia, the isolationist policy persevered for about four decades in São Paulo with major support from the media and society. This research examined how the press portrayed the problem of São Paulo and the endemic situation of patients admitted compulsorily. It also examined how the newspapers contributed to strengthen the maintenance of health policy. The hypothesis is that the print media engaged in defending the interests of the state government, despite the dissatisfaction among patients and divergent voices of scientists. The study selected 199 texts of the newspapers O Estado de S.Paulo, Folha da Noite, Folha da Manhã and Folha de S.Paulo, between 1933 and 1967. The methods used were quantitative and qualitative analysis of content and semi-structured interviews. Throughout the study period, 37 percent of the texts supported the policy of compulsory isolation, while only 16 percent were opposed and 47 percent were neutral. In the 1930s and 1940s, the position was overwhelmingly pro-compulsory hospitalization. In the 1950s and 1960s, there was a change of posture and the newspapers began to publish texts criticizing the prophylactic policy from São Paulo state. The measures of social control wanted to exclude the diseased portion of society. The São Paulo newspapers were compromised with these measures arguing the belief that São Paulo fulfilled a role of "great power" in Brazil. Until the 1950s criticism to the model did not have space in these newspapers, having been published mainly in the press of Rio de Janeiro. The defense of compulsory segregation or the omission about the problems experienced by patients, mainly in the Vargas era, helped to maintain the status quo of health policy in São Paulo. With the support of the newspapers to the policy of compulsory segregation, this model of prophylaxis would be abolished in São Paulo only in 1967, with five years of delay compared to the federal decision / Mestrado / Divulgação Científica e Cultural / Mestre em Divulgação Científica e Cultural

The energy demand is steadily increasing, and the electricity sector is undergoing a severe change in this decade. The primary drivers, such as the need to decarbonize the power industry and megatrends for more distributed and renewable systems, are resulting in revolutionary changes in our lifestyle and industry. The power grid cannot be easily or quickly be upgraded, as investment decisions, construction approvals, and payback time are the main factors to consider. Therefore, new technology, energy storage, tariff reform, and new business models are rapidly changing and challenging the conventional industry. In recent times, industrial peak shaving application has sparked an increased interest in battery energy storage system (BESS).  This work investigated BESS’s potential from peak shaving and Time-of-use (TOU) applications for a Culture-house in Skellefteå. Available literature provides the knowledge of various BESS applications, tariff systems, and how battery degradation functions. The predicted electrical load demand of the culture-house for 2019 is obtained from a consultant company Incoord. The linear optimization was implemented in MATLAB using optimproblem function to perform peak shaving and time-of-use application for the Culture-hose BESS. A cost-optimal charging/discharging strategy was derived through an optimization algorithm by analyzing the culture-house electrical demand and Skellefteå Kraft billing system. The decisional variable decides when to charge/discharge the battery for minimum battery degradation and electricity purchase charges from the grid.   Techno-economic viability is analyzed from BESS investment cost, peak-power tariff, battery lifespan, and batter aging perspective. Results indicate that the current BESS price and peak-power tariff of Skellefteå Kraft are not suitable for peak shaving. Electricity bill saving is too low to consider TOU application due to high battery degradation. However, combining peak shaving & TOU does generate more profit annually due to additional savings from the electricity bill. However, including TOU also leads to higher battery degradation, making it not currently a viable application. A future scenario suggests a decrease in investment cost, resulting in a shorter payback period.  The case study also analyses the potential in the second-life battery, where they are purchased at 80 % State of Health (SoH) for peak shaving application. Second-life batteries are assumed to last until 70 % or 60 % before End of Life (EOL). The benefit-cost ratio indicates that second-life batteries are an attractive investment if batteries can perform until 60% end of life, it would be an excellent investment from an economic and sustainability perspective. Future work suggests integrating more BESS applications into the model to make BESS an economically viable project.