For the broad commercial success of electric vehicles (EVs), it is essential to deeply understand how batteries behave in this challenging application. This thesis has therefore been focused on studying automotive lithium-ion batteries in respect of their performance under EV operation. Particularly, the  need  for  simple  methods  estimating  the  state-of-health  (SOH)  of batteries during EV operation has been addressed in order to ensure safe, reliable, and cost-effective EV operation. Within  the  scope  of  this  thesis,  a  method  has  been  developed  that  can estimate the SOH indicators capacity and internal resistance. The method is solely based on signals that are available on-board during ordinary EV operation  such  as  the  measured  current,  voltage,  temperature,  and  the battery  management  system’s  state-of-charge  estimate.  The  approach  is based on data-driven battery models (support vector machines (SVM) or system  identification)  and  virtual  tests  in  correspondence  to  standard performance  tests  as  established  in  laboratory  testing  for  capacity  and resistance determination. The proposed method has been demonstrated for battery data collected in field tests and has also been verified in laboratory. After a first proof-of-concept of the method idea with battery pack data from a plug-in hybrid electric vehicle (PHEV) field test, the method was improved with the help of a laboratory study where battery electric vehicle (BEV) operation of a battery  cell  was  emulated  under  controlled  conditions  providing  a thorough validation possibility. Precise partial capacity and instantaneous resistance  estimations  could  be  derived  and  an  accurate  diffusion resistance estimation was achieved by including a current history variable in the SVM-based model. The dynamic system identification battery model gave precise total resistance estimates as well. The SOH estimation method was also applied to a data set from emulated hybrid electric vehicle (HEV) operation of a battery cell on board a heavy-duty vehicle, where on-board standard  test  validation  revealed  accurate  dynamic  voltage  estimation performance of the applied model even during high-current situations. In order to exhibit the method’s intended implementation, up-to-date SOH indicators have been estimated from driving data during a one-year time period. / <p>QC 20150914</p>

Thesis (Ph. D.)--Georgia State University, 2007. / Title from file title page. Mary Beth Walker, committee chair; Paul G. Farnham, Erdal Tekin, Patricia G. Ketsche, committee members. Electronic text (140 p. : ill. (some col.)) : digital, PDF file. Description based on contents viewed Dec. 31, 2007. Includes bibliographical references (p. 136-139).

The possibility of measuring the state of health of a NiMH battery without doing it in a lab is evaluated, the goal was to see if it was possible to perceive any differences between batteries of different states of health and whether it’s worth further exploring this solution in a more detailed manner. In order to try and extract and analyze the state of health of the batteries a series of tests consisting of discharging batteries at different lengths of times and different resistive loads were made, the voltage of the batteries being captured by a multimeter. The study shows that getting the state of health of a battery with simple components is a possibility and is useful for battery-powered items. Situations where batteries can’t deliver enough energy for the item to function can be prevented by measuring the health of the batteries and then subsequently switching out the batteries if needed.

Knowledge of battery health is very important. It provides insight into the capacity of a given system and allows the operators to plan ahead more efficiently. But measuring state of health (SoH) of a battery is difficult, and takes time. More importantly, the battery needs to be taken out of operation to be analysed correctly. This paper aims to evaluate a proposed linear regression method for predicting battery health, based on easily acquired operational data. The main predictor being voltage deviation, a characteristic of battery voltages during charge/discharge cycles. Using this method, the only time a battery would need to be extracted is to gather training data. Then, the model could be used for similar batteries to predict their SoH. Meaning those systems would never need to be halted, increasing productivity. The results of this paper is that the data used was not suitable for linear regression. There were problems with heteroskedasticity and non-normality of the residuals, but mainly the estimated parameter for the relationship between voltage deviation and SoH ran contrary to established theory. Which could not be overlooked. Therefore, the estimated models should not be used to predict SoH. To accomplish the goal of accurate SoH prediction, more research should be conducted and a better sample used.

No description available.

The accurate and reliable estimation of the State-of-Charge (SOC) and State-of-Health (SOH) of Li-ion batteries is paramount to the safe and reliable operation of any electrified vehicle. Not only is accuracy and reliability necessary, but these estimation techniques must also be practical and intelligent since their use in real world applications can include noisy input signals, varying ambient conditions and incomplete or partial sequences of measured battery data. To that end, a novel framework, utilizing deep learning techniques, is considered whereby battery modelling and state estimation are performed in a single unified step. For SOC estimation, two different deep learning techniques are used with experimental data. These include a Recurrent Neural Network with Long Short-Term Memory (LSTM-RNN) and a Deep Feedforward Neural Network (DNN); each one possessing its own set of advantages. The LSTM-RNN achieves a Mean Absolute Error (MAE) of 0.57% over a fixed ambient temperature and a MAE of 1.61% over a dataset with ambient temperatures increasing from 10°C to 25°C. The DNN algorithm, on the other hand, achieves a MAE of 1.10% over a 25°C dataset while, at -20°C, a MAE of 2.17% is obtained. A Convolutional Neural Network (CNN), which has the advantage of shared weights, is used with randomized battery usage data to map raw battery measurements directly to an estimated SOH value. Using this strategy, average errors of below 1% are obtained when using fixed reference charge profiles. To further increase the practicality of this algorithm, the CNN is trained and validated over partial reference charge curves. SOH is estimated with a partial reference profile with the SOC ranging from 60% to 95% and achieves a MAE of 0.81%. A smaller SOC range is then used where the partial charge profile spans a SOC of 85% to 95% and a MAE of 1.60% is obtained. Finally, a fused convolutional recurrent neural network (CNN-RNN) is used to perform combined SOC and SOH estimation over constant charge profiles. This is performed by feeding the estimated SOH from the CNN into a LSTM-RNN, which, in turn, estimates SOC with a MAE of less than 0.5% over the lifetime of the battery. / Thesis / Doctor of Philosophy (PhD)

Inequalities in levels of health between regions within a country are frequently regarded as a problem. Zimbabwe is characterised by poor and unequal conditions of health (both the state of people‘s health and health services). The health system of the country shows severe spatial inequalities that are manifested at provincial, district and even local levels. The current research therefore examines and analyses the spatial inequalities and temporal variation of health conditions in Zimbabwe. Composite indices were used to determine the people‘s state of health in Zimbabwe. Administrative districts were ranked according to the level of people‘s state of health. Cluster analysis was also performed to demarcate administrative districts according the level of health service provision. Districts with minimum difference were demarcated in a single cluster. Clusters were delineated using data on patterns of diseases and health and such clusters were used to demarcate the country‘s spatial health system according to the Adapted Epidemiological Transition Model. This was meant to evaluate the applicability of the model to Zimbabwe. It emerged from the research that generally the country‘s health conditions are poor and the health system is characterised by severe spatial inequalities. Some districts are experiencing poor health service provision and serious health challenges and are still in the age of pestilence and famine but others have good health service provision as well as highly developed health conditions and are in the age of degenerative and man-made diseases of the epidemiological transition model. It further emerged that the country‘s health has been evolving with signs of improvement since the 1990s. Some proposals are made in research for spatial development of health in the country. Recommendations were made regarding possible adjustment to previous strategies and policies used in Zimbabwe, for the development of the health system of the country. New strategies were also recommended for the improvement of the health system of the country. / Geography / Ph.D. (Geography)

Un système à pile à combustible permet de convertir l'énergie d'un gaz combustible en électricité et chaleur, par le biais d'une réaction électrochimique. Il existe plusieurs types de piles à combustible et celle de notre étude a été à membrane échangeuse de protons (ou PEM), fonctionnant à des températures de l'ordre de 50°C à 100°C. A ce jour, une des principales problématiques est la durée de vie de la pile et sa gestion. Elle peut en effet être sujette à de multiples défaillances, comme l'assèchement ou l'engorgement dus à la gestion de l'eau dans la pile, les empoisonnements apportés par les gaz combustible ou comburant, les détériorations internes, etc. L'objectif de cette thèse a été de définir et de mettre en oeuvre des méthodes expérimentales et d'analyse pour caractériser ces défaillances. Ces méthodes expérimentales se basent sur des perturbations électriques de la pile ainsi que des mesures des réponses à ces perturbations. On y retrouvera notamment la spectroscopie d'impédance mieux adaptée aux systèmes instables (brevet). On peut les différencier en deux types d'essais : les essais de faible amplitude, qui peuvent être assez facilement réalisables même lorsque la pile est en train d'accomplir sa mission de fourniture d'énergie par exemple, et les essais de large amplitude qui ont un impact assez fort sur la réponse de la pile. Ces essais restent complémentaires et permettent d'évaluer un certain état de santé de la pile au moment de cette mesure. Le post traitement de ces mesures a aussi fait l'objet d'améliorations, notamment en vue d'améliorer la robustesse des résultats. Enfin, ces méthodes ont été validées pour suivre et analyser des dégradations provoquées et déterminer quels sont les paramètres clés associés à telle ou telle dégradation. / A fuel cell system transforms the fuel energy into electricity and heat with electrochemical reaction. There are many kinds of fuel cells and we study here the Proton Exchange Fuelcell (PEMFC), which operates between 50°C and 100°C. At the moment, main issues are fuel cells’ life time and its management. Multiple problems can occur such as drying or flooding due to water management, poisoning with impurities in gas, internal deterioration, etc. The objective of this thesis is to define and carry out experimental and analysing methods to characterize these problems. These experimental methods use electrical perturbation and measurements of their effects. Impedance Spectroscopy is part of these methods, but is greatly improved for instable system (patent). We used two types of tests: low amplitude signal, which can be performed during normal operation of the fuel cell, and large amplitude signal which have a strong impact on the fuel cell response. These tests are complementary and are able to evaluate the state of health of the fuel cell. The analysing process of these measurements is ameliorated, in order to improve the uniqueness of the results. At the end, some problems are generated (drying, flooding, etc) and these methods are performed to follow the variation of performance and determine which parameter is involved with the deterioration.

Le développement de nouveaux convertisseurs d’énergie, plus efficients et plus respectueux de l’environnement, tels que les piles à combustibles, tend à s’accélérer. Leur diffusion à grande échelle suppose cependant des garanties en termes de sécurité et de disponibilité. Une solution possible pour ce faire est de développer des solutions de Prognostics and Health Management (PHM) de ces systèmes, afin de mieux les surveiller, anticiper les défaillances et recommander les actions nécessaires à l’allongement de leur durée de vie. Dans cet esprit, cette thèse porte sur la proposition d’une approche de pronostic dédiée aux piles à combustibles de types PEMFC à l’aide de filtrage particulaire.Le raisonnement s’attache tout d’abord à mettre en place une formalisation du cadre de travail ainsi que des exigences de mise en. Ceci se poursuit par le développement d’un modèle basé sur la physique permettant une estimation d’état de santé et de son évolution temporelle. L’estimation d’état est réalisée grâce à du filtrage particulaire. Différentes variantes de filtres sont considérées sur la base d’une de la littérature et de nouvelles propositions adaptées au PHM sont formulées et comparées à celles existantes. Les estimations d’état de santé fournies par le processus de filtrages ont utilisées pour réaliser des prédictions de l’état de santé futur du système, puis de sa durée devie résiduelle. L’ensemble des propositions est validé sur 4 jeux de données obtenus sur des PEMFC suivant des profils de mission variés. Les résultats montrent de bonnes performances de prédictions et d’estimations de durée de vie résiduelle avant défaillance. / The development of new energy converters, more efficient and environment friendly, such as fuelcells, tends to accelerate. Nevertheless, their large scale diffusion supposes some guaranties in termsof safety and availability. A possible solution to do so is to develop Prognostics and HealthManagement (PHM) on these systems, in order to monitor and anticipate the failures, and torecommend the necessary actions to extend their lifetime. In this spirit, this thesis deals with theproposal of a prognostics approach based on particle filtering dedicated to PEMFCs.The reasoning focuses first on setting a formalization of the working framework and theexpectations. This is pursued by the development of a physic-based modelling enabling a state ofhealth estimation and its evolution in time. The state estimation is made thanks to particle filtering.Different variants of filters are considered on the basis of the literature and new proposals adaptedto PHM are proposed and compared to existing ones. State of health estimates given by the filter areused to predict the future state of the system and its remaining useful life. All the proposals arevalidated on four datasets from PEMFC following different mission profiles. The results show goodperformances for predictions and remaining useful life estimates before failure.

Miracle of human life becomes evident at the moment of his birth. However at this moment only the next stage is reached by the process that starts from conjugation between father��s gamete and mother’s egg cell and which ends after the youngster is pubescent. This process is called the development. In the appropriate period of child’s growth his body is characterized by specific morphological features. Parents are the best observers of their children. However in order to observe deviations of infant’s development it is necessary to know what normal development looks like. Early years may become a basis for long and healthy life therefore the early prophylaxis is of great value (Prasauskienė, 2003). Purpose of this paper was to investigate effect of kinezitherapy and other factors on motor development of normally developing children in the first years of life. Investigation methods used in this paper were as follows: 1. Assessment of gross motor skills’ development; 2. Analysis of subjects’ medical histories; 3. Mathematical statistics. The investigation was carried out at the Child’s Development Clinic “Lopšelis” of Kaunas (Kauno Vaiko Raidos Klinika “Lopšelis”) in the department of infants’ home beginning on October of 2004 and until February of 2006 (period – 14 months). During this period time and development of early gross motor skills in 19 infants were examined that had no neurological disorders (in accordance with Munich diagnostic scale of functional development)... [to full text]