IEEE Access special section editorial: battery energy storage and management systems

Battery energy storage and management systems constitute an enabling technology for more sustainable transportation and power grid systems. On the one hand, emerging materials and chemistries of batteries are being actively synthesized to continually improve their energy density, power density, cycle life, charging rate, etc. On the other hand, advanced battery management systems (BMSs) are being intensively developed to guarantee the safety, reliability, efficiency, and cost-effectiveness of batteries in realistic operations, as well as their integration with mechatronics. Owing to their multi-physics nature, designing high-performance batteries and their management systems requires multidisciplinary approaches, with an ever-increasing synergy of electrochemi- cal, material, mechatronics, computer, and control disciplines

Optimization of building performance via model-based predictive control

Il controllo predittivo basato su modello (MPC) è una tecnica di controllo avanzata che ha svolto un ruolo importante nella gestione di molti processi nel settore industriale. Oggi, nell’ottica di una gestione energetica efficiente degli edifici, l’utilizzo di questa strategia si sta dimostrando una soluzione promettente per ridurre al minimo i consumi e i costi energetici complessivi. Tuttavia, gli studi sulla sua fattibilità tecnica in edifici esistenti sono ancora in una fase iniziale. Pertanto, il risultato principale di questa tesi è la progettazione e lo sviluppo di un prototipo hardware e software per la verifica sul campo di un sistema di controllo predittivo, basato su modello, integrando un modello predittivo virtuale della porzione dell'edificio in esame, il controllore e l'interfaccia grafica per i dispositivi di monitoraggio e regolazione utilizzati. Inoltre, particolare attenzione è stata posta sulla fattibilità tecnica relativa all'implementazione di un tipico sistema MPC, che include un sottosistema di monitoraggio, un set di acquisizione dati e un metodo di identificazione del sistema per ottenere il modello per il controllore, mediante un approccio di modellazione grey-box. La fase di modellazione e l'approccio empirico sviluppato sono presentati nella prima parte di questa tesi di ricerca, mentre la parte centrale riguarda: lo sviluppo del prototipo di controllo predittivo, basato su modello, all'interno di uno strumento virtuale del software LabVIEW e la descrizione del test sperimentale, effettuato durante la stagione di riscaldamento, garantendo la normale operatività dell’edificio durante l'intero periodo di monitoraggio. Infine, è presentato lo studio sviluppato in ambiente di simulazione per indagare il potenziale della logica di controllo per la valutazione di scenari di riqualificazione. Il focus è sulla definizione dei principali componenti del simulatore MPC e sui risultati ottenuti testando uno degli scenari di intervento.Model Predictive Control (MPC) is an advanced control technique which has played an important role in the management of many processes in the industry sector. Nowadays, in the perspective of an efficient building energy management, the exploitation of this strategy is proving to be a promising solution for minimising overall energy consumptions and costs. However, investigations on the feasibility of the technique in real existing buildings are at an initial stage. Hence, the main outcome of this dissertation is the design and development of a prototype hardware and software set up for on-field testing of a model-based predictive control system, integrating a virtual predictive model of the portion of the building under investigation, the controller and the interface to the monitoring and regulation devices used. Moreover, this research is addressed to investigate the technical feasibility of the development and deployment of a typical MPC system, which includes a monitoring sub-system, a data acquisition set up and a system identification method to obtain the model for the controller by means of a grey-box modelling approach. The modelling phase and the empirical approach developed are presented in the first part of this research thesis, while the core part concerns: the development of the MPC prototype, within a virtual instrument of LabVIEW software and the description of the experimental test, which was carried out during heating season, ensuring normal building operation during the entire monitoring period. Finally, this dissertation presents the study developed in simulation environment to investigate the potential of the control logic for the evaluation of retrofitting scenarios. The focus is on the definition of the main MPC simulator components and on the results obtained by testing one of the intervention scenarios

Non-invasive Multimodal Monitoring in Traumatic Brain Injury

Traumatic brain injury (TBI) is a leading cause of death and disability, often resulting in increased intracranial pressure (ICP) and cerebral ischemia. Current ICP measurement methods involve invasive, non-therapeutic procedures. This research aims to develop a non-invasive, continuous optical system for monitoring ICP and cerebral oxygenation. Using backscattered brain optical signals, it leverages cerebral pulsatile photoplethysmograms (PPGs) and non-pulsatile near-infrared spectroscopy (NIRS) signals to assess ICP and oxygenation. The innovation lies in using cerebral NIRS-PPGs to measure ICP, based on the hypothesis that changes in ICP affect cerebral PPG signal morphology. These changes in morphological features, with the support of advanced algorithms including Machine Learning (ML) models, could be utilised in translating the changes in the pulsatile signals in absolute measurements of ICP. The research firstly implemented Monte Carlo simulations to fully understand the effect of multi-source detector separations on brain light tissue interaction. Secondly, a novel reflectance, custom-made TBI multiwavelength and multisource-detector optical sensor and instrumentation, including advanced signal processing algorithms, was designed to acquire, pre-process, and analyse raw PPG signals (AC + DC) from the brain. Thirdly, a novel head phantom and an in vitro brain haemodynamic system were developed for evaluating the sensor. The phantom was the ideal tool for simulating different clinical scenarios that cannot be implemented in real in vivo studies. Fourthly, this research carried out three in vitro studies to investigate the sensor's capability to non-invasively monitor intracranial pressure and oxygenation. The first study evaluated the quality of the optical signals acquired from the developed probe at different source-detector (S-D) separations and multiple wavelengths. It was concluded that the optimal S-D separation to reach the cerebral tissue, and acquire good quality PPG signals, was within 3 cm and 4 cm. The second study assessed the central hypothesis of this research by recording PPG signals from the phantom’s brain at different intracranial pressure levels and implementing ML models utilising pertinent features from the PPG. Results from the second study showed a correlation coefficient of 0.86, mean absolute error of 3.7 mmHg, and limits of agreement of ±4 mmHg, which suggest that NIRS-PPG signals could estimate ICP non invasively. Finally, a third study demonstrated the sensor’s response to in vitro changes in blood oxygenation levels, with less than 33.8% error in half the measurements compared to the reference. This final implementation of spatially resolved spectroscopy algorithms actualize the proposed non-invasive multimodal monitoring sensor for traumatic brain injury. The novel technological developments and the new knowledge acquired from this research paves the way for the development of a transformative non-invasive optical sensor technology for the continuous monitoring of ICP and cerebral oxygenation in TBI patients and beyond

Qualitative Adaptive Identification for Powertrain Systems. Powertrain Dynamic Modelling and Adaptive Identification Algorithms with Identifiability Analysis for Real-Time Monitoring and Detectability Assessment of Physical and Semi-Physical System Parameters

A complete chain of analysis and synthesis system identification tools for detectability assessment and adaptive identification of parameters with physical interpretation that can be found commonly in control-oriented powertrain models is presented. This research is motivated from the fact that future powertrain control and monitoring systems will depend increasingly on physically oriented system models to reduce the complexity of existing control strategies and open the road to new environmentally friendly technologies. At the outset of this study a physics-based control-oriented dynamic model of a complete transient engine testing facility, consisting of a single cylinder engine, an alternating current dynamometer and a coupling shaft unit, is developed to investigate the functional relationships of the inputs, outputs and parameters of the system. Having understood these, algorithms for identifiability analysis and adaptive identification of parameters with physical interpretation are proposed. The efficacy of the recommended algorithms is illustrated with three novel practical applications. These are, the development of an on-line health monitoring system for engine dynamometer coupling shafts based on recursive estimation of shaft’s physical parameters, the sensitivity analysis and adaptive identification of engine friction parameters, and the non-linear recursive parameter estimation with parameter estimability analysis of physical and semi-physical cyclic engine torque model parameters. The findings of this research suggest that the combination of physics-based control oriented models with adaptive identification algorithms can lead to the development of component-based diagnosis and control strategies. Ultimately, this work contributes in the area of on-line fault diagnosis, fault tolerant and adaptive control for vehicular systems

Model-based strategies for computer-aided operation of recombinant E. coli fermentation

Tese de Doutoramento em Engenharia Química e BiológicaThe main objectives of this thesis were the development of model-based strategies for improving the performance of a high-cell density recombinant Escherichia coli fed-batch fermentation. The construction of a mathematical model framework as well as the derivation of optimal and adaptive control laws were used to accomplish these tasks. An on-line data acquisition system was also developed for an accurate characterization of the process and for the implementation of the control algorithms. The mathematical model of the process is composed of mass balance equations to the most relevant state variables of the process. Kinetic equations are based on the three possible metabolic pathways of the microorganism: glucose oxidation, fermentation of glucose and acetate oxidation. A genetic algorithm was used to derive the kinetic structure and to estimate both yield and kinetic coefficients of the model, minimizing the normalized quadratic differences between simulated and real values of the state variables. After parameter estimation, a sensitivity function analysis was applied to evaluate the influence of the various parameters on model behavior. Sensitivity functions revealed the sensitivity of the state variables to variations in each model parameter. Thus, essential parameters were selected and the model could be re-written in a simplified version that could also describe accurately experimental data. A system for the on-line monitoring of the major state variables was also developed. Glucose and acetate concentrations were measured with a developed Flow Injection Analysis system, while the carbon dioxide and oxygen transfer rates were calculated from data obtained with exhaust gas analysis. The fermentation culture weight was also continuously assessed with a balance, allowing the use of more precise mass-based concentrations, while environmental variables like pH, dissolved oxygen and temperatures were controlled and assessed via a Digital Control Unit. The graphical programming environment LabVIEW was used to acquire and integrate these variables in a supervisory computer, allowing the performance of integrated monitoring and control of the process. A model-based adaptive linearizing control law was derived for the regulation of acetate concentration during fermentations. The non-linear model was subjected to transformations in order to obtain a linear behavior for the control loop when a non-linear control is applied. The implementation of the control law was performed through a C script embedded in the supervisory LabVIEW program. Finally, two optimization techniques for the maximization of biomass concentration were compared: a first order gradient method and a stochastic method based on the biological principle of natural evolution, using a genetic algorithm. The former method revealed less efficient concerning to the computed maximum, and dependence on good initial values.A presente tese teve como principais objectivos o desenvolvimento de estratégias baseadas em modelos para melhorar o desempenho da fermentação em modo semi-continuo em altas densidades celulares de Escherichia coil recombinada. Para o efeito, foi construído um modelo matemático representativo do processo e a partir deste foram desenvolvidos algoritmos de controlo óptimo e adaptativo. De forma a possibilitar a implementação de leis de controlo em linha e a caracterização do processo fermentativo, foi desenvolvido um sistema informático de aquisição e envio de dados. O modelo matemático representativo do processo em estudo foi elaborado tendo por base as equações dinâmicas de balanço mássico para as variáveis de estado mais relevantes, contemplando as três possíveis vias metabólicas do microrganismo. A estrutura cinética, bem como os parâmetros do modelo foram determinados por recurso a uma abordagem sistemática tendo por base a minimização das diferenças quadráticas entra dados reais e dados simulados, com recurso a uma ferramenta de optimização estocástica denominada de Algoritmos Genéticos. Após a etapa de identificação do modelo matemático, foram calculadas as sensibilidades relativas ao longo do tempo das variáveis de estado do modelo relativamente aos vários parâmetros determinados. Os resultados desta análise de sensibilidade possibilitaram avaliar a relevância de cada um dos parâmetros em causa, permitindo propor uma estrutura de modelo menos complexa, por exclusão dos parâmetros menos importantes. O sistema elaborado para a aquisição e envio em linha de dados da fermentação inclui um sistema de FIA (Flow Injection Analysis) desenvolvido para a medição das concentrações de acetato e glucose, uma unidade de controlo digital que controla as variáveis físicas mais relevantes para o processo, e um equipamento de Espectrometria de Massas para analisar as correntes gasosas de entrada e saída do fermentador. O sistema dispõe ainda de duas balanças, uma das quais para a aferição em linha do peso do caldo de fermentação, permitindo o use de concentrações mássicas que proporcionam resultados mais exactos. A aquisição e integração destas variáveis medidas são, efectuadas através de um software de supervisão elaborado no ambiente de programação gráfico LabVIEW. Adicionalmente, foi elaborada uma lei de controlo adaptativo linearizante para a regulação da concentração de acetato no meio de fermentação. A síntese da lei de controlo não linear foi efectuada por técnicas de geometria diferencial com linearização do sistema por retroacção de estado. A adaptação foi feita tendo por base a estimação de parâmetros variáveis no tempo, nos quais se concentram as incertezas do modelo. A implementação ao processo real da referida lei de controlo foi efectuada por recurso a um programa elaborado em C incluindo no programa supervisor elaborado em LabVIEW. Finalmente, para a optimização da quantidade de biomassa formada no final da fermentação por manipulação do caudal de alimentação, foram estudadas duas ferramentas de optimização: um método de gradiente e uma ferramenta baseada em Algoritmos Genéticos. Esta última revelou-se mais eficaz tanto na convergência para o valor óptimo, como na estimativa inicial fornecida.Fundação para a Ciência e a Tecnologia (FCT) – PRAXIS XXI/16961/98.União Europeia - Fundo Social Europeu (FSE) – III Quadro Comunitário de Apoio (QCA III).Fundação Calouste Gulbenkian (FCQ) - Educação e Bolsas.Agência de Inovação (ADI) - PROTEXPRESS

Program and Proceedings: The Nebraska Academy of Sciences 1880-2013

PROGRAM FRIDAY, APRIL 19, 2013 REGISTRATION FOR ACADEMY, Lobby of Lecture wing, Olin Hall Aeronautics and Space Science, Session A, Olin 249 Aeronautics and Space Science, Session B, Olin 224 Collegiate Academy, Biology Session A, Olin B Biological and Medical Sciences, Session A, Olin 112 Biological and Medical Sciences, Session B, Smith Callen Conference Center NE Chapter, Nat\u27l Council For Geographic Education, Olin 325 Junior Academy, Judges Check-In, Olin 219 Junior Academy, Senior High REGISTRATION, Olin Hall Lobby Chemistry and Physics, Section A, Chemistry, Olin A Chemistry and Physics, Section B, Physics, Planetarium Collegiate Academy, Chemistry and Physics, Session A, Olin 324 Junior Academy, Senior High Competition, Olin 124, Olin 131 Aeronautics and Space Science, Poster Session, Olin 249 Anthropology, Olin 111 NWU Health and Sciences Graduate School Fair, Olin and Smith Curtiss Halls Aeronautics and Space Science, Poster Session, Olin 249 MAIBEN MEMORIAL LECTURE, OLIN B Bob Feurer, North Bend High School, Making People Smarter Using Habits of Mind LUNCH, PATIO ROOM, STORY STUDENT CENTER (pay and carry tray through cafeteria line, or pay at NAS registration desk) Aeronautics Group, Sunflower Room Biological and Medical Sciences, Session C, Olin 112 Biological and Medical Sciences, Session D, Smith Callen Conference Center Chemistry and Physics, Section A, Chemistry, Olin A Collegiate Academy, Biology Session A, Olin B Collegiate Academy, Biology Session B, Olin 249 Collegiate Academy, Chemistry and Physics, Session B, Olin 324 Junior Academy, Judges Check-In, Olin 219 Junior Academy, Junior High REGISTRATION, Olin Hall Lobby Junior Academy, Senior High Competition, (Final), Olin 110 Anthropology, Olin 111 Teaching of Science and Math, Olin 224 Applied Science and Technology, Olin 325 Junior Academy, Junior High Competition, Olin 124, Olin 131 NJAS Board/Teacher Meeting, Olin 219 BUSINESS MEETING, OLIN B AWARDS RECEPTION for NJAS, Scholarships, Members, Spouses, and Guests First United Methodist Church, 2723 N 50th Street, Lincoln, N

WOFEX 2021 : 19th annual workshop, Ostrava, 1th September 2021 : proceedings of papers

The workshop WOFEX 2021 (PhD workshop of Faculty of Electrical Engineer-ing and Computer Science) was held on September 1st September 2021 at the VSB – Technical University of Ostrava. The workshop offers an opportunity for students to meet and share their research experiences, to discover commonalities in research and studentship, and to foster a collaborative environment for joint problem solving. PhD students are encouraged to attend in order to ensure a broad, unconfined discussion. In that view, this workshop is intended for students and researchers of this faculty offering opportunities to meet new colleagues.Ostrav

Modelado Estocástico e Integración de Recursos Energéticos Distribuidos en la Red Eléctrica Inteligente

The residential sector accounts for approximately 30% of the energy consumed in developed countries. This demand is currently covered not only by fossil fuels but also renewable energy sources that ensure a reduction in polluting emissions but which are generally distributed, generate intermittently and are difficult to manage. This requires the development of energy policies that reduce global consumption, as well as control and management systems that target the final consumer. In order to deal with this issue a detailed knowledge of the consumers’ behaviour is needed, both at an aggregate level for the management of the system and at an individual level for the development of measures to adapt their consumption. Furthermore, in this novel context, the feasibility of the different available strategies must be studied in addition to the benefits that can be obtained from their implementation and the control measures that can be developed. This PhD Thesis addresses the development of an energy modelling system for the residential sector as a way of predicting the electricity demand in households and establishing demand response strategies, energy policies and control actions that ease the integration process of distributed energy resources accordingly. The selected modelling technique follows the so-called bottom-up methodology, which enables the consumption in the residential sector as the sum of the individual contributions of each device installed in each household to be obtained. In addition, the simulation of these profiles is carried out using stochastic techniques that allow the heterogeneous and unpredictable behaviour of residents to be reproduced with a high temporal resolution. The modelling system has been divided into three main components which include the consumption due to lighting systems, the heating and air conditioning devices demand and the general appliances consumption. This has facilitated a detailed study of different energy saving policies and the assessment of potential demand response strategies, as well as the development of novel energy management techniques. All of these measures together with the modelling system have been implemented in a simulation tool which was also provided with renewable production data, collected in actual installations. Therefore, not only has the consumption been studied on its own, but also the integration of various resources has been assessed. Some of the studied measures are: replacing devices with more efficient technologies in the case of lighting systems, implementing low-level demand response strategies for household appliances, studying the impact on the low-voltage grid of increasing installation rates of certain technologies such as air conditioning systems and developing novel control techniques in the context of a smart community that can improve the hosting capacity of renewable solar production. Finally, the models and strategies studied in this work have been combined with an advanced metering infrastructure under the umbrella of a smart building. In this context, they provided an additional source of information towards the digitalisation of the electrical system where the extensive use of data allows for the implementation of even more advanced control strategies and will undoubtedly lead to future developments under the paradigm of Smart Grids.El sector residencial representa aproximadamente el 30% de la energía consumida en los países desarrollados. Esta demanda está actualmente cubierta no solo por combustibles fósiles sino también por fuentes renovables que aseguran una reducción en las emisiones contaminantes, pero que generalmente se encuentran distribuidas, producen intermitentemente y son difíciles de gestionar. Esto exige el desarrollo de políticas energéticas que reduzcan el consumo global y sistemas de control y gestión que tengan como objetivo el consumidor final. Solucionar estos retos pasa por conocer el comportamiento los consumidores, tanto a nivel agregado para la gestión del sistema, como a nivel individual para el desarrollo de medidas de adaptación de su propio consumo. Además, en este contexto novedoso es necesario estudiar la viabilidad de las distintas estrategias, los beneficios que se pueden obtener y las medidas de control adicionales que pueden ser desarrolladas. La siguiente Tesis doctoral plantea el desarrollo de un sistema de modelado del consumo en el sector residencial como medio para predecir las necesidades de demanda eléctrica dentro de la red inteligente y establecer a partir de ellas medidas de respuesta a la demanda, políticas energéticas y acciones de control que ayuden a la integración de los recursos energéticos distribuidos. La técnica de modelado escogida sigue una metodología bottom-up (de abajo a arriba) que permite obtener el consumo en el sector residencial como la suma de las contribuciones de cada dispositivo instalado en cada vivienda. Además, la simulación de dichas curvas se ha realizado mediante técnicas estocásticas que permiten reproducir el comportamiento heterogéneo y poco predecible de los residentes con altas resoluciones temporales. El sistema de modelado se ha dividido en tres componentes principales que son el consumo en iluminación, el consumo en calefacción y aire acondicionado y el consumo en electrodomésticos de uso generales. Esto ha permitido un estudio detallado de las distintas medidas de ahorro energético y potenciales estrategias de respuesta a la demanda así como el desarrollo de novedosas técnicas de gestión energética. Todas estas medidas junto con el sistema de modelado han sido implementadas en una herramienta de simulación en la cual se han incluido también datos de producción renovable recogidos en instalaciones reales. De este modo, no solo se ha estudiado el consumo de forma independiente, sino que diversas medidas energéticas han sido también evaluadas. Algunas de ellas han sido: la sustitución de dispositivos por tecnologías más eficientes en el caso de sistemas de iluminación, la implementación de estrategias de respuesta a la demanda a bajo nivel para los electrodomésticos disponibles en los hogares, el estudio del impacto en la red de baja tensión del aumento de determinadas tecnologías como los sistemas de aire acondicionado y el desarrollo de técnicas de control en el contexto de una comunidad inteligente que mejoren la capacidad de acogida de producción fotovoltaica. Finalmente, los modelos y estrategias estudiadas han sido integradas junto con un sistema de contadores inteligentes bajo el paraguas de un edificio gestionable. En este contexto, han aportado una fuente adicional de información hacia la digitalización del sistema eléctrico donde el uso masivo de datos permite implementar estrategias de control aun más avanzadas y que dará pie sin lugar a dudas a futuros desarrollos