New services and ever increasing traffic volumes require the next generation of mobile networks, e.g. 5G, to be much more flexible and scalable. The primary enabler for its flexibility is transforming network functions from proprietary hardware to software using modern virtualization technologies, paving the way of virtual network functions (VNF). Such VNFs can then be flexibly deployed on cloud data centers while traffic is routed along a chain of VNFs through software-defined networks. However, such flexibility comes with a new challenge of allocating efficient computational resources to each VNF and optimally placing them on a cluster. In this thesis, we argue that, to achieve an autonomous and efficient performance optimization method, a solid understanding of the underlying system, service chains, and upcoming traffic is required. We, therefore, conducted a series of focused studies to address the scalability and performance issues in three stages. We first introduce an automated profiling and benchmarking framework, named NFV-Inspector to measure and collect system KPIs as well as extract various insights from the system. Then, we propose systematic methods and algorithms for performance modelling and resource recommendation of cloud native network functions and evaluate them on a real 5G testbed. Finally, we design and implement a bottom-up performance simulator named PerfSim to approximate the performance of service chains based on the nodes’ performance models and user-defined scenarios. / <p>Article 5 part of thesis as manuscript, now published.</p>

Changes in weather patterns worldwide and global warming increased the demand for high-performance buildings resilient to climate change. Building Performance Simulation (BPS) is a robust technique to test, assess, and enhance energy efficiency measures and comply with stringent energy codes of buildings. Climate has a considerable impact on the buildings' thermal environment and energy performance; therefore, choosing reliable and accurate weather data is crucial for building performance evaluation and reducing the performance gap. Typical Weather Years (TWYs) have been traditionally used for energy simulation of buildings. Even if detailed energy assessments can be performed using available multi-year weather data, most simulations are carried out using a typical single year. As a result, this fictitious year must accurately estimate the typical multi-year conditions. TWYs are widely used because they accelerate the modeling process and cut down on computation time while generating relatively accurate long-term predictions of building energy performance. However, there is no certainty that a single year can describe the changing climate and year-by-year variations in weather patterns. Nowadays, with increased computational power and higher speeds in calculation processes, it is possible to adopt multi-year weather datasets to fully assess long-term building energy performance and avoid errors and inaccuracies during the preliminary selection procedures. This study aims to investigate the impact of Typical Weather Years and Actual Weather Years (AWYs) on a single-family house and a university building under two opposite climates, Winnipeg (cold) and Catania (hot). First, a single-family house in Winnipeg, Canada, was selected to evaluate how typical weather years affect the energy performance of the building and compare it with AWYs simulation. Two widely used typical weather data, CWEC and TMY, were selected for the simulation. The results were compared with the outcomes of simulation using AWYs derived from the same weather station from 2015 to 2019, which covered the latest climate changes. The results showed that typical weather years could not sufficiently capture the year-by-year variation in weather patterns. The typical weather years overestimated the cooling load while underestimating the heating demands compared to the last five actual weather years. A more extensive study was conducted for more confidence in the findings and understanding of the weather files. The research was expanded by comparing the results of building performance simulation of the single-family house and an institutional building with more complex envelope characteristics belonging to the University of Manitoba under cold (Winnipeg, Canada) and hot (Catania, Italy) climates. Overall, 48 simulations were performed using ten actual weather years from 2010 to 2019 and two TWYs from each climate for both buildings. The results showed that while the TWYs either overestimate or underestimate the cooling and heating demands of both buildings, cooling load predictions were highly overestimated in the heating-dominant climate of Winnipeg, ranging from 10.5% to 82.4% for both buildings by CWEC and TMY weather data. In the cooling-dominant climate of Catania, energy simulations using IWEC and TMY typical weather data highly overestimated the heating loads between 2.8% and 82.4%.

Simulações computacionais são um recurso de grande valia no projeto do edifício, particularmente na área de desempenho ambiental, permitindo predizer fenômenos complexos como desempenho térmico, lumínico, acústico e energético dos edifícios e de seu entorno. O surgimento do BIM (Building Information Modeling ou Modelagem da Informação da Construção), por sua vez, forneceu aos profissionais da indústria da construção novas ferramentas para auxiliar na criação e gestão da informação da construção. Ao combinar um modelo 3D com um banco de dados único do projeto, BIM acaba por reduzir a perda de informação e o retrabalho, permitindo o trabalho colaborativo e aumentando a confiabilidade e rastreabilidade das informações do projeto ao longo do ciclo de vida da construção. Muito antes do BIM, ferramentas de simulação de desempenho do edifício (Building Performance Simulation - BPS, na sigla em inglês) já empregavam modelos 3D, o que significa que especialistas de avaliação ambiental do edifício frequentemente tinham que modelar o edifício - e remodelá-lo cada vez que o projeto fosse alterado - dentro destas ferramentas de modo a executar as análises de desempenho. Neste contexto, a integração entre ferramentas BIM e BPS é fundamental para aumentar a eficiência de uma indústria da construção altamente fragmentada. Nos últimos anos, muitos pesquisadores têm se focado em alcançar tal integração. Este estudo sintetiza as pesquisas nesta questão por meio da revisão sistemática de mais de 250 pesquisas publicadas mundialmente no período de 1991 a 2015. Os resultados mostram que, apesar de um aumento significativo no número de estudos publicados nos últimos cinco anos, a plena integração entre BIM e BPS é um assunto complexo e continua sendo um desafio. Esta revisão sistemática produziu um diagnóstico abrangente e contribui com pesquisadores por revelar padrões, tendências e lacunas da área de pesquisa, orientando assim futuros esforços de pesquisa. / Computer simulations are a valuable resource in building design, notably in the environmental performance field, enabling designers and engineers to predict complex phenomena such as thermal, lighting, acoustic and energy performance. The emergence of BIM (Building Information Modeling), in turn, provided these professionals with new tools to assist in the creating and managing of building information. By combining a 3D model to a unique project database, BIM ultimately reduces the loss of information and rework, allowing collaborative work and increasing reliability and traceability of the project information throughout the construction lifecycle. Long before BIM, Building Performance Simulation (BPS) tools already employed 3D models, meaning that simulationists frequently had to model the building - and remodel it as many times as the design changed - within these tools in order to run performance analyses. In this context, the integration of BIM and BPS tools is critical to increase efficiency of a highly fragmented construction industry. In the past years, many researchers have been focusing on achieving this integration. This study summarizes research on this topic by systematically reviewing over 250 researches published worldwide from 1991 to 2015. Results show that, despite a significant increase in the number of studies published in the last five years, fully integration between BIM and BPS is a complex subject and remains a challenge. This systematic review produced a comprehensive diagnosis and contributes with researchers by revealing patterns, trends and gaps of the research area, orientating future research efforts.

Simulações computacionais são um recurso de grande valia no projeto do edifício, particularmente na área de desempenho ambiental, permitindo predizer fenômenos complexos como desempenho térmico, lumínico, acústico e energético dos edifícios e de seu entorno. O surgimento do BIM (Building Information Modeling ou Modelagem da Informação da Construção), por sua vez, forneceu aos profissionais da indústria da construção novas ferramentas para auxiliar na criação e gestão da informação da construção. Ao combinar um modelo 3D com um banco de dados único do projeto, BIM acaba por reduzir a perda de informação e o retrabalho, permitindo o trabalho colaborativo e aumentando a confiabilidade e rastreabilidade das informações do projeto ao longo do ciclo de vida da construção. Muito antes do BIM, ferramentas de simulação de desempenho do edifício (Building Performance Simulation - BPS, na sigla em inglês) já empregavam modelos 3D, o que significa que especialistas de avaliação ambiental do edifício frequentemente tinham que modelar o edifício - e remodelá-lo cada vez que o projeto fosse alterado - dentro destas ferramentas de modo a executar as análises de desempenho. Neste contexto, a integração entre ferramentas BIM e BPS é fundamental para aumentar a eficiência de uma indústria da construção altamente fragmentada. Nos últimos anos, muitos pesquisadores têm se focado em alcançar tal integração. Este estudo sintetiza as pesquisas nesta questão por meio da revisão sistemática de mais de 250 pesquisas publicadas mundialmente no período de 1991 a 2015. Os resultados mostram que, apesar de um aumento significativo no número de estudos publicados nos últimos cinco anos, a plena integração entre BIM e BPS é um assunto complexo e continua sendo um desafio. Esta revisão sistemática produziu um diagnóstico abrangente e contribui com pesquisadores por revelar padrões, tendências e lacunas da área de pesquisa, orientando assim futuros esforços de pesquisa. / Computer simulations are a valuable resource in building design, notably in the environmental performance field, enabling designers and engineers to predict complex phenomena such as thermal, lighting, acoustic and energy performance. The emergence of BIM (Building Information Modeling), in turn, provided these professionals with new tools to assist in the creating and managing of building information. By combining a 3D model to a unique project database, BIM ultimately reduces the loss of information and rework, allowing collaborative work and increasing reliability and traceability of the project information throughout the construction lifecycle. Long before BIM, Building Performance Simulation (BPS) tools already employed 3D models, meaning that simulationists frequently had to model the building - and remodel it as many times as the design changed - within these tools in order to run performance analyses. In this context, the integration of BIM and BPS tools is critical to increase efficiency of a highly fragmented construction industry. In the past years, many researchers have been focusing on achieving this integration. This study summarizes research on this topic by systematically reviewing over 250 researches published worldwide from 1991 to 2015. Results show that, despite a significant increase in the number of studies published in the last five years, fully integration between BIM and BPS is a complex subject and remains a challenge. This systematic review produced a comprehensive diagnosis and contributes with researchers by revealing patterns, trends and gaps of the research area, orientating future research efforts.

This thesis proposes a new methodology to extend the software performance engineering process. Common performance measurement and tuning principles mainly target to improve the software function itself. Hereby, the application source code is studied and improved independently of the overall system performance behavior. Moreover, the optimization of the software function has to be done without an estimation of the expected optimization gain. This often leads to an under- or overoptimization, and hence, does not utilize the system sufficiently. The proposed performance improvement methodology and framework, called dynamic performance stubs, improves the before mentioned insufficiencies by evaluating the overall system performance improvement. This is achieved by simulating the performance behavior of the original software functionality depending on an adjustable optimization level prior to the real optimization. So, it enables the software performance analyst to determine the systems’ overall performance behavior considering possible outcomes of different improvement approaches. Moreover, by using the dynamic performance stubs methodology, a cost-benefit analysis of different optimizations regarding the performance behavior can be done. The approach of the dynamic performance stubs is to replace the software bottleneck by a stub. This stub combines the simulation of the software functionality with the possibility to adjust the performance behavior depending on one or more different performance aspects of the replaced software function. A general methodology for using dynamic performance stubs as well as several methodologies for simulating different performance aspects is discussed. Finally, several case studies to show the application and usability of the dynamic performance stubs approach are presented.

This study examines the potential to conduct building thermal performance simulation (BPS) of unconditioned low-cost housing during the early design stages. By creating a set of regression models (meta-models) based on EnergyPlus simulations, this research aims to promote and simplify BPS in the building envelope design process. The meta-models can be used as tools adapted for three Brazilian cities: Curitiba, São Paulo and Manaus, providing decision support to designers by enabling rapid feedback that links early design decisions to the buildings thermal performance. The low-cost housing unit studied is a detached onestory house with an area of approximately 51m2, which includes two bedrooms, a combined kitchen and living room, and one bathroom. This representative configuration is based on collected data about the most common residence options in some Brazilian cities. This naturally ventilated residence is simulated in the Airflow Network module in EnergyPlus, which utilizes the average wind pressure coefficients provided by the software. The parametric simulations vary the house orientation, U-value, heat capacity and absorptance of external walls and the roof, the heat capacity of internal walls, the window-to-wall ratio, type of window (slider or casement), and the existence of horizontal and/or vertical shading devices with varying dimensions. The models predict the resulting total degree-hours of discomfort in a year due to heat and cold, based on comfort limits defined by the adaptive method for naturally ventilated residences according to ANSI ASHRAE Standard 55. The methodology consists of (a) analyzing a set of Brazilian low-cost housing projects and defining a geometric model that can represent it; (b) determining a list of design parameters relevant to thermal comfort and defining value ranges to be considered; (c) defining the input data for the 10.000 parametric simulations used to create and test the meta-models for each analyzed climate; (d) simulating thermal performance using Energy Plus; (e) using 60% of the simulated cases to develop the regression models; and (f) using the remaining 40% data to validate the meta-models. Except by Heat discomfort regression models for the cities of Curitiba and São Paulo the meta-models show R2 values superior to 0.9 indicating accurate predictions when compared to the discomfort predicted with the output data from EnergyPlus, the original simulation software. Meta-models application tests are performed and the meta-models show great potential to guide designers decisions during the early design. / Esta pesquisa avalia as potencialidades do uso de simulações do desempenho térmico (SDT) nas etapas iniciais de projetos de habitações de interesse social (HIS) não condicionadas artificialmente. Busca-se promover e simplificar o uso de SDT no processo de projeto da envolvente de edificações através da criação de modelos de regressão baseados em simulações robustas através do software EnergyPlus. Os meta-modelos são adaptados ao clima de três cidades brasileiras: Curitiba, São Paulo e Manaus, e permitem uma rápida verificação do desconforto térmico nas edificações podendo ser usados como ferramentas de suporte às decisões de projeto nas etapas iniciais. A HIS considerada corresponde a uma unidade térrea com aproximadamente 51m2, composta por dois quartos, um banheiro e cozinha integrada à sala de jantar. Esta configuração é baseada em um conjunto de projetos representativos coletados em algumas cidades brasileiras (como São Paulo, Curitiba e Manaus). Estas habitações naturalmente ventiladas são simuladas pelo módulo Airflow Network utilizando o coeficiente médio de pressão fornecido pelo EnergyPlus. As simulações consideram a parametrização da orientação da edificação, transmitância térmica (U), capacidade térmica (Ct) e absortância () das paredes externas e cobertura; Ct e U das paredes internas; relação entre área de janela e área da parede; tipo da janela (basculante ou de correr); existência e dimensão de dispositivos verticais e horizontais de sombreamento. Os meta-modelos desenvolvidos fornecem a predição anual dos graus-hora de desconforto por frio e calor, calculados com base nos limites de conforto definidos pelo método adaptativo para residências naturalmente ventiladas (ANSI ASHRAE, 2013). A metodologia aplicada consiste em: (a) análise de um grupo de projetos de HIS brasileiras e definição de um modelo geométrico que os represente; (b) definição dos parâmetros relevantes ao conforto térmico, assim como seus intervalos de variação; (c) definição dos dados de entrada para as 10.000 simulações paramétricas utilizadas na criação e teste de confiabilidade dos meta-modelos para cada clima analisado; (d) simulação do desempenho térmico por meio do software EnergyPlus; (e) utilização de 60% dos casos simulados para o desenvolvimento dos modelos de regressão; e (f) uso dos 40% dos dados restantes para testar a confiabilidade do modelo. Exceto pelos modelos para predição do desconforto por calor para Curitiba e São Paulo, os demais meta-modelos apresentaram valores de R2 superiores a 0.9, indicando boa adequação das predições de desconforto dos modelos gerados ao desconforto calculado com base no resultado das simulações no EnergyPlus. Um teste de aplicação dos meta-modelos foi realizado, demonstrando seu grande potencial para guiar os projetistas nas decisões tomadas durante as etapas inicias de projeto.

[pt] Este trabalho tem por objetivo principal a avaliação de desempenho de um sistema piloto de preaquecimento dos motores da central termelétrica Gera Maranhão, via energia solar térmica, em Miranda do Norte, Maranhão, através de uma simulação numérica. Cinco subsistemas independentes, cada um responsável pelo preaquecimento de um motor Wartsila 20V32 de 8,73 MW, foram construídos, somando um total de 500 coletores solares instalados e uma superfície de captação solar total de 1000 metros quadrados. Uma estação meteorológica com sensores de radiação solar global, difusa, direta e temperatura ambiente foi posicionada do lado dos sistemas para medir as condições ambientais na região. A simulação do desempenho do sistema solar foi efetuada ao longo de um ano com dados de radiação solar da estação meteorológica de Buriticupu, no Maranhão, dados que mais se aproximam dos dados disponíveis de Miranda do Norte. Correlações para transformar a radiação global medida numa superfície horizontal para uma superfície inclinada foram selecionadas após uma revisão bibliográfica dentre as disponíveis na literatura. Diferentes cenários de controle do acionamento das bombas de água foram comparados a fim de determinar a melhor configuração de operação. A influência da temperatura de preaquecimento dos motores no desempenho do sistema solar foi avaliada também. Os resultados da simulação foram comparados com os resultados obtidos via o método F-CHART. Uma participação média anual da energia solar de 11,5 por cento foi encontrada para o preaquecimento dos motores levando a uma redução de 24693 kg/ano de óleo combustível usado na caldeira do sistema de preaquecimento dos motores da usina termelétrica. / [en] The present work has as main objective the performance evaluation of a pilot system for preheating the engines of Gera Maranhão power plant, in Miranda do Norte, state of Maranhão, via thermal solar energy using a numerical simulation. Five independent subsystems, each one responsible for the preheating of a Wartsila 20V32 internal combustion engine of 8.73 MW, were installed. These systems amount five hundred solar collectors, with a total solar collecting area of 1000 square meters. A meteorological station with sensors for global, diffusive and beam solar radiation, as well as ambient temperature recorders, was placed by the side of the system in mode to measure ambient condition in the area. The simulation of the solar system performance was processed over a year with data of solar radiation for a meteorological station of Buriticupu, state of Maranhão, Brazil. Correlations to transform the global radiation measured on a horizontal plane to a sloped plane were selected, following a selection from a literature review. For the control of the water pumps, different scenarios were compared in order to determine the best operational configuration. The influence of engine preheating temperature in the performance of the solar system was also evaluated. Simulation results were compared with results obtained with the F-CHART method. An annual average solar energy contribution of 11.5 percent was found for the preheating of the engines. This resulted in a reduction of 24693 kg per year of fuel oil used in the boiler of the traditional preheating system of the power plant.

This study examines the potential to conduct building thermal performance simulation (BPS) of unconditioned low-cost housing during the early design stages. By creating a set of regression models (meta-models) based on EnergyPlus simulations, this research aims to promote and simplify BPS in the building envelope design process. The meta-models can be used as tools adapted for three Brazilian cities: Curitiba, São Paulo and Manaus, providing decision support to designers by enabling rapid feedback that links early design decisions to the buildings thermal performance. The low-cost housing unit studied is a detached onestory house with an area of approximately 51m2, which includes two bedrooms, a combined kitchen and living room, and one bathroom. This representative configuration is based on collected data about the most common residence options in some Brazilian cities. This naturally ventilated residence is simulated in the Airflow Network module in EnergyPlus, which utilizes the average wind pressure coefficients provided by the software. The parametric simulations vary the house orientation, U-value, heat capacity and absorptance of external walls and the roof, the heat capacity of internal walls, the window-to-wall ratio, type of window (slider or casement), and the existence of horizontal and/or vertical shading devices with varying dimensions. The models predict the resulting total degree-hours of discomfort in a year due to heat and cold, based on comfort limits defined by the adaptive method for naturally ventilated residences according to ANSI ASHRAE Standard 55. The methodology consists of (a) analyzing a set of Brazilian low-cost housing projects and defining a geometric model that can represent it; (b) determining a list of design parameters relevant to thermal comfort and defining value ranges to be considered; (c) defining the input data for the 10.000 parametric simulations used to create and test the meta-models for each analyzed climate; (d) simulating thermal performance using Energy Plus; (e) using 60% of the simulated cases to develop the regression models; and (f) using the remaining 40% data to validate the meta-models. Except by Heat discomfort regression models for the cities of Curitiba and São Paulo the meta-models show R2 values superior to 0.9 indicating accurate predictions when compared to the discomfort predicted with the output data from EnergyPlus, the original simulation software. Meta-models application tests are performed and the meta-models show great potential to guide designers decisions during the early design. / Esta pesquisa avalia as potencialidades do uso de simulações do desempenho térmico (SDT) nas etapas iniciais de projetos de habitações de interesse social (HIS) não condicionadas artificialmente. Busca-se promover e simplificar o uso de SDT no processo de projeto da envolvente de edificações através da criação de modelos de regressão baseados em simulações robustas através do software EnergyPlus. Os meta-modelos são adaptados ao clima de três cidades brasileiras: Curitiba, São Paulo e Manaus, e permitem uma rápida verificação do desconforto térmico nas edificações podendo ser usados como ferramentas de suporte às decisões de projeto nas etapas iniciais. A HIS considerada corresponde a uma unidade térrea com aproximadamente 51m2, composta por dois quartos, um banheiro e cozinha integrada à sala de jantar. Esta configuração é baseada em um conjunto de projetos representativos coletados em algumas cidades brasileiras (como São Paulo, Curitiba e Manaus). Estas habitações naturalmente ventiladas são simuladas pelo módulo Airflow Network utilizando o coeficiente médio de pressão fornecido pelo EnergyPlus. As simulações consideram a parametrização da orientação da edificação, transmitância térmica (U), capacidade térmica (Ct) e absortância () das paredes externas e cobertura; Ct e U das paredes internas; relação entre área de janela e área da parede; tipo da janela (basculante ou de correr); existência e dimensão de dispositivos verticais e horizontais de sombreamento. Os meta-modelos desenvolvidos fornecem a predição anual dos graus-hora de desconforto por frio e calor, calculados com base nos limites de conforto definidos pelo método adaptativo para residências naturalmente ventiladas (ANSI ASHRAE, 2013). A metodologia aplicada consiste em: (a) análise de um grupo de projetos de HIS brasileiras e definição de um modelo geométrico que os represente; (b) definição dos parâmetros relevantes ao conforto térmico, assim como seus intervalos de variação; (c) definição dos dados de entrada para as 10.000 simulações paramétricas utilizadas na criação e teste de confiabilidade dos meta-modelos para cada clima analisado; (d) simulação do desempenho térmico por meio do software EnergyPlus; (e) utilização de 60% dos casos simulados para o desenvolvimento dos modelos de regressão; e (f) uso dos 40% dos dados restantes para testar a confiabilidade do modelo. Exceto pelos modelos para predição do desconforto por calor para Curitiba e São Paulo, os demais meta-modelos apresentaram valores de R2 superiores a 0.9, indicando boa adequação das predições de desconforto dos modelos gerados ao desconforto calculado com base no resultado das simulações no EnergyPlus. Um teste de aplicação dos meta-modelos foi realizado, demonstrando seu grande potencial para guiar os projetistas nas decisões tomadas durante as etapas inicias de projeto.

Indiana University-Purdue University Indianapolis (IUPUI) / In the last years, lithium-ion batteries (LIBs) have become the most important energy storage system for consumer electronics, electric vehicles, and smart grids. The development of lithium-ion batteries (LIBs) based on current practice allows an energy density increase estimated at 10% per year. However, the required power for portable electronic devices is predicted to increase at a much faster rate, namely 20% per year. Similarly, the global electric vehicle battery capacity is expected to increase from around 170 GWh per year today to 1.5 TWh per year in 2030--this is an increase of 125% per year. Without a breakthrough in battery design technology, it will be difficult to keep up with the increasing energy demand. To that end, a design methodology to accelerate the LIB development is needed. This can be achieved through the integration of electro-chemical numerical simulations and machine learning algorithms. To help this cause, this study develops a design methodology and framework using Simcenter Battery Design Studio® (BDS) and Bayesian optimization for design and optimization of cylindrical cell type 18650. The materials of the cathode are Nickel-Cobalt-Aluminum (NCA)/Nickel-Manganese-Cobalt-Aluminum (NMCA), anode is graphite, and electrolyte is Lithium hexafluorophosphate (LiPF6). Bayesian optimization has emerged as a powerful gradient-free optimization methodology to solve optimization problems that involve the evaluation of expensive black-box functions. The black-box functions are simulations of the cyclic performance test in Simcenter Battery Design Studio. The physics model used for this study is based on full system model described by Fuller and Newman. It uses Butler-Volmer Equation for ion-transportation across an interface and solvent diffusion model (Ploehn Model) for Aging of Lithium-Ion Battery Cells. The BDS model considers effects of SEI, cell electrode and microstructure dimensions, and charge-discharge rates to simulate battery degradation. Two objectives are optimized: maximization of the specific energy and minimization of the capacity fade. We perform global sensitivity analysis and see that thickness and porosity of the coating of the LIB electrodes that affect the objective functions the most. As such the design variables selected for this study are thickness and porosity of the electrodes. The thickness is restricted to vary from 22microns to 240microns and the porosity varies from 0.22 to 0.54. Two case studies are carried out using the above-mentioned objective functions and parameters. In the first study, cycling tests of 18650 NCA cathode Li-ion cells are simulated. The cells are charged and discharged using a constant 0.2C rate for 500 cycles. In the second case study a cathode active material more relevant to the electric vehicle industry, Nickel-Manganese-Cobalt-Aluminum (NMCA), is used. Here, the cells are cycled for 5 different charge-discharge scenarios to replicate charge-discharge scenario that an EVs battery module experiences. The results show that the design and optimization methodology can identify cells to satisfy the design objective that extend and improve the pareto front outside the original sampling plan for several practical charge-discharge scenarios which maximize energy density and minimize capacity fade.

<p>  </p> <p>In the last years, lithium-ion batteries (LIBs) have become the most important energy storage system for consumer electronics, electric vehicles, and smart grids. The development of lithium-ion batteries (LIBs) based on current practice allows an energy density increase estimated at 10% per year. However, the required power for portable electronic devices is predicted to increase at a much faster rate, namely 20% per year. Similarly, the global electric vehicle battery capacity is expected to increase from around 170 GWh per year today to 1.5 TWh per year in 2030--this is an increase of 125% per year. Without a breakthrough in battery design technology, it will be difficult to keep up with the increasing energy demand. To that end, a design methodology to accelerate the LIB development is needed. This can be achieved through the integration of electro-chemical numerical simulations and machine learning algorithms.</p> <p><br></p> <p>To help this cause, this study develops a design methodology and framework using Simcenter Battery Design Studio® (BDS) and Bayesian optimization for design and optimization of cylindrical cell type 18650. The materials of the cathode are Nickel-Cobalt-Aluminum (NCA)/Nickel-Manganese-Cobalt-Aluminum (NMCA), anode is graphite, and electrolyte is Lithium hexafluorophosphate (LiPF6). Bayesian optimization has emerged as a powerful gradient-free optimization methodology to solve optimization problems that involve the evaluation of expensive black-box functions. The black-box functions are simulations of the cyclic performance test in Simcenter Battery Design Studio. </p> <p>The physics model used for this study is based on full system model described by Fuller and Newman. It uses Butler-Volmer Equation for ion-transportation across an interface and solvent diffusion model (Ploehn Model) for Aging of Lithium-Ion Battery Cells. The BDS model considers effects of SEI, cell electrode and microstructure dimensions, and charge-discharge rates to simulate battery degradation. Two objectives are optimized: maximization of the specific energy and minimization of the capacity fade. We perform global sensitivity analysis and see that thickness and porosity of the coating of the LIB electrodes that affect the objective functions the most. As such the design variables selected for this study are thickness and porosity of the electrodes. The thickness is restricted to vary from 22 micron to 240 microns and the porosity varies from 0.22 to 0.54. </p> <p>Two case studies are carried out using the above-mentioned objective functions and parameters. In the first study, cycling tests of 18650 NCA cathode Li-ion cells are simulated. The cells are charged and discharged using a constant 0.2C rate for 500 cycles. In the second case study a cathode active material more relevant to the electric vehicle industry, Nickel-Manganese-Cobalt-Aluminum (NMCA), is used. Here, the cells are cycled for 5 different charge-discharge scenarios to replicate charge-discharge scenario that an EVs battery module experiences. The results show that the design and optimization methodology can identify cells to satisfy the design objective that extend and improve the pareto front outside the original sampling plan for several practical charge-discharge scenarios which maximize energy density and minimize capacity fade. </p>