An advanced Lithium-ion battery optimal charging strategy based on a coupled thermoelectric model

Lithium-ion batteries are widely adopted as the power supplies for electric vehicles. A key but challenging issue is to achieve optimal battery charging, while taking into account of various constraints for safe, efficient and reliable operation. In this paper, a triple-objective function is first formulated for battery charging based on a coupled thermoelectric model. An advanced optimal charging strategy is then proposed to develop the optimal constant-current-constant-voltage (CCCV) charge current profile, which gives the best trade-off among three conflicting but important objectives for battery management. To be specific, a coupled thermoelectric battery model is first presented. Then, a specific triple-objective function consisting of three objectives, namely charging time, energy loss, and temperature rise (both the interior and surface), is proposed. Heuristic methods such as Teaching-learning-based-optimization (TLBO) and particle swarm optimization (PSO) are applied to optimize the triple-objective function, and their optimization performances are compared. The impacts of the weights for different terms in the objective function are then assessed. Experimental results show that the proposed optimal charging strategy is capable of offering desirable effective optimal charging current profiles and a proper trade-off among the conflicting objectives. Further, the proposed optimal charging strategy can be easily extended to other battery types

Metaheuristics algorithms to identify nonlinear Hammerstein model: A decade survey

Metaheuristics have been acknowledged as an effective solution for many difficult issues related to optimization. The metaheuristics, especially swarm’s intelligence and evolutionary computing algorithms, have gained popularity within a short time over the past two decades. Various metaheuristics algorithms are being introduced on an annual basis and applications that are more new are gradually being discovered. This paper presents a survey for the years 2011-2021 on multiple metaheuristics algorithms, particularly swarm and evolutionary algorithms, to identify a nonlinear block-oriented model called the Hammerstein model, mainly because such model has garnered much interest amidst researchers to identify nonlinear systems. Besides introducing a complete survey on the various population-based algorithms to identify the Hammerstein model, this paper also investigated some empirically verified actual process plants results. As such, this article serves as a guideline on the fundamentals of identifying nonlinear block-oriented models for new practitioners, apart from presenting a comprehensive summary of cutting-edge trends within the context of this topic area

A New Approach to Model Parameter Determination of Self-Potential Data using Memory-based Hybrid Dragonfly Algorithm

A new approach based on global optimization technique is applied to invert Self-Potential (SP) data which is a highly nonlinear inversion problem. This technique is called Memory-based Hybrid Dragonfly Algorithm (MHDA). This algorithm is proposed to balance out the high exploration behavior of Dragonfly Algorithm (DA), which causes a low convergence rate and often leads to the local optimum solution. MHDA was developed by adding internal memory and iterative level hybridization into DA which successfully balanced the exploration and exploitation behaviors of DA. In order to assess the performance of MHDA, it is firstly implemented to invert the single and multiple noises contaminated in synthetic SP data, which were caused by several simple geometries of buried anomalies: sphere and inclined sheet. MHDA is subsequently implemented to invert the field SP data for several cases: buried metallic drum, landslide, and Lumpur Sidoarjo (LUSI) embankment anomalies. As a stochastic method, MHDA is able to provide Posterior Distribution Model (PDM), which contains possible solutions of the SP data inversion. PDM is obtained from the exploration behavior of MHDA. All accepted models as PDM have a lower misfit value than the specified tolerance value of the objective function in the inversion process. In this research, solutions of the synthetic and field SP data inversions are estimated by the median value of PDM. Furthermore, the uncertainty value of obtained solutions can be estimated by the standard deviation value of PDM. The inversion results of synthetic and field SP data show that MHDA is able to estimate the solutions and the uncertainty values of solutions well. It indicates that MHDA is a good and an innovative technique to be implemented in solving the SP data inversion problem

A Review of Geophysical Modeling Based on Particle Swarm Optimization

This paper reviews the application of the algorithm particle swarm optimization (PSO) to perform stochastic inverse modeling of geophysical data. The main features of PSO are summarized, and the most important contributions in several geophysical felds are analyzed. The aim is to indicate the fundamental steps of the evolution of PSO methodologies that have been adopted to model the Earth’s subsurface and then to undertake a critical evaluation of their benefts and limitations. Original works have been selected from the existing geophysical literature to illustrate successful PSO applied to the interpretation of electromagnetic (magnetotelluric and time-domain) data, gravimetric and magnetic data, self-potential, direct current and seismic data. These case studies are critically described and compared. In addition, joint optimization of multiple geophysical data sets by means of multi-objective PSO is presented to highlight the advantage of using a single solver that deploys Pareto optimality to handle diferent data sets without conficting solutions. Finally, we propose best practices for the implementation of a customized algorithm from scratch to perform stochastic inverse modeling of any kind of geophysical data sets for the beneft of PSO practitioners or inexperienced researchers

An integrated simulation tool proposed for modeling and optimization of CHP units

Master's thesis in Petroleum engineeringIn this project, a novel framework for CHP optimization is proposed. The objective of the study was to develop an automatic optimization tool based on the integration of IPSEpro simulation software and MATLAB programming environment. The data exchange between these components was organized via COM interface. An experimentally validated model of the commercial AET100 CHP unit was utilized. The CHP was considered as a part of a grid. Therefore electricity trading possibility was taken into account. The system was extended to polygeneration by implementing a solar panel as an additional power source. The objective was to minimize the cost function, which consists of operational and capital investments costs, under a set of constraints. For solving the problem, the Genetic Algorithm was applied. As an addition to the study, two other algorithms (Particle Swarm Optimization and Differential Evolution) were also tested. The applying a tool to real data was not considered in the project. However, an optimization was done for test data to show the performance of a developed framework. The test optimization was done for the 24-hours period in July and December, with different electricity and gas price profiles and various ambient conditions. The obtained results were analyzed in details. It was shown that the proposed optimization tool provides appropriate results. It is flexible and has a good potential to be further extended and developed

Development of a platform for simulating and optimizing thermoelectric energy systems

Thermoelectrics are solid state devices that can convert thermal energy directly into electrical energy. They have historically been used only in niche applications because of their relatively low efficiencies. With the advent of nanotechnology and improved manufacturing processes thermoelectric materials have become less costly and more efficient As next generation thermoelectric materials become available there is a need for industries to quickly and cost effectively seek out feasible applications for thermoelectric heat recovery platforms. Determining the technical and economic feasibility of such systems requires a model that predicts performance at the system level. Current models focus on specific system applications or neglect the rest of the system altogether, focusing on only module design and not an entire energy system. To assist in screening and optimizing entire energy systems using thermoelectrics, a novel software tool, Thermoelectric Power System Simulator (TEPSS), is developed for system level simulation and optimization of heat recovery systems. The platform is designed for use with a generic energy system so that most types of thermoelectric heat recovery applications can be modeled. TEPSS is based on object-oriented programming in MATLAB®. A modular, shell based architecture is developed to carry out concept generation, system simulation and optimization. Systems are defined according to the components and interconnectivity specified by the user. An iterative solution process based on Newton\u27s Method is employed to determine the system\u27s steady state so that an objective function representing the cost of the system can be evaluated at the operating point. An optimization algorithm from MATLAB\u27s Optimization Toolbox uses sequential quadratic programming to minimize this objective function with respect to a set of user specified design variables and constraints. During this iterative process many independent system simulations are executed and the optimal operating condition of the system is determined. A comprehensive guide to using the software platform is included. TEPSS is intended to be expandable so that users can add new types of components and implement component models with an adequate degree of complexity for a required application. Special steps are taken to ensure that the system of nonlinear algebraic equations presented in the system engineering model is square and that all equations are independent. In addition, the third party program FluidProp is leveraged to allow for simulations of systems with a range of fluids. Sequential unconstrained minimization techniques are used to prevent physical variables like pressure and temperature from trending to infinity during optimization. Two case studies are performed to verify and demonstrate the simulation and optimization routines employed by TEPSS. The first is of a simple combined cycle in which the size of the heat exchanger and fuel rate are optimized. The second case study is the optimization of geometric parameters of a thermoelectric heat recovery platform in a regenerative Brayton Cycle. A basic package of components and interconnections are verified and provided as well

Automated calibration of a Carbon dynamic model for lakes and reservoirs : (calibração automática de um modelo de dinâmica de Carbono em lagos e reservatórios)

Orientador : Michael MannichCoorientador : Cristóvão Vicente Scapulatempo FernandesDissertação (mestrado) - Universidade Federal do Paraná, Setor de Tecnologia, Programa de Pós-Graduação em Engenharia de Recursos Hídricos e Ambiental. Defesa: Curitiba, 16/03/2017Inclui referências e apêndicesResumo: A carência de medidas de fluxos de gases de efeito estufa (GEE), junto com as incertezas referentes às extrapolações de emissões pontuais para emissões totais, resultam em conclusões imprecisas referente a participação de reservatórios no clima global. O modelo matemático CICLAR é usado para simular fluxos de CO2 e CH4 por 45 anos no reservatório de Capivari, Paraná, Brasil. O modelo é estruturado em compartimentos de diferentes formas de carbono, como o carbono inorgânico dissolvido (CID) e o carbono orgânico particulado vivo (COPL). Processos químicos de transferência de massa entre compartimentos são modelados como reações de primeira ordem e de saturação que são controladas por parâmetros numéricos. O valor destes parâmetros são calibrados através da minimização de diferenças entre dados observados e modelados através de algoritmos de calibração. O algoritmo metaheuristico de Otimização Multi-objetivo por Enxame de Particulas Combinada de Pareto (CPMOPSO), que combina técnicas de seleção de líderes, mutações e subenxames, foi desenvolvido e aplicado como método de otimização. O algoritmo de calibração automática utiliza dados provenientes da calibração manual. Quatro cenários foram analisados: o avaliativo, que usa os primeiros 30 e os últimos 15 anos de dados do reservatório para calibrar e validar o modelo; e o retrospective, o prospectivo e o ideal, que usam 9 anos de dados, distribuídos de maneiras diferentes, para calibrar o modelo. A qualidade dos resultados da calibração foi positivamente considerada através do uso do cenário avaliativo. Os resultados da calibração sob os cenários retrospectivo e prospectivo mostraram que o algoritmo tende a superestimar emissões de metano se dados mal distribuídos são utilizados. A otimização sob o cenário ideal obteve melhores resultados e mostrou que a disposição dos dados tem maior impacto do que a quantidade sobre a calibração. Todas as soluções sob todos os cenários obtiveram soluções com coeficientes de Nash-Sutcliffe superiores a 0.95 para o período de calibração. As distribuições acumuladas das médias dos Potenciais de Aquecimento Global (GWP) mostraram que a maioria das soluções calibradas classificam o reservatório como um sumidouro de dióxido de carbono equivalente, absorvendo até 90 Gg de CO2 eq. Estimativas alternativas de estoque de carbono foram utilizadas para calibrar o modelo em um escopo em que nenhuma solução prévia é conhecida. São feitas considerações adicionas referentes a aplicação de métodos de análise de incertezas e agregação Bayesiana para melhor aferir múltiplos conjuntos de parâmetros. Palavras-chaves: Modelagem matemática. Dinâmica do carbono. Gases de efeito estufa. Potencial de aquecimento global. Enxame de partículas. Dominância de Pareto.Abstract: The low availability of measured greenhouse gas (GHG) fluxes for lakes and reservoirs, coupled with uncertainties regarding extrapolating total reservoir emissions from point measurements, result in inaccurate conclusions regarding the role of reservoirs in the global climate. The Carbon Cycle in Lakes and Reservoirs (CICLAR) model is used to study potential contributions, through carbon dioxide (CO2) and methane (CH4) emissions, of the Capivari reservoir, Brazil, since its construction in 1970. The model is structured in compartments for different carbon forms, such as dissolved inorganic carbon (DIC) and live particulate organic carbon (POCL), and model chemical processes as first order reactions controlled by numerical parameters. The values of these parameters are calibrated by minimizing differences between original and modeled data through an optimization algorithm. The Combined Pareto Multi-objective Particle Swarm Optimization (CPMOPSO) metaheuristic algorithm, which combines leader selection, mutation and subswarm techniques, is developed and successfully used as the optimization technique. The automated calibration algorithm uses data originated from the manual calibration. Four calibration scenarios are used to analyze the impact of data disposition in the calibration results: the evaluative scenario that has the initial 30 years to calibrate and the final 15 to validate the model; and the retrospective, prospective and ideal scenarios, that uses 9 years of data differently distributed. The evaluative data scenario is used to assess the quality of the calibration results, which successfully fit the validation data. The retrospective and prospective scenario are used to analyze the performance of the calibration under unevenly spread data, and the results show that the model had a bias to overestimate methane emissions. The calibration under the ideal scenario is used to show that having evenly spread data has a bigger impact on calibration results than having larger amounts of data. All calibrated solutions for all scenarios present Nash-Sutcliffe coefficient values higher than 0.95 for the calibration period. The cumulative distribution of average Global Warming Potential (GWP) indexes shows that most calibrated solutions estimated that the Capivari reservoir is a sinkhole for equivalent carbon dioxide and that it can absorb up to 90 Gg of equivalent CO2. Alternative carbon stock estimations are used to calibrate the model under a framework in which the results cannot be validated due to no previous solutions being known. Further consideration are drawn regarding the application of uncertainty analysis and Bayesian aggregation methods to better assess the combination of multiple set of parameters. Keywords: Mathematical modeling. Carbon dynamics. Greenhouse gases. Global warming potential. Particle swarm optimization. Pareto dominance

Transformation Thermotics and Extended Theories

This open access book describes the theory of transformation thermotics and its extended theories for the active control of macroscopic thermal phenomena of artificial systems, which is in sharp contrast to classical thermodynamics comprising the four thermodynamic laws for the passive description of macroscopic thermal phenomena of natural systems. This monograph consists of two parts, i.e., inside and outside metamaterials, and covers the basic concepts and mathematical methods, which are necessary to understand the thermal problems extensively investigated in physics, but also in other disciplines of engineering and materials. The analyses rely on models solved by analytical techniques accompanied by computer simulations and laboratory experiments. This monograph can not only be a bridge linking three first-class disciplines, i.e., physics, thermophysics, and materials science, but also contribute to interdisciplinary development