A Comprehensive Review and Application of Metaheuristics in Solving the Optimal Parameter Identification Problems

For many electrical systems, such as renewable energy sources, their internal parameters are exposed to degradation due to the operating conditions. Since the model’s accuracy is required for establishing proper control and management plans, identifying their parameters is a critical and prominent task. Various techniques have been developed to identify these parameters. However, metaheuristic algorithms have received much attention for their use in tackling a wide range of optimization issues relating to parameter extraction. This work provides an exhaustive literature review on solving parameter extraction utilizing recently developed metaheuristic algorithms. This paper includes newly published articles in each studied context and its discussion. It aims to approve the applicability of these algorithms and make understanding their deployment easier. However, there are not any exact optimization algorithms that can offer a satisfactory performance to all optimization issues, especially for problems that have large search space dimensions. As a result, metaheuristic algorithms capable of searching very large spaces of possible solutions have been thoroughly investigated in the literature review. Furthermore, depending on their behavior, metaheuristic algorithms have been divided into four types. These types and their details are included in this paper. Then, the basics of the identification process are presented and discussed. Fuel cells, electrochemical batteries, and photovoltaic panel parameters identification are investigated and analyzed

Using Artificial Intelligence to Predict the Discharge Performance of Cathode Materials for Lithium-ion Batteries Applications

A comprehensive understanding of the composition-structure-property relationships for doped cathode materials used in lithium-ion batteries remains lacking which delays the progress of developing new cathode materials. This thesis proposes that machine learning (ML) techniques can be used to predict the discharge capacities of the cathode materials whilst revealing these underlying relationships. To achieve this, the data for three different doped cathodes are curated from the publications, namely, the doped spinel cathode, LiMxMn2−xO4, the M-doped nickel- cobalt-manganese layered cathode, LiNixCoyMnzM1−x−y−zO2, and the carbon -coated and doped olivine cathode, C/LiM1M2PO4 (M1, M2 denote different metal ions). Several linear and non-linear ML models are trained with the data and compared for the power of predicting initial and higher cycle discharge capacity. Gradient boosting models have shown the best prediction power for predicting the initial and 20th cycle end discharge capacity of 102 doped spinel cathode and the initial and 50th cycle discharge capacity of 168 doped nickel-cobalt-manganese layered cathodes. For the doped spinel cathode, higher discharge capacities at both cycles can be achieved through increasing the material formula mass, reducing the crystal lattice constant and using dopants with smaller electronegativity. For the doped layered cathodes, it is revealed that the higher lithium content, lower formula molar mass, small doping content and doped with low electronegativity dopant are more likely to possess greater capacities at both cycles. Bayesian ridge regression and gradient boosting model are shown to have the highest prediction power over the initial and the 20th cycle discharge capacity of carbon-coated and doped olivine cathode. In addition, the olivine systems with lower dopant content, higher base-metal content and smaller unit cells are shown to be more likely to possess higher capacities at both cycles. Finally, future research directions are presented including the suggestion of involving other new input variables and using principal component analysis and feature selection algorithms to use to improve the model performance

New advances in vehicular technology and automotive engineering

An automobile was seen as a simple accessory of luxury in the early years of the past century. Therefore, it was an expensive asset which none of the common citizen could afford. It was necessary to pass a long period and waiting for Henry Ford to establish the first plants with the series fabrication. This new industrial paradigm makes easy to the common American to acquire an automobile, either for running away or for working purposes. Since that date, the automotive research grown exponentially to the levels observed in the actuality. Now, the automobiles are indispensable goods; saying with other words, the automobile is a first necessity article in a wide number of aspects of living: for workers to allow them to move from their homes into their workplaces, for transportation of students, for allowing the domestic women in their home tasks, for ambulances to carry people with decease to the hospitals, for transportation of materials, and so on, the list don’t ends. The new goal pursued by the automotive industry is to provide electric vehicles at low cost and with high reliability. This commitment is justified by the oil’s peak extraction on 50s of this century and also by the necessity to reduce the emissions of CO2 to the atmosphere, as well as to reduce the needs of this even more valuable natural resource. In order to achieve this task and to improve the regular cars based on oil, the automotive industry is even more concerned on doing applied research on technology and on fundamental research of new materials. The most important idea to retain from the previous introduction is to clarify the minds of the potential readers for the direct and indirect penetration of the vehicles and the vehicular industry in the today’s life. In this sequence of ideas, this book tries not only to fill a gap by presenting fresh subjects related to the vehicular technology and to the automotive engineering but to provide guidelines for future research. This book account with valuable contributions from worldwide experts of automotive’s field. The amount and type of contributions were judiciously selected to cover a broad range of research. The reader can found the most recent and cutting-edge sources of information divided in four major groups: electronics (power, communications, optics, batteries, alternators and sensors), mechanics (suspension control, torque converters, deformation analysis, structural monitoring), materials (nanotechnology, nanocomposites, lubrificants, biodegradable, composites, structural monitoring) and manufacturing (supply chains). We are sure that you will enjoy this book and will profit with the technical and scientific contents. To finish, we are thankful to all of those who contributed to this book and who made it possible.info:eu-repo/semantics/publishedVersio

Improvements on physics-informed models for lithium batteries

The fast adoption of battery electric vehicles (BEV) has resulted in a demand for rapid technological advancements. Strategic areas undergoing this development include lithium-ion energy storage. This is inclusive of electrochemical design improvements and advanced battery management control architectures. Field objectives for these developments include but are not limited to, reductions in cell degradation, improvements in fast charging capabilities, increases in system-level energy densities, and a reduction in energy storage costs. Improvements in online predictive models provide a path for realising these objectives through informed control interactions, reduced degradation effects, and decreased vehicle costs. This thesis contributes to these developments through improvements in fast physics-informed battery models for both lithium-ion and lithium-metal batteries. The key novelty presented is the improvement of real-time, physics-based electrochemical model generation for lithium-ion batteries. A computationally informed realisation algorithm is developed and expands on the previously published realisation algorithm methods. An open-source Julia-based architecture is presented and provides a high-performance implementation while maintaining dynamic language capabilities for fast code development, and readability. A performance improvement of 21.7\% was shown over the previous discrete realisation algorithm, with an additional framework improvement of 3.51 times when compared to the previously published framework. A methodology for the creation and modification of the reduced order models via in-vehicle hardware is presented and validated through an ARM-based model generation investigation. This addition provides a versatile method for cell degradation prediction over the battery life and can provide an interface for improved prediction of cell-to-cell variations. This methodology is applied to intercalation-based NMC/graphite batteries and is both numerically and experimentally validated. A further element of novelty produced in this thesis includes advancements in lithium-metal phase-field representations through the creation of a Julia-based numerical framework optimised for high-performance predictions. This framework is then utilised as a ground truth model for the development of an autoregressive physics-informed neural solver aimed to predict lithium-metal evolution. Through the implementation of the physics-informed neural solver, a reduction in the numerical prediction time of 40.3\% compared to the underlying phase-field representation was achieved. This methodology enables fast lithium-morphology predictions for improved design space explorations, online deployment, and advancements in electrodeposition material discovery for lithium-metal batteries

Performance Analysis of Tree Seed Algorithm for Small Dimension Optimization Functions

Tree-Seed Algorithm (TSA) simulates the growth of trees and seeds on a land. TSA is a method proposed to solve continuous optimization problems. Trees and seeds indicate possible solutions in the search space for optimization problems. Trees are planted in the ground at the beginning of the search and each tree produces several seeds during iterations. While the trees were selected randomly during seed formation, the tournament selection method was used and also hybridized by adding the C parameter, which is the acceleration coefficient calculated according to the size of the problem. In this study, continuous optimization problem has been solved by the hybrid method. First, the performance analyses of the five best known numerical benchmark functions have been done, in both TSA and hybrid method TSA with 2, 3, 4 and 5 dimensions, and 10-50 population numbers. After that, well-known algorithms in the literature like Particle Swarm Optimization (PSO), TSA, Artificial Bee Colony (ABC), Harmony Search (HS), as well as hybrid method TSA (HTSA) have been applied to twenty-four numerical benchmark functions and the performance analyses of algorithms have been done. Hopeful and comparable conclusions based on solution quality and robustness can be obtained with the hybrid method

Nanoinformatics

Machine learning; Big data; Atomic resolution characterization; First-principles calculations; Nanomaterials synthesi

Nanoinformatics

Machine learning; Big data; Atomic resolution characterization; First-principles calculations; Nanomaterials synthesi

Development of a mathematical model to enable optimal efficiency of the indabuko lithium-ion battery.

Masters Degree. University of KwaZulu-Natal, Pietermaritzburg.Cathode materials are the foremost primary challenge for the vast scale application of lithium-ion batteries in electric vehicles and the stockpiles of power. Foreseeing the properties of cathode materials is one of the central issues in energy storage. In the recent past, density functional theory (DFT) calculations aimed at materials property predictions offered the best trade-off between computational cost and accuracy compared to experiments. However, these calculations are still excessive and costly, limiting the acceleration of new materials discovery. Now the results from different computational materials science codes are made available in databases, which permit quick inquiry and screening of various materials by their properties. Such gigantic materials databases allow a dominant data-driven methodology in materials discovery, which should quicken advancements in the field. This study was aimed at applying machine learning algorithms on existing computations to make precise predictions of physical properties. Thus, the dissertation primary goal was build best ML models that are capable of predicting DFT calculated properties such as, formation energy, energy band-gap and classify materials as stable or unstable based on their thermodynamic stability. It was established that the algorithms only require the chemical formula as input when predicting materials properties. The theoretical aspect of this work describes the current machine learning algorithms and presents "descriptors"-representations of materials in a dataset that plays a significant role in prediction accuracy. Also, the dissertation examined how various descriptors and algorithms influence learning model. The Catboost Regressor was found to be the best algorithm for determining all the properties that were selected in this study. Results indicated that with appropriate descriptors and ML algorithms it is feasible to foresee formation energy with coefficient of determination (R2) of 0.95, mean absolute error (MAE) of 0.11 eV and classify materials into stable and unstable with 86% of accuracy and area under the ROC Curve (AUC) of 89%. Lastly, we build a web application that allow users to predict material properties easily

Characterization and Morphological Analysis of Porous Electrodes for Lithium-Ion Batteries

Climate change is one of the greatest challenges of the century. Compared to the pre-industrial era, the average global temperature has already risen by 1 °C (1.5 °C in Germany). The temperature increase is caused by the emission of greenhouse gases, which convert sunlight reflected into the atmosphere into heat. Around 20% of the CO2 emissions in Germany are attributed to the transport sector. Electro mobility represents a possible solution to this problem. Powerful lithium-ion batteries (LIBs) are needed for mobile storage of electricity from renewable energy. Three main approaches are being pursued to improve the performance of LIBs: the search for new materials, the development of new battery concepts, and the optimization of existing systems. This work takes the latter approach by imaging and studying the electrode morphology using tomography. Detailed morphological analysis and simulations are used to identify microstructural kinetic limitations. The results are compared with electrochemical characterization methods. In the following, the results of the five chapters of this cumulative dissertation are summarized. Chapters 1–3 are related to the study of transport limitations in batteries using a liquid electrolyte, Chapter 4 deals with all-solid-state batteries, and Chapter 5 applies the reconstruction approach to a hierarchical porous material. In Chapter 1, transport limitations of an electrode are detected by both reconstruction-simulation (RS) and electrochemical measurements, and the results of the two approaches are compared to each other. The aim of the study is to determine the ionic tortuosity in both ways to quantify transport limitations in the pore space, filled by a liquid electrolyte. Graphite, which is a common anode material, is chosen as the active material. First, graphite electrodes with different thicknesses are investigated by electrochemical impedance spectroscopy (EIS) in a symmetrical cell setup. The resulting spectra are fitted using the transmission line model (TLM), which describes the impedance of porous electrodes. The analysis reveals an ionic tortuosity of τ_EIS=7.3. Second, one graphite electrode is physically reconstructed over the entire cross-section using FIB-SEM tomography. For this purpose, the pore space of the electrode is infiltrated by an osmium-based contrast agent. The space which is filled by the liquid electrolyte in normal battery operation is thus directly imaged and the contrast of the resulting image stack is enhanced, facilitating an accurate reconstruction. A comprehensive morphological analysis is conducted featuring porosity profiles, the geometric tortuosity, and a chord length distribution (CLD) of the solid phase and void space. All analyses are performed with regards to the spatial direction, showing that the flaky graphite particles form a distinct anisotropic microstructure. This leads to strong transport hindrances in the direction perpendicular to the current collector. The reconstruction volume is verified to be representative by a finite-size analysis, which is indispensable in order to obtain reliable results. Diffusion simulations based on a random-walk approach yield a similar tortuosity value of τ_RS=6.55, which is within the experimental error of τ_EIS. Consequently, this study shows that long-range transport simulations (without considering double-layer formation) and EIS combined with TLM (ion transport in the pores and double-layer formation) give comparable results even for a highly anisotropic microstructure. Compared to FIB-SEM tomography along with numerical simulations, EIS is significantly faster, cheaper, and easier to apply, and it is available in almost every electrochemical laboratory. However, the underlying microstructural features causing steric transport hindrances can only be analyzed by appropriate tomography methods. EIS screenings can be used to detect transport limitations of newly designed electrodes. Thus, the results of this study may contribute to the future development of more powerful electrodes. In Chapter 2, the impedance of electrodes with variable thickness is examined for different liquid electrolyte systems. For this purpose, batteries are first cycled using a tetraglyme-based solvate ionic liquid (IL), a conventional carbonate-based electrolyte, and a LiFSI in IL electrolyte system. The area-specific resistances are estimated based on the overvoltages at 50% state of charge, which increase in the order; carbonate-based electrolyte < IL < solvate IL. The different electrolyte systems are characterized based on the electrode thickness by means of EIS. Special attention is paid to the impedance at 10 4 Hz, since this frequency corresponds approximately to the time scale of typical cyclization rates of 1–2 C. The impedances of the electrolyte systems increase in the same order as it was observed in the cycling experiments. Next, the analytical model of Huang and Zhang is used to shed light on the individual contributions to the overall electrode impedance for the carbonate-based electrolyte and the solvate IL. This model calculates the electrode impedance, taking into account salt concentration polarization in the electrolyte-filled pore space. It is applicable to electrolyte systems consisting of one type each of cation and anion in a solvent. Therefore, the LiFSI in IL electrolyte cannot be analyzed by this model. At 10-4 Hz, only a weak dependence on the electrode thickness is observed for the real part and the modulus of the complex impedance in the range of 50–100 µm. Using a generalized TLM, the impedance contributions of ion transport and thickness-dependent charge transfer as well as solid phase diffusion are analyzed separately. The impedance of ion transport for both the solvate IL and the carbonate-based electrolyte is higher than the contribution from charge transfer and solid phase diffusion at 10 4 Hz and for thicknesses between 50–150 µm. This explains the low dependence on thickness of the impedance spectra and leads to the conclusion that greater electrode thicknesses than the conventional 80 µm would be possible, given that the morphological properties can be kept constant over the entire electrode. Chapter 3 examines the influence of the carbon-binder domain (CBD) on Li+ charge transport in the electrolyte phase by using a RS approach and compares the results with EIS experiments. The morphology of the electrolyte-filled pore space in LIBs is influenced by the microstructure of the solid components: active material (AM) particles, binder, and conductive carbon. The binder and conductive carbon form an interpenetrating nanoporous phase, the CBD. While the µm-scaled AM particles can be easily reconstructed by 3D tomography, the CBD is often not taken into account due to its small feature size. In this chapter, a LiCoO2 (LCO) composite cathode is physically reconstructed by means of FIB-SEM tomography to determine the Li+ transport tortuosity and to morphologically characterize the CBD. EIS experiments in the framework of the TLM are conducted to determine the ionic tortuosity experimentally and are compared with the RS approach. The three-phase reconstruction provides both the hitherto highest reported resolution down to a voxel size of (13.9 × 13.9 × 20.0) nm3, and an unprecedented large volume with a minimum edge length of 20 µm. This enables a representative description of the interstitial pore space. A detailed morphological analysis is presented to characterize the morphology of the void space featuring CLD, specific surface area determination, connectivity analysis, and calculation of the geometric tortuosity. The results show that the microstructural properties of the cathode are affected by the presence of the CBD spanning the void space as a convoluted network and leading to more tortuous and constricted Li+ transport pathways. Pore-scale numerical diffusion simulations reveal a significantly higher ionic tortuosity of 1.9 when the CBD is taken into account compared to 1.5 without CBD, which cannot be solely attributed to the lower porosity. The RS analysis underscores that only pore-scale simulations in physical reconstructions including the CBD can reproduce experimental tortuosity values derived from EIS. In Chapter 4, the morphology of two sheet-type all-solid-state battery (ST-ASSB) cathodes with different solid electrolytes (SEs) is investigated to identify kinetic limiting features. The slurry-based manufacturing process of ST-ASSBs is comparable to that of conventional lithium-ion batteries and is thus relevant for eventual mass production. The sulfur-based SEs are β-LPS (β-Li3PS4) and LPSI (2 Li3PS4∙LiI) with conductivities of 0.2 mS cm 1 and 0.8 mS cm 1, respectively. While β-LPS is composed of mesoporous nanoparticles, the LPSI particles exhibit sizes up to the µm range and no intrinsic porosity. Small state-of-the-art NMC 85|05|10 particles coated with LiNbO3 are used as cathode active material (CAM). Three-phase FIB-SEM based reconstructions of large cathode volumes in high resolution reveal structurally representative and realistic models of the SE, CAM particles, and void space. The binder, which is distributed as a thin layer over all surfaces, cannot be resolved due to its small feature size and poor contrast. The volume fractions found in the reconstructions suggest that, for β-LPS, the binder accumulates predominantly within the nanoparticulate SE phase due to the high intrinsic surface area. For LPSI, it is distributed over all interfaces. Void space is dead space in ASSBs as it makes transport paths in SE more tortuous, prevents charge transfer at the SE–CAM interface, and reduces the volumetric energy density of the battery. For the β-LPS-based cathode, a small void fraction of 1 vol% can be found, while LPSI exhibits a much higher fraction of 11 vol%. The voids in the LPSI-based cathode are larger compared to β-LPS and mainly found at the SE or SE–CAM interface. For β-LPS, the voids are predominantly surrounded by CAM. This explains the larger active surface area of 87% for β-LPS, while 62% of the CAM surface is in direct contact with the SE for LPSI. An analysis of CAM connectivity shows that >99% of the CAM volume is directly connected in each case, making electron transport within the cathode uncritical. Numerical transport simulations show that the ionic tortuosity of the electrolyte phase of the LPSI sample is twice that of the β-LPS sample. In contrast, cycling experiments reveal that the LPSI sample has a higher discharge capacity (178 mAh/g vs. 150 mAh/g) and lower overvoltage. Using a general TLM, the individual contributions to the battery impedance were estimated to draw conclusions about kinetic limitations in the two samples. The charge transfer at the SE–CAM interface accounts for by far the largest impedance, while Li chemical diffusion in the CAM and ionic transport in the SE phase account for only a comparably small fraction. Due to their similar chemical composition, both electrolytes exhibit a similar charge transfer resistance. However, due to the higher effective SE–CAM interface of the LPSI sample, a lower effective charge transfer resistance is obtained, which explains the lower overvoltage. Consequently, especially in lowering the interfacial impedance, there is still great potential to further improve the performance of ST-ASSB. In the fifth Chapter, the physical reconstruction technique is applied to hierarchical porous materials (HPMs), which have a high potential for use in the field of energy storage and conversion. HPMs are a class of functional materials characterized by a large specific surface area and an interconnected pore space with high accessibility. The study presents a universal laser-based procedure for generating metal oxide HPMs with cauliflower-like morphology. Based on a facile nanosecond pulsed laser-treatment, the manufacturing process is easy to implement, solvent-free, and scalable. The resulting hybrid micro-/nanostructures can be generated on a variety of metallic substrates over a wide range of melting points. The morphology of the superstructures can be directly controlled by varying the laser parameters. The formation process is investigated in detail by means of FIB-SEM tomography. For this purpose, the cauliflower-like structures are generated on copper metal, embedded in epoxy resin, and physically reconstructed. Cross-sections of the superstructures show a ring-like pattern similar to tree rings. These rings can be fitted by ellipses with a constant center point and linearly rising elliptical axes, which makes the structures resemble an ideal ellipsoid. The distance between the single rings is constant and depends on the laser scan line distance. The porosity increases towards the outer surface, resulting in a large external surface area. A hierarchical network of pores with diameters from nanometer to micrometer is created. During generation, the laser scans the metal surface in a linear pattern, causing material to melt and partially evaporate whereby the metal partially oxidizes. In a self-organization process, microstructures are created which grow layer by layer through stepwise recondensation due to the meandering laser path. A complex and over several orders of magnitude self-similar morphology is formed. Interestingly, the determined fractal dimension corresponds to that of natural cauliflower. The concept can be applied to a variety of materials, especially transition metals, such as those used as cathode material in LIBs. In conclusion, this work provides new insights into the microstructure of battery electrodes. For this purpose, a protocol for two- and three-phase reconstructions is developed and applied to a variety of different samples. It is shown that only direct imaging of the morphology provides reliable conclusions about the reason for transport limitations and morphological heterogeneity. FIB-SEM tomography is the method of choice for physical reconstructions of electrodes as it achieves a sufficiently high resolution and provides a high sensitivity towards light elements, such as those found in the CBD. Optimization of the electrode morphology to reduce transport limitations will help to make LIBs even more efficient in the future

Traffic data based ideal airport suggestions providing regional service in disasters

Bu çalışma, önceki benzer çalışmalardan üç ana yönden ayrılmaktadır. Birincisi, yöntem olarak diğerlerinden ayrılarak ülke çapında sadece tek bir lokasyondaki havalimanı tesisi değil kümeleme yöntemi uygulanarak bölgesel hizmet verebilecek en uygun tesisler tespit edilmiştir. İkincisi, önceki çalışmalarda İstanbul Havalimanı henüz hizmete girmediğinden dolayı değerlendirmeye alınmamışken, bu çalışmada optimizasyon hesaplarına dahil edilmiştir. Üçüncüsü, yeni durumun önceki çalışmalara nazaran daha güncel verilerle ve daha güncel bir optimizasyon yöntemi olan ağaç tohum algoritması kullanılarak optimum çözümler üretilmiştir. Trafik verileri, trafiğe dayalı ağırlık katsayıları ve ulaşım mesafelerinin elde edildiği konum verilerine dayanarak yapılan analizler sonucunda, toplam on altı havalimanı kendi bölgelerine, özellikle herhangi bir felaket sırasında veya sonrasındaki acil durumlarda, servis sağlayabilecek ideal havalimanları bu çalışmada ortaya çıkarılarak tavsiye edilmiştir. Bu araştırmanın önerileri doğrultusunda, herhangi bir acil durumda gerekli olan ve havayolu ile sağlanabilecek hizmetler sayesinde zaman ve gider kaybının azaltılması gibi amaçlar gözetilirken daha da önemlisi can kaybının en az düzeye indirilmesi konusunda ilerleme kaydedilmesi beklenmektedir.This study differs from previous similar studies in three main directions. Firstly, the location of the most suitable facilities that can provide regional service were determined by applying clustering method. Thus, not only one airport facility in a single location serving across the country was determined, which separates this research from the others. Secondly, in the previous studies, Istanbul Airport was not taken into account because it has not been put into service. Therefore, this study takes that particular airport into consideration. Thirdly, optimum solutions for such new situation were produced with more up-to-date data and using a modern optimization method, the tree seed algorithm. As a result of the analyzes made based on traffic data, traffic-based weight coefficients and location data from which transportation distances were obtained, a total of sixteen ideal airports were recommended that can provide service to their regions, especially during emergencies or after any disaster. In accordance with the suggestions of this research, it is expected that progress will be made in minimizing the loss of time and expense, and especially minimizing the loss of life thanks to the services which can be provided by air transportation in any emergency.No sponso