Luleå University of Technology Publications
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Graphene-PVDF composite membrane for piezoelectric nanogenerators and lithium-ion batteries
No full textHerein, we introduce a composite membrane comprising polyvinylidene fluoride/graphene nanosheets (PVDF/graphene) for applications in piezoelectric nanogenerators (PENGs) and lithium-ion batteries (LIBs), where the graphene nanosheets play a vital role in enhancing the piezoelectric properties, surface energy, and porosity. A comparative analysis of the pure PVDF and the PVDF/graphene is conducted to evaluate their piezoelectric performance and suitability as separators in LIBs. The PVDF/graphene composite membrane produced a significantly improved piezoelectric output of ∼10.8 V under a force of 75 N, while the pure PVDF membrane exhibited only ∼3.7 V under the same conditions. Additionally, the Li//PVDF/graphene//graphite half-cell retained ∼81.3% of its specific capacity and maintained a coulombic efficiency of over 99.2% after 100 cycles at a 0.2 C rate. In contrast, the Li//pure PVDF//graphite half-cell retained only ∼48.6% specific capacity. Furthermore, in a full-cell configuration, the graphite//PVDF/graphene//LCO cell demonstrated excellent stability, retaining ∼88% of its specific capacity after 50 cycles, whereas the cell with pure PVDF membrane retained only 38%. Therefore, the PVDF/graphene nanosheet composite membrane has the potential to be used as a piezoelectric membrane in PENGs and as a separator in LIBs.Funder: National Centre for Photovoltaic Research and Education (NCPRE); J. Gust. Richert Foundation (2023-00824);Fulltext license: CC BY-NC</p
Relation between hosting capacity of MV and LV networks and a joint hosting capacity assessment
No full textSolar PV connections at medium voltage (MV) level can impact low voltage (LV) networks by reducing their margin for installing additional PV units. This paper presents an approach to estimate the hosting capacity of distribution networks, including an integrated model of MV-LV networks and a visualisation method for assessing the sharing of hosting capacity between voltage levels. The objective is to quantify the mutual influence of MV and LV connections and to support the planning and management of connection requests. The proposed visualisation method illustrates the trade-off between MV and LV production units, accounting for both voltage and loading limitations, and provides a good representation of how PV connections at one voltage level affect the other. Results show that the coupling between MV and LV networks significantly impacts the hosting capacity, how it can be shared, and that constraints at either level can limit future connections. The method facilitates the identification of the voltage level imposing the most significant limitations and thus also supports the assessment of where and which mitigation strategies would be more effective. Overall, the method highlights the importance of a joint assessment considering the MV-LV coupling to support informed planning decisions. Funder: Skellefteå Kraft;Full text license: CC BY;This article has previously appeared as a manuscript in a thesis.</p
Reliability of SWMM for Predicting Performance of Field-Scale Bioretention Systems
No full textModeling bioretention systems using the Storm Water Management Model (SWMM) is a common practice. However, there is limited observational evidence to determine how accurately and reliably the model performs. This study compared the measured outflow from the underdrain of four bioretention systems with SWMM modeling results, providing critical insights into the models’ applicability and limitations. Results indicated that prior to calibration, the SWMM captures peak flow characteristics and the shape of hydrographs reasonably well. However, calibration improved the performance for flow peaks, resulting in better Nash-Sutcliffe efficiency, for example, from 0.25 to 0.70 in bioretention cell S1. Also, the uncertainty in outflow predictions varied between different bioretention systems, with the width of the uncertainty band varying by up to a factor 2.5 between the systems with the most and least uncertainty in the model predictions. This study found SWMM to be a reliable tool for modeling bioretention hydrology, with reliability varying between systems and improving notably after calibration.Funder: Dag&Nät;Full text license: CC BY;This article has previously appeared as a manuscript in a thesis.</p
Employing artificial intelligence to predict δ¹⁸O and δ²H isotope ratios in precipitation in Iraq under changing climate patterns
No full textUnderstanding precipitation dynamics in arid regions such as Iraq is of paramount importance in hydrological and climatological studies, as it is a key approach to water resources management and climate change adaptation. This study aims to develop a mathematical predictive model for rainfall isotopic values using machine learning techniques. Stable isotope data for oxygen (δ¹⁸O) and deuterium (δ²H) in precipitation were collected from 32 meteorological stations distributed across Iraq over a 14-year period (2010–2024). The dataset also included meteorological parameters for these stations, including precipitation amount, air temperature, relative humidity, and calculated station elevation. Several machine learning algorithms (i.e., SVM, GBR, ANN, CatBoost, XGBoost, and RF) were employed to compare predicted isotopic values with actual readings, accounting for rainfall characteristics and patterns. The results demonstrated that the RF model achieved superior predictive performance, with a calibration coefficient (R²) of 0.89 in the testing set, indicating strong predictive capability. This model also recorded the lowest mean absolute error (MAE) of 1.39 and the lowest root mean square error (RMSE) of 3.5 compared to the other algorithms, reflecting improved predictive accuracy. These findings confirm the effectiveness of integrating machine learning, particularly the RF approach, in enhancing the modeling of isotopic signature predictions in environmental studies. Furthermore, they highlight the potential of AI-based models as powerful tools for reconstructing historical isotopic datasets, supporting climate variability assessment and sustainable water resources management in arid and semi-arid regions.Full text license: CC BY</p
Studies and design of space optical payloads for in-orbit RPOD applications
No full textThe growing importance of in-orbit services in the evolving space sector brings a new set of challenges to ensure the reliability and safety of these missions. Industry efforts are now focused on exploiting advancing technologies such as artificial intelligence and state-of-the-art robotics to make these operations progressively more independent from ground-based control. In this context, it is crucial to provide autonomous systems responsible for Guidance, Navigation and Control (GNC) during Rendez-vous, Proximity Operations and Docking (RPOD) with reliable visual data. As part of its Rendez-vous solutions, Infinite Orbits is developing optical equipment to support these operations in Geostationary Equatorial Orbits (GEO). A space-grade camera with embedded computing capabilities to capture and process high resolution pictures is currently in the qualification stages. The system features the necessary interfaces to communicate with other satellite subsystems, which makes it suitable for receiving information from other sensors and controlling actuators. The camera can be customized in its optical elements to be optimized for far-range or close-to-terminal-range in-orbit services operations. My internship at Infinite Orbits has focused on supporting the system design and the qualification process of this camera, named OrbSight2 (OS2). In parallel, I led the optomechanical and thermal design of a flashlight intended to provide reliable illumination during docking operations with a client satellite. Altogether, this experience has provided enriching opportunities to follow the full design of orbital optical payloads with a specific focus and valuable insights into in-orbit services. I have leveraged and developed my skillset in optomechanical and thermal design on the one hand, and in the standardized methodology for the development, validation, and testing of complex space equipments on the other. I notably benefit the expertise of a team composed of mechanical, thermal, optical, electronics, and software engineers
Comparison of manual and automated coverage path planning for mechanized forest regeneration
No full textIn Finland and Scandinavia, even-aged forest management predominates, often including mechanical site preparation and manual planting. Growing labor shortages and increased demand for sustainability have driven interest in mechanized and autonomous planting systems. This study evaluates two automated Coverage Path Planners (CPP), Pathfinder and TerraTrail, developed to optimize planting routes for mechanized forest regeneration. Their performance is compared to the routes of the manually operated mechanized planting machine, PlantMax. Three operational sites in Sweden, representing varied terrain and hydrological conditions are evaluated. The evaluation focuses on coverage, Euclidean and Dubins path lengths. Both CPPs incorporate Digital Elevation Models (DEM), Depth-to-Water (DTW) maps and vehicle-specific kinematics to generate planting routes. Two scenarios are evaluated: one where the CPPs neglect the DTW map, and another where the CPPs are constrained to avoid DTW values below 0.3 m. Results show that automated CPPs achieve 15–19% higher coverage than manual planning on average. Pathfinder showed similar normalized path lengths in an unconstrained scenario as the manual operator, but 14% shorter in the constrained environment. TerraTrail shows 7% longer normalized path lengths in an unconstrained scenario, while the constrained scenario shows similar path lengths as the manual operator. These findings emphasize the potential of deploying automated CPP systems to enhance precision, sustainability, and labor efficiency of silvicultural operations. The CPPs support both autonomous deployment and decision support tool for operators. Further refinement, including combining both CPPs to leverage the best functions of each, along with reversible path planning, could enhance their value in forestry practices.Full text license: CC BY-SA 4.0;</p
Predictive Analysis of Future Cellular Coverage Network Operator, and Vegetation Maps Through Machine Learning : An FCNN Model to predict 6 targets on 6 years of Grid Based Dataset
No full textCellular networks are currently one of the main ways that people connect with each other. In today's digital world, these networks are essential for enabling data exchange and communication. Ensuring the effective growth and optimization of these networks is crucial given the rising demand for high-speed data services. Effective infrastructure planning, improving service quality, and filling in any current network coverage gaps all depend on anticipating future coverage needs (Xue & Liang, 2025). Numerous factors, including the land's topography, the network infrastructure already in place, and even the presence of vegetation in the area, affect how much coverage cellular networks offer (Ruben Borralho, 2021). This study uses geographic, network operator, and vegetation mapping data along with machine learning algorithms to overcome the difficulties in cellular coverage prediction. The goal is to help network operators make well-informed decisions that will lead to better planning strategies and improved service delivery by utilizing these technologies. To accomplish this, a large dataset of ~19 GB of Sweden from 2013 to 2019 was used. Before deciding to train Fully Connected Neural Networks (FCNN) for 6 distinct coverage targets, such as reaching 10 Mbps at 16 dB, a number of machine learning models were tested, including Random Forest, Convolutional Neural Networks and Thresholding techniques. To improve prediction accuracy, preprocessing methods like quantile normalization, Yeo-Johnson scaling, win-sorization, etc were used on the data before the FCNN models were trained. The study's findings showed that the FCNN model achieved an impressive accuracy rate of 93.92% for the initial coverage target. Additionally, there was a significant correlation between the actual coverage seen in 2017 and the coverage maps that were predicted. This accomplished result emphasizes how important it is to take into account elements such as vegetation density and land characteristics when figuring out how cellular signals spread and how to best optimize network coverage for improved performance. This study offers important insights that can help network operators make well-informed decisions about resource allocation and network expansion by developing a robust predictive model that can scale. An important development in network planning is the combination of map data and machine learning methods, which provides a more data-driven strategy for improving the caliber and coverage of cellular networks
Evaluation of Crashworthiness and Fracture Toughness at High Deformation Rates for Advanced High Strength Steel sheets
No full textGradually more stringent environmental and safety regulations in the transport sector have made third generation Advanced High Strength Steel (3rd-gen AHSS) grades and new generations of press hardening steels (PHS) cost-effective and natural substitutes in the automotive industry. Increasing the strength of steel allows for potentially downgauging the sheet thickness while maintaining or improving structural performance, and thus reducing the weight of the vehicle. 3rd-gen AHSS and PHS grades have been continuously adapted by the automotive industry for body-in-white parts and energy-absorbing safety components. However, the limited ductility of these higher-strength materials can make them more prone to cracking, which in turn has a negative impact on the folding behaviour of safety structures in a crash. For further introduction of new high-strength steel grades in the design and production of safety parts, proper calibrated material models are needed, and their crash behaviour must be investigated and quantified. Plane stress fracture toughness measured with the Essential Work of Fracture (EWF) method has recently emerged as a viable material parameter to rationalise edge crack resistance and crashworthiness. EWF offers a small-scale laboratory methodology capable of characterising important fracture characteristics of modern automotive steel grades. Hence, EWF together with well-instrumented crash tests in the laboratory are powerful tools for estimating the crashworthiness and quantifying energy absorption. However, much of the published fracture toughness data is based on quasi-static conditions, which do not reflect the conditions in a crash typically involving high deformation rates. To characterise the material for crash scenarios and validate simulation models, further investigation is necessary at higher deformation rates. In this PhD thesis, the crashworthiness and fracture characteristics of 3rd-gen AHSS and PHS grades at higher deformation rates were investigated. The crashworthiness and energy absorbing capacity were evaluated by studying dynamically loaded axially crushed crash boxes both experimentally using full-field deformation measurements and numerically by finite element analysis using a commercially available damage model. Stereo high-speed imaging allowed for more efficient evaluation of crash performance with fewer components and aided in model validation. Furthermore, the rate dependence of fracture toughness and the underlying mechanisms were explored, revealing that crack propagation resistance after crack initiation significantly influences fracture toughness at higher loading rates. It was also experimentally shown that there is significant adiabatic heating in the fracture process zone using the EWF methodology at higher loading rates, which can influence the value of fracture toughness
Sustainability challenges in the value chains of battery minerals
No full textTo address the climate change issue, a global transition from the current “brown economy” to a “green economy” is imperative. The realization of this worldwide ambition necessitates large-scale electrification which leads to a growing demand for lithium-ion batteries as energy storage technologies. The developing market of batteries requires significant mineral and metal inputs. However, there are diverse challenges, rooted in different stages of a battery value chain, in meeting the escalating demand for battery metals and minerals. These challenges consist of, for example, various uncertainties; the need for building institutional and knowledge capacity; environmental, social, economic, and governance issues; and geopolitical tensions. These challenges can hinder the uninterrupted supply of battery raw materials and propagate through the whole battery value chain affecting all involved stakeholders. This fact reinforces the global concern over how to enhance the resilience of supply for each battery raw material, while upholding sustainable development goals. The aim of this research work is to contribute to the development of the knowledge domains that are considered prerequisites to the supply sustainability of battery minerals and a real green transition. Here, considering the entire value chain of a lithium-ion battery, the approaches adopted by regulatory agencies, governments, mining companies, and vehicle and battery manufacturers are evaluated. The objectives of this evaluation are to discern and categorize gaps and opportunities in the implemented strategies, to identify key criteria for resilient and sustainable battery mineral value chain, and to analyze the roles of various actors in global mineral supply chains during the transition to a green economy. These assessments are accompanied by the analyses of the factors threatening the primary supply of the selected battery raw materials including lithium, cobalt, graphite, and nickel. The purpose of these in-depth analyses is to comprehend the interplay between mine production of each individual battery raw material and a multitude of risks and uncertainties, which is a valuable asset to supply chain management. Moreover, another objective of this work is to predict the future mining production of the selected battery raw materials in twenty years ahead. To achieve this, three time series forecasting techniques namely Seasonal Autoregressive Integrated Moving Average, Holt’s linear trend methods, and Holt-Winters techniques are Utilized. Predicting the future regional and global mining production of battery raw materials provides decision makers with a knowledge platform about the dynamics of supply security in the future. This platform can also help the stakeholders engaged in the different stages of a battery value chain to adopt sound strategies to minimize the probability of demand and supply imbalance in the future
Engineered Fluorine-Free Electrolytes for Next-Generation Batteries
No full textDue to the successful commercialization of lithium-ion batteries (LIBs), there is a growing interest in developing new battery materials with improved properties. The uneven distribution of natural resources, the low abundance of battery materials in the Earth’s crust, and the growing geopolitical concerns should also be considered and addressed. In this context, alternative battery technologies, such as sodium-ion batteries (SIBs) and lithium metal batteries (LMBs), are getting attention by researchers, due to the low cost of readily available sodium resources and the very high capacity of a lithium metal anode, etc. Conventional electrolytes of any battery technology are today heavily based on fluorinated salts and volatile organic solvents, posing serious safety issues all the way from synthesis to application and recycling. Additionally, the increasing concerns of per- and polyfluoroalkyl substances (PFAS) highlight the urgent demand to explore performant fluorine-free electrolytes, ideally also non-flammable. In this study, novel fluorine-free ionic materials and electrolytes have been designed and their physical and electrochemical properties thoroughly investigated. In the first part (Paper I), fluorine-free “solvent-in-salt” (SIS) sodium electrolytes based on sodium bis(2-(2-ethoxyethoxy)ethyl) phosphate (NaDEEP) salt and tris(2-(2-ethoxyethoxy)ethyl) phosphate (TEOP) solvent are presented. The addition of TEOP increased the electrochemical oxidation stability of the SIS electrolytes and an unusual ionic conductivity behavior is observed – the ionic conductivities of the electrolytes increase with increasing salt concentration. In the second paper (Paper II), a series of new orthoborate-based ionic materials, containing the bis(glycolato)borate (BGB) anion and phosphonium/ammonium cations are prepared and compared with the popular bis(oxalato)borate (BOB) salts. Some of these ionic materials are room temperature ionic liquids (RTILs), while others are organic ionic plastic crystals (OIPCs). The tetrabutylphosphonium bis(glycolato)borate ([P4444][BGB]) OIPC displays much higher decomposition temperature than the structural analogous [P4444][BOB] IL, and multinuclear solid-state NMR spectroscopy indicated weaker cation-anion interactions in phosphonium-based salts than the ammonium-based ones. Given the excellent moisture and thermal stabilities brought by the BGB anion, a family of BGB-based alkali and alkaline metal salts were synthesized and characterized (Paper III). The LiBGB-based electrolytes using dimethyl sulfoxide (DMSO), triethyl phosphate (TEP) and trimethyl phosphate (TMP) have excellent moisture stability, optimal ionic conductivity, better aluminum (Al) passivation and long-term Li plating-stripping performance. Sequentially, the next study (Paper IV) is focused on investigating the effect of additives on the performance of these electrolytes, such as vinylene carbonate (VC), fluoroethylene carbonate (FEC), etc. Finally, in the fifth paper (Paper V), two- and three-component eutectic electrolytes based on pyrrolidinium saccharinate [Pyrr][Sac], lithium saccharinate Li[Sac] and/or [P4444][BGB] salts were created. The physicochemical properties of these salts as well as the Li compatibility and cell performance are thoroughly investigated. Overall, these studies identified several new fluorine-free salts and electrolytes with beneficial properties that can potentially be used in next-generation batteries