684 research outputs found

    LIPIcs, Volume 251, ITCS 2023, Complete Volume

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    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    Evaluation and optimisation of traction system for hybrid railway vehicles

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    Over the past decade, energy and environmental sustainability in urban rail transport have become increasingly important. Hybrid transportation systems present a multifaceted challenge, encompassing aspects such as hydrogen production, refuelling station infrastructure, propulsion system topology, power source sizing, and control. The evaluation and optimisation of these aspects are critical for the adaptation and commercialisation of hybrid railway vehicles. While there has been significant progress in the development of hybrid railway vehicles, further improvements in propulsion system design are necessary. This thesis explores strategies to achieve this ambitious goal by substituting diesel trains with hybrid trains. However, limited research has assessed the operational performance of replacing diesel trains with hybrid trains on the same tracks. This thesis develops various optimisation techniques for evaluating and refining the hybrid traction system to address this gap. In this research's first phase, the author developed a novel Hybrid Train Simulator designed to analyse driving performance and energy flow among multiple power sources, such as internal combustion engines, electrification, fuel cells, and batteries. The simulator incorporates a novel Automatic Smart Switching Control technique, which scales power among multiple power sources based on the route gradient for hybrid trains. This smart switching approach enhances battery and fuel cell life and reduces maintenance costs by employing it as needed, thereby eliminating the forced charging and discharging of excessively high currents. Simulation results demonstrate a 6% reduction in energy consumption for hybrid trains equipped with smart switching compared to those without it. In the second phase of this research, the author presents a novel technique to solve the optimisation problem of hybrid railway vehicle traction systems by utilising evolutionary and numerical optimisation techniques. The optimisation method employs a nonlinear programming solver, interpreting the problem via a non-convex function combined with an efficient "Mayfly algorithm." The developed hybrid optimisation algorithm minimises traction energy while using limited power to prevent unnecessary load on power sources, ensuring their prolonged life. The algorithm takes into account linear and non-linear variables, such as velocity, acceleration, traction forces, distance, time, power, and energy, to address the hybrid railway vehicle optimisation problem, focusing on the energy-time trade-off. The optimised trajectories exhibit an average reduction of 16.85% in total energy consumption, illustrating the algorithm's effectiveness across diverse routes and conditions, with an average increase in journey times of only 0.40% and a 15.18% reduction in traction power. The algorithm achieves a well-balanced energy-time trade-off, prioritising energy efficiency without significantly impacting journey duration, a critical aspect of sustainable transportation systems. In the third phase of this thesis, the author introduced artificial neural network models to solve the optimisation problem for hybrid railway vehicles. Based on time and power-based architecture, two ANN models are presented, capable of predicting optimal hybrid train trajectories. These models tackle the challenge of analysing large datasets of hybrid railway vehicles. Both models demonstrate the potential for efficiently predicting hybrid train target parameters. The results indicate that both ANN models effectively predict a hybrid train's critical parameters and trajectory, with mean errors ranging from 0.19% to 0.21%. However, the cascade-forward neural network topology in the time-based architecture outperforms the feed-forward neural network topology in terms of mean squared error and maximum error in the power-based architecture. Specifically, the cascade-forward neural network topology within the time-based structure exhibits a slightly lower MSE and maximum error than its power-based counterpart. Moreover, the study reveals the average percentage difference between the benchmark and FFNN/CNFN trajectories, highlighting that the time-based architecture exhibits lower differences (0.18% and 0.85%) compared to the power-based architecture (0.46% and 0.92%)

    Development of Physics-Based Models of Lithium-ion Battery Energy Storage for Power System Techno-Economic Studies

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    The pathway to achieving a sustainable, low-carbon power system includes the widespread integration of energy storage to tackle intermittency of renewable energy sources and provide stability to the grid through various grid services. Among the wide range of stationary energy storage technologies available, the lithium-ion battery dominates the growth in installations throughout the world. Although lithium-ion battery energy storage systems are complex grid assets with nonlinear characteristics and lifespans that depend on operating conditions, the majority of economic assessments are conducted using a simple energy reservoir model that does not consider the physical processes occurring inside the lithium-ion battery storage. This thesis focuses on the development of physics-based models for lithium-ion battery energy storage in power system techno-economic studies. The aim of this work is to assist developers and investors in making better-informed decisions. In this work, modelling approaches used to represent lithium-ion battery energy storage in power system operation and planning studies are reviewed. The role of advanced models in enhancing the accuracy of economic evaluations and producing feasible schedules for battery storage providing transmission-level services is discussed. More importantly, this work proposes three physics-based mixed-integer models for battery energy storage for use in power system operation research studies. The first model is based on the single particle model and replicates the nonlinear operational characteristics of the battery. This model can be used for short-term operation studies. The second proposed model combines the widely-used energy reservoir model with the physical description of solid electrolyte interphase formation as a degradation mechanism. This model has been tested for long-term studies in both energy and power grid applications. Finally, the third proposed model is a data-driven model that accurately reproduces the degradation processes and nonlinear performance of the lithium-ion cell. The model facilitates long-term assessment of battery energy storage and effectively tracks both capacity and power fade over time. The results obtained from all the models are validated using the digital twin, which is based on the single particle model

    Demand Response in Smart Grids

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    The Special Issue “Demand Response in Smart Grids” includes 11 papers on a variety of topics. The success of this Special Issue demonstrates the relevance of demand response programs and events in the operation of power and energy systems at both the distribution level and at the wide power system level. This reprint addresses the design, implementation, and operation of demand response programs, with focus on methods and techniques to achieve an optimized operation as well as on the electricity consumer

    Path-based splitting methods for SDEs and machine learning for battery lifetime prognostics

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    In the first half of this Thesis, we present the numerical analysis of splitting methods for stochastic differential equations (SDEs) using a novel path-based approach. The application of splitting methods to SDEs can be viewed as replacing the driving Brownian-time path with a piecewise linear path, producing a ‘controlled-differential-equation’ (CDE). By Taylor expansion of the SDE and resulting CDE, we show that the global strong and weak errors of splitting schemes can be obtained by comparison of the iterated integrals in each. Matching all integrals up to order p+1 in expectation will produce a weak order p+0.5 scheme, and in addition matching the integrals up to order p+0.5 strongly will produce a strong order p scheme. In addition, we present new splitting methods utilising the ‘space-time’ L´evy area of Brownian motion which obtain global strong Oph1.5q and Oph2q weak errors for a class of SDEs satisfying a commutativity condition. We then present several numerical examples including Multilevel Monte Carlo. In the second half of this Thesis, we present a series of papers focusing on lifetime prognostics for lithium-ion batteries. Lithium-ion batteries are fuelling the advancing renewable-energy based world. At the core of transformational developments in battery design, modelling and management is data. We start with a comprehensive review of publicly available datasets. This is followed by a study which explores the evolution of internal resistance (IR) in cells, introducing the original concept of ‘elbows’ for IR. The IR of cells increases as a cell degrades and this often happens in a non-linear fashion: where early degradation is linear until an inflection point (the elbow) is reached followed by increased rapid degradation. As a follow up to the exploration of IR, we present a model able to predict the full IR and capacity evolution of a cell from one charge/discharge cycle. At the time of publication, this represented a significant reduction (100x) in the number of cycles required for prediction. The published paper was the first to show that such results were possible. In the final paper, we consider experimental design for battery testing. Where we focus on the important question of how many cells are required to accurately capture statistical variation

    Application of deep learning methods in materials microscopy for the quality assessment of lithium-ion batteries and sintered NdFeB magnets

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    Die Qualitätskontrolle konzentriert sich auf die Erkennung von Produktfehlern und die Überwachung von Aktivitäten, um zu überprüfen, ob die Produkte den gewünschten Qualitätsstandard erfüllen. Viele Ansätze für die Qualitätskontrolle verwenden spezialisierte Bildverarbeitungssoftware, die auf manuell entwickelten Merkmalen basiert, die von Fachleuten entwickelt wurden, um Objekte zu erkennen und Bilder zu analysieren. Diese Modelle sind jedoch mühsam, kostspielig in der Entwicklung und schwer zu pflegen, während die erstellte Lösung oft spröde ist und für leicht unterschiedliche Anwendungsfälle erhebliche Anpassungen erfordert. Aus diesen Gründen wird die Qualitätskontrolle in der Industrie immer noch häufig manuell durchgeführt, was zeitaufwändig und fehleranfällig ist. Daher schlagen wir einen allgemeineren datengesteuerten Ansatz vor, der auf den jüngsten Fortschritten in der Computer-Vision-Technologie basiert und Faltungsneuronale Netze verwendet, um repräsentative Merkmale direkt aus den Daten zu lernen. Während herkömmliche Methoden handgefertigte Merkmale verwenden, um einzelne Objekte zu erkennen, lernen Deep-Learning-Ansätze verallgemeinerbare Merkmale direkt aus den Trainingsproben, um verschiedene Objekte zu erkennen. In dieser Dissertation werden Modelle und Techniken für die automatisierte Erkennung von Defekten in lichtmikroskopischen Bildern von materialografisch präparierten Schnitten entwickelt. Wir entwickeln Modelle zur Defekterkennung, die sich grob in überwachte und unüberwachte Deep-Learning-Techniken einteilen lassen. Insbesondere werden verschiedene überwachte Deep-Learning-Modelle zur Erkennung von Defekten in der Mikrostruktur von Lithium-Ionen-Batterien entwickelt, von binären Klassifizierungsmodellen, die auf einem Sliding-Window-Ansatz mit begrenzten Trainingsdaten basieren, bis hin zu komplexen Defekterkennungs- und Lokalisierungsmodellen, die auf ein- und zweistufigen Detektoren basieren. Unser endgültiges Modell kann mehrere Klassen von Defekten in großen Mikroskopiebildern mit hoher Genauigkeit und nahezu in Echtzeit erkennen und lokalisieren. Das erfolgreiche Trainieren von überwachten Deep-Learning-Modellen erfordert jedoch in der Regel eine ausreichend große Menge an markierten Trainingsbeispielen, die oft nicht ohne weiteres verfügbar sind und deren Beschaffung sehr kostspielig sein kann. Daher schlagen wir zwei Ansätze vor, die auf unbeaufsichtigtem Deep Learning zur Erkennung von Anomalien in der Mikrostruktur von gesinterten NdFeB-Magneten basieren, ohne dass markierte Trainingsdaten benötigt werden. Die Modelle sind in der Lage, Defekte zu erkennen, indem sie aus den Trainingsdaten indikative Merkmale von nur "normalen" Mikrostrukturmustern lernen. Wir zeigen experimentelle Ergebnisse der vorgeschlagenen Fehlererkennungssysteme, indem wir eine Qualitätsbewertung an kommerziellen Proben von Lithium-Ionen-Batterien und gesinterten NdFeB-Magneten durchführen

    State Estimation of Li-ion Batteries Using Machine Learning Algorithms

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    Lithium-ion batteries are mainly utilized in electric vehicles, electric ships, etc. due to their virtue of high energy density, low self-discharge, and low costs. Electric vehicles are prone to accelerated battery degradation due to the high charging/discharging cycles and high peak power demand. Hence, efficient management of the batteries is a dire need in this regard. Battery management systems (BMS) have been developing to control, monitor, and measure the variables of the battery such as voltage, current, and temperature, to estimate the states of charge (SOC) and state of health (SOH) of the battery. This study is divided into three parts; in the first part, the SOC of the battery is estimated utilizing electrochemical impedance spectroscopy (EIS) measurements. The EIS measurements are obtained at different SOC and temperature levels. The highly correlated measurements with the SOC are then extracted to be used as input features. Gaussian process regression (GPR) and linear regression (LR) are employed to estimate the SOC of the battery. In the second part of this study, the EIS measurements at different SOC and temperature levels are employed to estimate the SOH of the battery. In this part, transfer learning (TL) along with deep neural network (DNN) is adopted to estimate the SOH of the battery at another outrange temperature level. The effect of the number of fixed layers is also investigated to compare the performance of various DNN models. The results indicate that the DNN with no fixed layer outclasses the other DNN model with one or more fixed layers. In the third part of this dissertation, the co-estimation of SOC and SOH is conducted as SOC and SOH are intertwined characteristics of the battery, and a change in one affects the other variation. First, the SOH of the battery is estimated using EIS measurements by GPR and DNN. The estimated SOH, along with online-measurable variables of the battery, i.e., voltage and current, are then utilized as input features for long-short term memory (LSTM) and DNN algorithms to estimate the SOC of the battery
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