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This paper provides a comprehensive overview of approaches to the determination of isocontours and isosurfaces from given data sets. Different algorithms are reported in the literature for this purpose, which originate from various application areas, such as computer graphics or medical imaging procedures. In all these applications, the challenge is to extract surfaces with a specific isovalue from a given characteristic, so called isosurfaces. These different application areas have given rise to solution approaches that all solve the problem of isocontouring in their own way. Based on the literature, the following four dominant methods can be identified: the marching cubes algorithms, the tessellation-based algorithms, the surface nets algorithms and the ray tracing algorithms. With regard to their application, it can be seen that the methods are mainly used in the fields of medical imaging, computer graphics and the visualization of simulation results. In our work, we provide a broad and compact overview of the common methods that are currently used in terms of isocontouring with respect to certain criteria and their individual limitations. In this context, we discuss the individual methods and identify possible future research directions in the field of isocontouring.
Batteries typically consist of multiple individual cells connected in series. Here we demonstrate single-cell state of charge (SOC) and state of health (SOH) diagnosis in a 24 V class lithium-ion battery. To this goal, we introduce and apply a novel, highly efficient algorithm based on a voltage-controlled model (VCM). The battery, consisting of eight single cells, is cycled over a duration of five months under a simple cycling protocol between 20 % and 100 % SOC. The cell-to-cell standard deviations obtained with the novel algorithm were 1.25 SOC-% and 1.07 SOH-% at beginning of cycling. A cell-averaged capacity loss of 9.9 % after five months cycling was observed. While the accuracy of single-cell SOC estimation was limited (probably owed to the flat voltage characteristics of the lithium iron phosphate, LFP, chemistry investigated here), single-cell SOH estimation showed a high accuracy (2.09 SOH-% mean absolute error compared to laboratory reference tests). Because the algorithm does not require observers, filters, or neural networks, it is computationally very efficient (three seconds analysis time for the complete data set consisting of eight cells with approx. 780.000 measurement points per cell).
Optimization of energetic refurbishment roadmaps for multi-family buildings utilizing heat pumps
(2023)
A novel methodology for calculating optimized refurbishment roadmaps is developed in this paper. The aim of the roadmaps is to determine when and how should which component of the building envelope and heat generation system be refurbished to achieve the lowest net present value. The integrated optimization approach couples a particle swarm optimization algorithm with a dynamic building simulation of the building envelope and the heat supply system. Due to a free selection of implementation times and refurbishment depth, the optimization method achieves the lowest net present value and high CO2 reduction and is therefore an important contribution to achieve climate neutrality in the building stock.
The method is exemplarily applied to a multi-family house built in 1970. In comparison to a standard refurbishment roadmap, cost savings of 6–16 % and CO2 savings of 6–59 % are possible. The sensitivity of the refurbishment roadmap measures is analyzed on the basis of a parametric analysis. Robust optimization results are obtained with a mean refurbishment level of approx. 50 kWh/m2/a of the building envelope. The preferred heat generation system is a bivalent brine-heat pump system with a share of 70 % of the heat load being covered by the electric heat pump.
Am 1. Juli 2022 trafen sich im Rahmen des Abschlusskolloquiums des Projekts ACA-Modes rund 60 Teilnehmende aus Forschung, Lehre und Industrie zu einer internationalen Konferenz an der Hochschule Offenburg. Hier wurden die Projektergebnisse rund um die erfolgreiche Implementierung modellprädiktiver Regelstrategien vorgestellt, aktuelle Fragestellungen diskutiert und Entwicklungspfade hin zu einem netzdienlichen Betrieb von Energieverbundsystemen skizziert.
Heat pumps play a central role in decarbonizing the heat supply of buildings. However, in this article, implementing heat pumps in existing buildings, a significant challenge is still presented due to high temperature requirements. In this article, a systematic analysis of the effects of heat source temperatures, maximum heat pump condenser temperatures, and system temperatures on the seasonal performance of heat pump (HP) systems is presented. The quantitative performance analysis encompasses over 50 heat pumps installed in residential buildings, revealing correlations between the building characteristics, observed temperatures, and heat pump type. The performance of an HP system retrofitted to a 30-dwelling multifamily building is presented in more detail. The bivalent HP system combines air and ground as heat sources and achieves a seasonal performance factor of 3.25 with a share of the gas boiler of 27% in its first year of operation. In these findings, the technical feasibility of retrofitting heat pumps is demonstrated in existing buildings and insights are provided into overcoming the challenges associated with high temperature requirements.
Lithium-ion batteries exhibit slow voltage dynamics on the minute time scale that are usually associated with transport processes. We present a novel modelling approach toward these dynamics by combining physical and data-driven models into a Grey-box model. We use neural networks, in particular neural ordinary differential equations. The physical structure of the Grey-box model is borrowed from the Fickian diffusion law, where the transport domain is discretized using finite volumes. Within this physical structure, unknown parameters (diffusion coefficient, diffusion length, discretization) and dependencies (state of charge, lithium concentration) are replaced by neural networks and learnable parameters. We perform model-to-model comparisons, using as training data (a) a Fickian diffusion process, (b) a Warburg element, and (c) a resistor-capacitor circuit. Voltage dynamics during constant-current operation and pulse tests as well as electrochemical impedance spectra are simulated. The slow dynamics of all three physical models in the order of ten to 30 min are well captured by the Grey-box model, demonstrating the flexibility of the present approach.
This article presents the development, parameterization, and experimental validation of a pseudo-three-dimensional (P3D) multiphysics aging model of a 500 mAh high-energy lithium-ion pouch cell with graphite negative electrode and lithium nickel manganese cobalt oxide (NMC) positive electrode. This model includes electrochemical reactions for solid electrolyte interphase (SEI) formation at the graphite negative electrode, lithium plating, and SEI formation on plated lithium. The thermodynamics of the aging reactions are modeled depending on temperature and ion concentration and the reactions kinetics are described with an Arrhenius-type rate law. Good agreement of model predictions with galvanostatic charge/discharge measurements and electrochemical impedance spectroscopy is observed over a wide range of operating conditions. The model allows to quantify capacity loss due to cycling near beginning-of-life as function of operating conditions and the visualization of aging colormaps as function of both temperature and C-rate (0.05 to 2 C charge and discharge, −20 °C to 60 °C). The model predictions are also qualitatively verified through voltage relaxation, cell expansion and cell cycling measurements. Based on this full model, six different aging indicators for determination of the limits of fast charging are derived from post-processing simulations of a reduced, pseudo-two-dimensional isothermal model without aging mechanisms. The most successful aging indicator, compared to results from the full model, is based on combined lithium plating and SEI kinetics calculated from battery states available in the reduced model. This methodology is applicable to standard pseudo-two-dimensional models available today both commercially and as open source.
Wirtschaftlichkeitsbetrachtung eines smarten Energiekonzepts für ein Bestandsquartier in Karlsruhe
(2023)
Die Transformation der Energieversorgung in Bestandsgebäuden ist für die Erreichung der Klimaziele im Gebäudesektor entscheidend. In einem modellhaften Quartiersprojekt in Karlsruhe-Durlach wird ein ‚smartes Energiekonzept‘, bestehend aus Wärmepumpen, Blockheizkraftwerk und PV-Anlagen mit lokalem Strom- und Wärmenetz umgesetzt und messtechnisch begleitet. Ziel ist dabei eine CO2-effiziente und wirtschaftliche Bereitstellung von Wärme und Strom.
In dem Artikel wird eine Wirtschaftlichkeitsbetrachtung für das Wärme- und Stromcontracting auf Basis der realen Investitionskosten sowie der gemessenen und berechneten Energieflüsse durchgeführt. Die Wärmegestehungskosten hängen neben den Investitionskosten von den energiewirtschaftlichen Rahmenbedingungen ab. Mit ansteigender CO2-Steuer werden mittelfristig Wärmegestehungskosten erreicht, die unter denen konventioneller Energiesysteme liegen. Dadurch bietet das integrierte Energiekonzept ein breites Anwendungspotenzial für städtische Bestandsquartiere außerhalb von Fernwärme-Gebieten.
Predictive control has great potential in the home energy management domain. However, such controls need reliable predictions of the system dynamics as well as energy consumption and generation, and the actual implementation in the real system is associated with many challenges. This paper presents the implementation of predictive controls for a heat pump with thermal storage in a real single-family house with a photovoltaic rooftop system. The predictive controls make use of a novel cloud camera-based short-term solar energy prediction and an intraday prediction system that includes additional data sources. In addition, machine learning methods were used to model the dynamics of the heating system and predict loads using extensive measured data. The results of the real and simulated operation will be presented.
Der prozentuale Energieaufwand für die Warmwasserbereitung ist umso höher, je geringer der Bedarf an Raumwärme – erreicht durch besser gedämmte Gebäudehüllen – ist. Gleichzeitig kann dieser Aufwand für Warmwasser aufgrund der normativ geforderten Systemtemperaturen von 60/55 °C bei zentraler Warmwasserbereitung über Wärmepumpen nur vergleichsweise energieaufwendig abgedeckt werden. Eine Studie des Fraunhofer ISE zeigt, wie groß dieser Temperatur-Effekt im Vergleich unterschiedlicher Trinkwasser-Erwärmungssysteme ist.