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data-driven modeling of Li-Ion Battery Based on the Manufacturer Specifications and Laboratory Measurements

IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS 2025年第2期61卷 3485-3493页

作者： Di Fonso, Roberta Cecati, Carlo Teodorescu, Remus Stroe, Daniel-Ioan Bharadwaj, Pallavi Aalborg Univ DK-9220 Aalborg Denmark Univ LAquila I-67100 Laquila Italy Indian Inst Technol Gandhinagar 382355 India

Accurate modeling of Lithium-ion battery is essential in the development and testing of state estimation and lifetime prediction algorithms. The desired features of the model include flexibility, fast development, accuracy and reliability. There are many different ways to model a battery, depending on the level of abstraction desired, the data available and the target application environment. This paper shows how to extract equivalent circuit model parameters from manufacturer datasheets and laboratory measurement to build robust battery simulation models. A step-by-step methodology for data preparation is presented for both datasheet and measurement-based methods. The benefits and the disadvantages of both approaches are also discussed. A simple equivalent circuit model is firstly derived from manufacturer specification and its robustness is enhanced by collecting more extensive experimental data in the laboratory. Furthermore, an advanced model to better capture the battery dynamics is developed. The aging effects are added to this battery model, to reflect the internal parameters variation according to the health condition of the battery. To measure the accuracy of the developed models, the relative error is computed. An initial relative error of 2.8% of the model build with manufacturer specifications is reduced to 1.0% using laboratory measurements and finally to less than 0.4% by incorporating aging effects.

关键词： Batteries Integrated circuit modeling data models Load modeling Computational modeling State of charge Aging Voltage Battery charge measurement Accuracy Lithium-ion battery equivalent circuit model datasheet specifications data driven modeling state of health

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data driven modeling of the Reactive Oxygen Species Stimulated by Photon Energy in Light Therapies

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IEEE ACCESS 2020年 8卷 18196-18206页

作者： Dong, Jianfei Wang, Tianfeng Chinese Acad Sci Suzhou Inst Biomed Engn & Technol Suzhou 215123 Peoples R China Delft Univ Technol Dept Microelect NL-2628 Delft Netherlands

Light therapies can be used to treat fungal infections. A general mechanism is attributed to the generation of cytotoxic reactive oxygen species (ROS) due to light stimulation. The effectiveness of these therapies has been widely studied in the literature via conducting biological experiments, where fungi are exposed to light with various wavelengths and power. However, despite the large amount of work reporting the experimental results, few efforts have been given to build a mathematical model that describes the amount of generated ROS as a function of the photon energy and power of the stimulating light. The lack of such a model still hinders the optimization of the light doses. In this work, we propose a novel modeling method based on experimental data, so as to establish a mathematical relationship between the ROS concentration and the stimulating photon energy and light fluence (energy density). The anti-fungal experiments were performed on Candida albicans (C: albicans) using four LED light sources with different wavelengths ranging from 385nm to 450nm. Both the viability of the fungi and the ROS concentration therein were measured during the experiments. High fitting accuracy has been achieved by the model, which therefore demonstrates the effectiveness of the proposed modeling techniques.

关键词： Biomedical engineering data driven modeling light-emitting diodes light therapy parameter estimation

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data driven modeling and Verification for Silicon Carbide JFET with Thermal Effects

Data Driven Modeling and Verification for Silicon Carbide JF...

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29th Annual IEEE Applied Power Electronics Conference and Exposition (APEC)

作者： Ruan Li-gang Zhu Ping Wang Li Nanjing Univ Aeronaut & Astronaut Dept Elect Engn Coll Automat Jiangsu Key Lab New Energy Generat & Power Conver Nanjing 210016 Jiangsu Peoples R China

ISBN: (纸本)9781479923250

A data driven model with thermal effects is proposed for normally-off SiC JFET in this paper. The novel feature of the model is that it saves the trouble of obtaining the physical parameters. Besides, the effect of temperature on SiC JFET performance is taken into account. It is important because the physical parameters on materials and dimensions of a particular SiC JFET device are difficult to be obtained for device users. The model is implemented based directly upon the characteristics of device datasheet and data from experimental tests. Simulation and experiments are carried out for the research of steady state characteristics, switching characteristics and short-circuit characteristics, which verify the accuracy of the model. Method of data driven modeling can be applied to the other power semiconductor switches.

关键词： junction gate field effect transistors power field effect transistors semiconductor device models silicon compounds wide band gap semiconductors SiC data driven modeling data driven verification physical parameters power semiconductor switches short-circuit characteristics silicon carbide JFET steady state characteristics switching characteristics thermal effects data models Integrated circuit modeling JFETs Junctions Silicon carbide Switches Temperature

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Frictional loss in bend pipes: numerical simulation and data driven modeling

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SADHANA-ACADEMY PROCEEDINGS IN ENGINEERING SCIENCES 2023年第3期48卷 1-16页

作者： Vasa, Adarsh Chaudhury, Kaustav Natl Inst Technol Rourkela Dept Mech Engn Rourkela 769008 India

Here, we present a quasi-universal correlation for the friction factor in 90-degree and 180-degree bend pipes using the ensemble of metamodels (EOM). Using computational simulations, we obtain frictional losses at curvature ratios from 0.01 to 0.2 and Reynolds numbers up to 0.1 million. With the help of this data, we develop the EOM based correlation. The extensive window of parameters considered in the present study encompasses a vast design and operating conditions of 90-degree and 180-degree bend pipes. The large parametric space considered here essentially represents a variety of hydrodynamic scenarios. We use a sufficient yet nominal set of parameters representative of the variation and perform numerical simulations over the sampled set. Using the simulation data, we then train and validate the EOM to develop the required correlation. The k-fold cross-validation technique is employed to the EOM to optimize the model's predictive capabilities over the changing flow regimes. We believe that this modeling strategy, along with the database, may act as a base framework towards the further development of data augmentation techniques in pipes of various configurations, in general. Using this EOM based correlation, we further generate a large database for the friction factor in the two smooth bend pipe configurations at various curvature ratio and different Reynolds number. This approach can be viewed as an alternative to the Moody chart, however, for pipe bends.

关键词： data driven modeling ensemble of metamodels bend pipes friction factor

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Verification of Sinusoidal Steady State system identification of a Phantom Omni haptic device using data driven modeling 6

Verification of Sinusoidal Steady State system identificatio...

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6th International Conference on Automation, Robotics and Applications (ICARA)

作者： Milne, B. J. E. Beelen, H. P. G. J. Merks, R. W. H. Weiland, S. Chen, X. Q. Hann, C. E. Parker, R. J.

ISBN: (纸本)9781479964666

Haptic feedback has two important sources of dynamics: the machine being controlled and the haptic device itself. This paper concentrates on the means of identifying the dynamics of a Phantom Omni haptic feedback device. Two models are compared: a dynamic model with parameters using results from sinusoidal steady state analysis and a data driven model that uses pseudo-random binary sequences (PRBS) for identification. The overall form of the frequency and phase response is well-defined for the dynamics model but for the data driven model a spectral estimate from PRBS response data is used to determine the model order. The results in this paper show that a dynamic equation based minimal model produces accuracy as good as the data driven model. While the data driven model has more fitting accuracy the increase in accuracy is not useful for modelling the physical response as the differences occur at high frequencies where the Phantom arm is not sensitive anyway. The dynamic model is particularly useful as it gives a physical basis for the observed output and the sinusoidal steady state behaviour is useful for exposing non-linearities. Future work includes development and verification an arm inertia model that allows system parameters to be identified from response data at arbitrary arm angles.

关键词： System identification data driven modeling Minimal modeling

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data driven modeling of pseudopalisade pattern formation

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JOURNAL OF MATHEMATICAL BIOLOGY 2023年第1期87卷 4-4页

作者： Athni Hiremath, Sandesh Surulescu, Christina TU Kaiserslautern Mech & Proc Engn Gottlieb Daimler Str 42 D-67663 Kaiserslautern Rhineland Palat Germany TU Kaiserslautern Felix Klein Zentrum Math Paul Ehrlich Str 31 D-67663 Kaiserslautern Rhineland Palat Germany

Pseudopalisading is an interesting phenomenon where cancer cells arrange themselves to form a dense garland-like pattern. Unlike the palisade structure, a similar type of pattern first observed in schwannomas by pathologist J.J. Verocay (Wippold et al. in AJNR Am J Neuroradiol 27(10):2037-2041, 2006), pseudopalisades are less organized and associated with a necrotic region at their core. These structures are mainly found in glioblastoma (GBM), a grade IV brain tumor, and provide a way to assess the aggressiveness of the tumor. Identification of the exact bio-mechanism responsible for the formation of pseudopalisades is a difficult task, mainly because pseudopalisades seem to be a consequence of complex nonlinear dynamics within the tumor. In this paper we propose a data-driven methodology to gain insight into the formation of different types of pseudopalisade structures. To this end, we start from a state of the art macroscopic model for the dynamics of GBM, that is coupled with the dynamics of extracellular pH, and formulate a terminal value optimal control problem. Thus, given a specific, observed pseudopalisade pattern, we determine the evolution of parameters (bio-mechanisms) that are responsible for its emergence. Random histological images exhibiting pseudopalisade-like structures are chosen to serve as target pattern. Having identified the optimal model parameters that generate the specified target pattern, we then formulate two different types of pattern counteracting ansatzes in order to determine possible ways to impair or obstruct the process of pseudopalisade formation. This provides the basis for designing active or live control of malignant GBM. Furthermore, we also provide a simple, yet insightful, mechanism to synthesize new pseudopalisade patterns by linearly combining the optimal model parameters responsible for generating different known target patterns. This particularly provides a hint that complex pseudopalisade patterns could be synthesized by

关键词： Optimal control Pseudopalisades data driven modeling Pattern formation

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data-driven modeling of band-pass filter for sub-5G applications

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MICROWAVE AND OPTICAL TECHNOLOGY LETTERS 2023年第8期65卷 2210-2216页

作者： Belen, Aysu Belen, Mehmet A. Iskenderun Tech Univ Iskenderun Vocat Sch Higher Educ Hybrid & Elect Vehicle Technol Hatay Turkiye Iskenderun Tech Univ Dept Elect Elect Engn Hatay Turkiye

Radiofrequency noise is one of the challenging problems in the design of high-performance wireless communication systems, for which microstrip band-pass filters are one of the most commonly used design solutions for this challenge. However, for having high-performance designs. usage of a 3D Electromagnetic simulation tool is a must in which the computation efficiency of the whole design optimization process might not be acceptable or even feasible. An efficient solution is utilization of AI-based algorithms to create data-driven surrogate models of the handled problem. In this paper, for achieving design optimization of an edge-coupled band-pass filter AI-based algorithms have been used to create a data-driven surrogate model. To achieve this, by using a 3D full-wave simulator a data set for the aimed bandpass filter is generated. Then a series of state-of-the-art regression algorithms, Support Vector Regression Machine, MultiLayer Perceptron, Ensemble Learning, Gaussian Process Regression, and Convolutional Neural Network have been used to create a data-driven surrogate model for the aimed filter design. In the third step, the obtained data-driven surrogate model is used to assist an optimization process directed by the Bayesian optimization technique to optimally determine geometrical design parameters of the desired band-pass filter for sub-5G applications at frequency of 3.4 GHz. The obtained results of the surrogate model are compared with experimental results and found to be in high agreement level. Furthermore, the performance of the optimally designed filter is compared with the counterpart designs in literature. Thus, based on the obtained results, it can be said that the proposed surrogate-assisted optimization process is not only an efficient method in terms of computational costs but also is an efficient method to obtain high-performance microwave filter designs.

关键词： 5G artificial intelligence data driven modeling microwave filter surrogate modeling

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A novel data-characteristic-driven modeling methodology for nuclear energy consumption forecasting

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APPLIED ENERGY 2014年 128卷 1-14页

作者： Tang, Ling Yu, Lean He, Kaijian Beijing Univ Chem Technol Sch Econ & Management Beijing 100029 Peoples R China

Due to the unique features of nuclear energy market, this paper tries to propose a novel data-characteristic-driven modeling methodology based on the principle of "data-characteristic-driven modeling", aiming at formulating appropriate forecasting model closely in terms of sample data's own data characteristics. In the novel data-characteristic-driven modeling methodology, two steps are mainly involved, i.e., data analysis and forecasting modeling. First, the sample data of nuclear energy consumption are thoroughly investigated in order to capture the main inner rules and hidden patterns driving the data dynamics, in terms of data characteristics. Second, the corresponding forecasting model is accordingly formulated and designed based on these data characteristics. For illustration and verification purposes, the proposed methodology is implemented to predict the nuclear energy consumption of USA and China. The empirical results demonstrate that the novel methodology with the principle of "data-characteristic-driven modeling" strikingly improves prediction performance, since the models elaborately built based on data characteristics statistically outperform all other benchmark models without consideration of data characteristics. This further confirms that the proposed methodology is a very promising tool in both analyzing and forecasting nuclear energy consumption. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： data characteristics Nuclear energy consumption Time series forecasting data driven modeling

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data-driven modeling of optimal intensity measure of soil-nailed wall structures

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STRUCTURAL ENGINEERING AND MECHANICS 2023年第1期86卷 85-92页

作者： Bayat, Massoumeh Bayat, Mahdi Bayut, Mahmoud Islamic Azad Univ Dept Comp Engn Mashhad Branch Mashhad Iran Islamic Azad Univ Dept Civil Engn Roudehen Branch Roudehen Iran Texas State Univ Ingram Sch Engn Smart Sustainable & Resilient Infrastruct SSRI Lab San Marcos TX 78666 USA

This article examines the seismic vulnerability of soil nail wall structures. Detailed information regarding finite element modeling has been provided. The fragility function evaluates the relationship between ground motion intensities and the probability of surpassing a specific level of damage. The use of incremental dynamic analysis (IDA) has been applied to the soil nail wall against low to severe ground motions. In the nonlinear dynamic analysis of the soil nail wall, a set of twenty seismic ground motions with varying PGA ranges are used. The numerical results demonstrate that the soil-nailed wall reaction is extremely sensitive to earthquake ground vibrations under different intensity measures (IM). In addition, the analytical fragility curve is provided for various intensity values.

关键词： data driven modeling finite element modeling nonlinear analysis nonlinear time history analysis optimal intensity measure

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data-driven surrogate modeling of dielectric reflectarray antenna using 3D printing technology for X band applications

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MICROWAVE AND OPTICAL TECHNOLOGY LETTERS 2024年第5期66卷 e34153-e34153页

作者： Bereket, Mehmet Belen, Mehmet A. Iskenderun Tech Univ Dept Elect & Elect Engn Hatay Turkiye

Reflectarray (RA) antennas are gaining recognition as a high-performance alternative for antenna design. They combine the functionalities of standard parabolic reflectors and phased array antennas without a feed network. However, the design of RA models requires a model that exhibits exceptional performance while meeting contradictory requirements such as size and operation band. An effective and computationally efficient approach is to employ a rapid and precise unit element model for the design of reflective antennas. Artificial intelligence (AI) regression techniques have been developed to predict electromagnetic behavior efficiently and accurately in microwave device design. A cross-shaped 3D printable RA was examined as a case study, and a design-ready surrogate model was developed with a mean absolute error of approximately 5 degrees using only 800 data samples. The method can be considered a feasible substitute for current surrogate-assisted RA design techniques due to its computational efficacy and dependability.

关键词： 3D printer artificial intelligence data driven modeling deep learning optimization surrogate modeling

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