1,720,958 research outputs found
HDL Code Generation from SIMULINK Environment for Li-Ion Cells State of Charge and Parameter Estimation
No full textMonitoring State of Charge battery cells is necessary for all the most common batteries to avoid damages and to extend lifetime. State of Charge must be estimated since it can not be directly measured on the cell. Among all developed techniques, Equivalent Circuit Models is one of the most interesting and it is based on modeling the behavior of the electric components of the cell. However, a real-time parameter estimation is necessary, due to the change of them during the battery life. FPGA is first choice for these applications, for its flexibility and hardware reconfigurability. Traditionally, FPGA is configured with a synthetized Hardware Description Language, HDL, but this process can be time consuming depending on the model complexity. In this paper, a Simulink model-based design for a Li-Ion cell parameters identification is presented. This approach together to HDL Coder Simulink tool, assures higher code portability and short time to market
An Optimized Long Short Term Memory and Gaussian Process Regression Based Framework for State of Charge Estimation
No full textA battery management system (BMS) is a crucial component in numerous small-scale to large-scale industrial applications. An accurately estimated State-Of-Charge (SOC) plays a vital role towards efficient management of a battery system. In this work, a combined machine learning framework, consisting of an optimized long short-term memory (LSTM) neural network and a non-parametric Gaussian process regression (GPR) technique is deployed for the SOC estimation. The composite model is trained using the constant current discharge profiles to obtain more generic and scalable results. The numeric input features (voltage, current and temperature) collected from Panasonic 18650 Lithium-Ion battery cell were used. Extensive training and optimization were performed using GPR followed by a surrogate optimization-based LSTM (GPR-SO-LSTM) via parallel pooling in MATLAB environment. Furthermore, an error correction (EC) algorithm was exploited for increased estimation accuracy. The trained model was tested on carefully curated testing data comprising of different discharge current ranges. Moreover, the accuracy of the model was further challenged by testing it with a battery-powered drill machine. Error metrics such as the average root mean square error (RMSE) of the proposed framework came out to be 0.382% which revealed significant performance improvement in comparison with individual GPR and LSTM models with an RMSE of 2.38% and 7.05% respectively
A Flexible Multi-Channel Platform for Versatile E-Nose and E-Tongue Operations
No full textThis paper introduces a flexible multi-channel platform designed for integrating multiple sensors, among which electronic noses (e-nose) and electronic tongues (e-tongue), targeting food quality and safety assessments. The proposed device is designed aiming for maximum portability, allowing on-site analyses, and maximum modularity, to meet user requirements. The customizable configurations allow the user to adjust the number of channels, current ranges, and voltage settings and to perform different analyses simultaneously. A variable number of sensing modules with different features can simultaneously be interfaced with the same control board, equipped with a microcontroller unit and a wireless module to both process data for on-board analyses and send it to the cloud. The modular design of the system allows for interchangeable sensing modules, offering high flexibility in sensory analysis compared to the existing portable solutions. Two custom dual-channel module boards have been designed with different operating current ranges, minimizing power consumption for extended portability, requiring only 3.16mA per channel during operations of the high-current module and 2.11mA for the low-current module. Device characterization demonstrates high linearity, with an R-squared higher than 0.999 over the entire voltage range. The channel-to-channel gain mismatch is limited to 0.26%. Spectral analysis shows 20 μVrms of equivalent noise, leading to an equivalent minimum sensor resolution of 16 noise-free bits
Tecniche avanzate per la valutazione dello stato di carica per sistemi Integrati di gestione delle batterie
No full textLithium-ion batteries are becoming increasingly popular due to their excellent characteristics. However, to reduce safety risks and avoid failures, the State of Charge (SoC) must be accurately monitored by the Battery Management System (BMS). Unfortunately, the SoC indicator cannot be measured but has to be properly estimated by suitable algorithms. Several techniques are available in the literature, showing advantages and disadvantages depending on the application field and the required accuracy. In this dissertation, some advanced techniques, including hybrid approaches and Machine Learning, have been investigated. The aim was to implement the algorithms on an embedded system and evaluate their effectiveness in terms of computational resources and SoC estimation accuracy in a real environment
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
FPGA Implementation of an Ant Colony Optimization Based SVM Algorithm for State of Charge Estimation in Li-Ion Batteries
Get PDFMonitoring the State of Charge (SoC) in battery cells is necessary to avoid damage and to extend battery life. Support Vector Machine (SVM) algorithms and Machine Learning techniques in general can provide real-time SoC estimation without the need to design a cell model. In this work, an SVM was trained by applying an Ant Colony Optimization method. The obtained trained model was 10-fold cross-validated and then designed in Hardware Description Language to be run on FPGA devices, enabling the design of low-cost and compact hardware. Thanks to the choice of a linear SVM kernel, the implemented architecture resulted in low resource usage (about 1.4% of Xilinx Artix7 XC7A100TFPGAG324C FPGA), allowing multiple instances of the SVM SoC estimator model to monitor multiple battery cells or modules, if needed. The ability of the model to maintain its good performance was further verified when applied to a dataset acquired from different driving cycles to the cycle used in the training phase, achieving a Root Mean Square Error of about 1.4%
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