1,720,960 research outputs found
FPGA Implementation of Support Vector Regression for Battery SoC Estimation
No full textThe Battery Management System (BMS) optimizes the reliability and operational efficiency of lithium-ion batteries by monitoring their physical conditions and functional state, including the evaluation of the State of Charge (SoC). Accurate SoC estimation is challenging due to the nonlinear characteristics of Li-ion batteries and the influence of external factors. The aim of this research is to study the feasibility of hardware implementation of a Support Vector Regression (SVR) algorithm on a FPGA platform for SoC estimation based on voltage, current, and temperature measurements. Special attention was paid to the utilization of resources fostering the possibility of parallel monitoring, and thus enabling the simultaneous management of multiple battery cells. Two architectural configurations, 32-bit and 64-bit of data representations were explored and compared to identify an optimal trade-off between the area occupancy and error committed in the inference phase. The root-mean-square error (RMSE) committed with the developed hardware was compared with that committed on a PC running MATLAB software with a double precision data format. The 64-bit version resulted in a difference in the RMSE of 0.0016% utilizing 18.33% of the available DSPs, allowing for only 5 replicated on-board instances. On the other hand, the 32-bit version required only 6.25% of the available DSPs, thus enabling 16 parallel instances, with an RMSE difference of 0.10%
A flexible machine learning based framework for state of charge evaluation
No full textBatteries State-of-Charge (SoC) must be accurately monitored for safe battery operations, and to extend battery life. Machine Learning (ML) algorithms allow to perform the SoC estimation on a data-based approach, avoiding the need for a physical model for each different battery. In this work, a new ML-based framework for the SoC evaluation is proposed, exploiting constant current discharges for model training, rather than the commonly exploited standard drive cycle profiles. This allows avoiding the conversion processes from the drive cycles vehicle acceleration set-point into a current profile, which lead to vehicle-dependent data and the need for a conversion tool. Currents, voltages and temperatures related to different current discharge rates were measured for a Panasonic 18650 Lithium-Ion battery cell. These data were used to train and optimize a Support Vector Regression (SVR) model in the MATLAB environment. Subsequently, different data were combined together to emulate a real vehicle discharge process and were used for evaluating the model. A Root Mean Square Error (RMSE) of 0.564% was obtained, proving that the SVR model trained with constant current discharges data has been capable to estimate the SoC of the tested drive cycles operations
Machine learning and impedance spectroscopy for battery state of charge evaluation
No full textThe Lithium-ion batteries market is rapidly growing. Estimating the batteries State of Charge (SOC) and their State of Health (SOH) is a challenging but crucial task, which Artificial Intelligence (AI) techniques can manage when trained with appropriate data. Physical measurements such as current, voltage and temperature during battery discharge are conventionally used as inputs of AI algorithms to provide an estimation of SOC. In this work, the effect of the battery impedance measurement on the training of a Support Vector Machine (SVM) has been studied. Electrochemical Impedance Spectroscopy (EIS) has been employed for in-situ impedance measurements at different frequencies to consider the effects of each perturbation. The obtained complex impedance values along with the measured current, voltage and temperature data, have been evaluated as features of a training set for an SVM in its regression form (SVR). To allow for simultaneous data acquisition, a module composed of 16 battery cells connected in series has undergone a total of 15 discharge cycles. Several SVR models have been trained with a variety of feature combinations, to evaluate the effect of different impedance information on the resulting model. When using the same battery cell for training and testing, the addition of magnitude and phase of the 100 Hz impedance to the input vector decreased the Root Mean Square Error (RMSE) of the estimated SOC from 1.34% to 1.09%. On the other hand, the same SVR model showed an RMSE of 1.23% when using different (but nominally identical) cells for testing
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
Aging modelling of Li-ion battery systems based on accelerated tests
No full textThis paper presents a new equivalent model based on the Arrhenius law in MATLAB/Simulink environment that can be exploited to evaluate the aging of Li-ion batteries. In this work, we show the simulation results obtained using this model. In addition, an automated test bench for multiple Li-ion battery cell characterization and accelerated aging will be presented together with early measurement results for validating the model
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe 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
Full text link“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
Full text linkWe 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
An Improved Method Based on Support Vector Regression With Application Independent Training for State of Charge Estimation
Full text linkNowadays, lithium-ion (Li-ion) is among the most used chemistry for batteries and shows an increasing market growth rate; however, to reduce failure or safety risks, the battery state-of-charge (SoC) must be accurately monitored and predicted by a suitable battery management system (BMS). Artificial intelligence (AI) techniques have been extensively applied to this field with good results. Typically, AIs are trained on dynamic profile data, emulating battery charging and discharging cycles related to the application under test. In this article, a novel approach is presented: application-independent constant current profiles are used to train a support vector regression (SVR) algorithm. To enhance the estimation accuracy, the output of the obtained SVR model was postprocessed. Finally, an error correction algorithm was applied to further reduce the estimation error. The system is validated over test cycles, representing different application scenarios for the battery cell operations. For the development of the proposed approach, a total of 105 constant current discharge profiles for the training and 20 realistic test cycles for the validation have been considered, including standard automotive cycles and a generic battery-powered power tool. The performance in the SoC estimation resulted in a root-mean-square error (RMSE) of 0.94% and a mean absolute error (MAE) of 0.75% over all the test cycles. Error metrics are comparable to those obtained for SoC estimation AI algorithms based on traditional approaches using application-dependent battery profiles for the training phase
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