1,721,001 research outputs found
State of Health Estimation Procedure for Lithium-Ion Batteries Using Partial Discharge Data and Support Vector Regression
Full text linkBattery aging is a complex phenomenon, and precise state of health (SoH) monitoring
is essential for effective battery management. This paper presents a data-driven method for SoH
estimation based on support vector regression (SVR), utilizing features built from both full and partial
discharge capacity curves, as well as battery temperature data. It provides an in-depth discussion
of the novel features constructed from different voltage intervals. Moreover, three combinations of
features were analyzed, demonstrating how their efficacy changes across different voltage ranges.
Successful results were obtained using the full discharge capacity curves, built from the full interval
of 2 to 3.4 V and achieving a mean R2 value of 0.962 for the test set, thus showcasing the adequacy
of the selected SVR strategy. Finally, the features constructed from the full voltage range were
compared with ones built from 10 small voltage ranges. Similar success was observed, evidenced by
a mean R2 value ranging between 0.939 and 0.973 across different voltage ranges. This indicates the
practical applicability of the developed models in real-world scenarios. The tuning and evaluation of
the proposed models were carried outusing a substantial dataset created by Toyota, consisting of
124 lithium iron phosphate batteries
Low-Cost High-Performance Generator for Testing Current Transformers Based on Frequency-Domain Feedback
Full text linkCharacterizing the harmonic measurement performance of current transformers (CTs) requires a proper generator for applying realistic periodic current waveforms, mimicking those found in distribution grids. This paper proposes an approach for implementing a high-performance current generator, based on the usual, low-cost architecture consisting of a power amplifier, a transformer to boost its capability and a reference CT. Frequency-domain error feedback is adopted, with feedback gain set according to a preliminary frequency response measurement. This enables heavily mitigating the nonlinearity introduced by the current boost transformer, which no longer must be heavily overdesigned to reach high accuracy
Compensating the Harmonic Distortion Introduced by Instrument Transformers: An Improved Method Based on Frequency-Domain Polynomials
No full textMonitoring harmonic components has paramount importance in modern distribution grids characterized by a strong penetration of nonlinear, power electronics-based devices. In this respect, the accuracy of harmonic assessment is heavily affected by the performance of instrument transformers, which depends also on their nonlinear behavior. This paper proposes an improved technique that enables an accurate reconstruction of primary harmonics from the secondary side. The method is based on polynomial modeling and compensation of harmonic distortion, which is the strongest nonlinear effect at low-order harmonics. The structure is extremely flexible, so that it can be tailored to reach the best tradeoff between performance and complexity, while avoiding overparametrization
A pruning technique for volterra models: Exploiting knowledge about input spectrum
No full textBehavioral modeling and identification of nonlinear time invariant systems in the frequency domain represents an extremely interesting and up to date topic in widespread application fields. The frequency-domain Volterra-Wiener (or polynomial) approach is one of the most widely employed, since it can be derived as the straightforward extension of the usual frequency response function to the nonlinear case. Its main drawback is that its complexity rapidly grows with the number of input harmonic components and nonlinearity order. The purpose of this work is presenting a method to reduce the number of coefficients defining the Volterra models by exploiting a priori knowledge about the input signal spectral content. Similarly to the spectral linearization approximation which is commonly used in radiofrequency and microwave applications, input components are classified into "large" and "small" according to their expected amplitudes. The output spectrum is computed by considering all the possible interactions between large components according to the Volterra theory. On the contrary, interactions between small components are neglected. The proposed modeling approach has been tested in numerical simulations on a Hammerstein system; results clearly show the advantages with respect to a conventional polynomial model
Battery Remaining Useful Life Prediction Supported by Long Short-Term Memory Neural Network
No full textThe rise of renewable energy and electric vehicles has led to enormous growth and development in the field of lithium-ion batteries. Ensuring long-life and safe operation of these batteries requires accurate estimation of key parameters such as state of charge, state of health (SoH), and remaining useful life (RUL). In this paper, a long short-term memory neural network (LSTM NN) is presented for RUL prediction. Furthermore, the predictors used are discussed in detail, and a comparison between the two models is presented. The network has been trained and tested on a substantial dataset of 124 batteries, aged under various fast charging conditions, and published by the Toyota Research Institute in collaboration with MIT and Stanford. Despite their vastly different cycle lives, the proposed LSTM NN structure has performed very accurate RUL prediction for all tested cells
Definition of Pruned Frequency-Domain Volterra Models Based on Knowledge About the Input Spectrum
Full text linkThe Volterra representation is one of the most widely employed approaches to the behavioral modeling of nonlinear time invariant systems in the frequency domain. Its main drawback is that the input-output relationship is defined by a set of coefficients, whose cardinality rapidly grows with the considered nonlinearity degree and with the number of input harmonics. The purpose of this work is proposing a method that, assuming to know which are the strongest spectral components in the typical input signals, allows writing a subclass of Volterra models whose behaviors are defined by a dramatically lower number of coefficients, with minor impact on accuracy. According to this information, input spectral components are classified into large, small and linear. The output spectrum is computed by considering all the possible interactions between large components, as from the Volterra theory. On the contrary, small components interact only with large components, but not with each other. Linear components are linearly transferred to the output. The effectiveness of the pruning technique is evaluated with both numerical simulations and experiments. Results highlight the advantages and the flexibility enabled by the proposed approach, which become even more evident in the presence of significant noise during identification
A New Method For Identifying Harmonic Distortion Compensation Filters For Voltage Transformers
No full textThe harmonic measurement accuracy obtained with voltage transformers can be greatly improved through proper compensation of their nonlinear behavior. In this respect, the authors of this paper have previously proposed a frequency-domain approach for mitigating the harmonic distortion, namely the strongest nonlinear effect. However, identifying the parameters of the compensation formulas requires injecting a broad set of signals, resembling those found during regular operation. The present paper proposes a new approach that enables dramatically reducing the number of training waveforms, thus the duration of the procedure, which has paramount importance for a large-scale implementation. Numerical simulations performed on a reference VT model highlight that the fast identification method enables the same accuracy as the conventional one, while showing exemplary robustness with respect to the metrological performance of the voltage generator used to apply the training waveforms
A New Method to Represent the Harmonic Measurement Accuracy of Current Transformers
Full text linkMetrological performance of current transformers (CTs) is typically quantified in terms of ratio and phase errors. While they provide a good picture at the fundamental component, they are not as effective in condensing the accuracy of harmonic measurements in the presence of nonlinearity. This article proposes a new approach derived from the Volterra representation of the CT. The complex error has been adopted as the accuracy metric, split into a deterministic contribution (purely correlated with the measurand) and a circularly symmetric random term, whose spread depends on the fundamental magnitude; moreover, the proposed approach boils down to the usual ratio and phase errors if the CT exhibits negligible nonlinearity. The effectiveness of the developed method has been tested on an inductive CT, as a typical current transducer suffering from nonlinearity; nevertheless, results can be deemed as general since the approach has been derived from a behavioral model of the CT, and thus, independent of its operating principle. The impact of both measurement disturbances and uncertainty introduced by the nonideal calibration of the test setup has been also evaluated
Nonlinear Behavioral Modeling of Voltage Transformers in the Frequency Domain: Comparing Different Approaches
No full textInstrument transformers suffer from weak nonlinearities, which may have a significant impact on harmonic measurements. Therefore, their detailed characterization should be based on a nonlinear model instead of a mere measurement of the frequency response function. Starting from this assumption, this article compares different models that can be employed to represent the behavior of voltage transformers (VTs): simplified Volterra models, frequency transfer matrixes (FTMs), and spectral linearization approximation; an inductive VT has been considered as a case study. The results highlight that the FTM approach is significantly affected by the range of variation of the fundamental component, while remarkable accuracy can be reached with the spectral linearization approximation at the expense of complexity. On the other hand, simplified Volterra models allow obtaining an effective tradeoff between the achieved accuracy and the number of coefficients
Investigating and Modeling the Harmonic Measurement Accuracy of Current Transformers
No full textOverall measurement accuracy of current transformers is expressed by their accuracy class. When considering the fundamental component, it corresponds to ratio and phase error limits that are relaxed at small current values. As far as low-power current transformers, fixed ratio and phase error limits are prescribed at harmonic components. This paper shows, through numerical simulations, that this approach cannot represent the performance of current transformers in the presence of nonlinearity. A general method to model the behavior of harmonic ratio and phase errors is presented, regardless of the operating principle of the current transformer. This suggests an alternative approach to specify accuracy requirements at a given harmonic, which should depend on its relative magnitude with respect to the fundamental
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