Battery state of health estimation with improved generalization using parallel layer extreme learning machine

The online estimation of battery state of health (SOH) is crucial to ensure the reliability of the energy supply in electric and hybrid vehicles. An approach for enhancing the generalization of SOH estimation using a parallel layer extreme learning machine (PL-ELM) algorithm is analyzed in this paper. The deterministic and stable PL-ELM model is designed to overcome the drift problem that is associated with some conventional machine learning algorithms; hence, extending the application of a single SOH estimation model over a large set of batteries of the same type. The PL-ELM model was trained with selected features that characterize the SOH. These features are acquired as the discrete variation of indicator variables including voltage, state of charge (SOC), and energy releasable by the battery. The model training was performed with an experimental battery dataset collected at room temperature under a constant current load condition at discharge phases. Model validation was performed with a dataset of other batteries of the same type that were aged under a constant load condition. An optimum performance with low error variance was obtained from the model result. The root mean square error (RMSE) of the validated model varies from 0.064% to 0.473%, and the mean absolute error (MAE) error from 0.034% to 0.355% for the battery sets tested. On the basis of performance, the model was compared with a deterministic extreme learning machine (ELM) and an incremental capacity analysis (ICA)-based scheme from the literature. The algorithm was tested on a Texas F28379D microcontroller unit (MCU) board with an average execution speed of 93 µs in real time, and 0.9305% CPU occupation. These results suggest that the model is suitable for online applications

Sensors and energy harvesters based on piezoelectric thick films

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Piezoelectric Resonant Sensors with Contactless Interrogation for Mass-Sensitive and Acoustic-Load Detection

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State of Health Estimation of Lithium‐Ion Batteries in Electric Vehicles under Dynamic Load Conditions

Among numerous functions performed by the battery management system (BMS), online estimation of the state of health (SOH) is an essential and challenging task to be accomplished periodically. In electric vehicle (EV) applications, accurate SOH estimation minimizes failure risk and improves reliability by predicting battery health conditions. The challenge of accurate estimation of SOH is based on the uncertain dynamic operating condition of the EVs and the complex nonlinear electrochemical characteristics exhibited by the lithium‐ion battery. This paper presents an artificial neural network (ANN) classifier experimentally validated for the SOH estimation of lithium‐ion batteries. The ANN‐based classifier model is trained experimentally at room temperature under dynamic variable load conditions. Based on SOH characterization, the training is done using features such as the relative values of voltage, state of charge (SOC), state of energy (SOE) across a buffer, and the instantaneous states of SOC and SOE. At implementation, due to the slow dynamics of SOH, the algorithm is triggered on a large‐scale periodicity to extract these features into buffers. The features are then applied as input to the trained model for SOH estimation. The classifier is validated experimentally under dynamic varying load, constant load, and step load conditions. The model accuracies for validation data are 96.2%, 96.6%, and 93.8% for the respective load conditions. It is further demonstrated that the model can be applied on multiple cell types of similar specifications with an accuracy of about 96.7%. The performance of the model analyzed with the confusion matrices is consistent with the requirements of the automotive industry. The classifier was tested on a Texas F28379D microcontroller unit (MCU) board. The result shows that an average real‐time execution speed of 8.34 μs is possible with a negligible memory occupation

Model and design of a double frequency piezoelectric resonator

A novel design of a multifrequency mechanical resonator with piezoelectric materials for energy harvesting is presented. The electromechanical response is described by a finite element model, which predicts the output voltage and the generated power

Impact of different silkworm dietary supplements on its silk performance.

Made available in DSpace on 2018-09-01T00:42:22Z (GMT). No. of bitstreams: 1 Impactofdifferentsilkwormdietarysupplementsonitssilkperformance..pdf: 1296466 bytes, checksum: 3cb1afc529fd9a52913bf4a899ecbe09 (MD5) Previous issue date: 2015-02-0

Nanoindentation study of the interfacial zone between cellulose fiber and cement matrix in extruded composites

[EN] The present study shows the application of the nanoindentation technique to evaluate the properties of the cellulose fiber-cement matrix interfacial zone in composites prepared with an auger extruder. The degree of strength of the bond between fiber and matrix is recognized as important variable that influences macro-mechanical properties, such as modulus of rupture and toughness of cement based composites. The nanoindentation measurements showed the highest hardness and elastic modulus in the part inner of the cellulosic fiber after hydration process due to precipitation and re-precipitation of cement hydration products. These results indicate that mineralization of the cellulosic fibers can affect the stress distribution and interfacial bond strength in the cement based composite.The authors acknowledge by financial support provided by Fundacao de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP, process no 2013/03823-8), Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior (CAPES, process no 3886/2014) and Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPq, process no 152069/2016), in Brazil. Special thanks for Fibria and Infibra for providing raw materials the development of this work.Teixeira, R.; Tonoli, G.; Santos, S.; Rayón, E.; Amigó, V.; Savastano, HJ.; Rocco Lahr, F. (2018). Nanoindentation study of the interfacial zone between cellulose fiber and cement matrix in extruded composites. Cement and Concrete Composites. 85:1-8. https://doi.org/10.1016/j.cemconcomp.2017.09.018S188

Open mirror symmetry for Pfaffian Calabi-Yau 3-folds

We investigate the open mirror symmetry of certain non-complete intersection Calabi- Yau 3-folds, so called pfaffian Calabi-Yau. We perform the prediction of the number of disk invariants of several examples by using the direct integration method proposed recently and the open mirror symmetry. We treat several pfaffian Calabi-Yau 3-folds in $\mathbb{P}^6$ and branes with two discrete vacua. Some models have the two special points in its moduli space, around both of which we can consider different A-model mirror partners. We compute disc invariants for both cases. This study is the first application of the open mirror symmetry to the compact non-complete intersections in toric variety.Comment: 64 pages; v2: typos corrected, minor changes, references added; v3: published version, minor corrections and improvement

Proteomic profiling reveals the transglutaminase-2 externalization pathway in kidneys after unilateral ureteric obstruction

Increased export of transglutaminase-2 (TG2) by tubular epithelial cells (TECs) into the surrounding interstitium modifies the extracellular homeostatic balance, leading to fibrotic membrane expansion. Although silencing of extracellular TG2 ameliorates progressive kidney scarring in animal models of CKD, the pathway through which TG2 is secreted from TECs and contributes to disease progression has not been elucidated. In this study, we developed a global proteomic approach to identify binding partners of TG2 responsible for TG2 externalization in kidneys subjected to unilateral ureteric obstruction (UUO) using TG2 knockout kidneys as negative controls. We report a robust and unbiased analysis of the membrane interactome of TG2 in fibrotic kidneys relative to the entire proteome after UUO, detected by SWATH mass spectrometry. The data have been deposited to the ProteomeXchange with identifier PXD008173. Clusters of exosomal proteins in the TG2 interactome supported the hypothesis that TG2 is secreted by extracellular membrane vesicles during fibrosis progression. In established TEC lines, we found TG2 in vesicles of both endosomal (exosomes) and plasma membrane origin (microvesicles/ectosomes), and TGF-β1 stimulated TG2 secretion. Knockout of syndecan-4 (SDC4) greatly impaired TG2 exosomal secretion. TG2 coprecipitated with SDC4 from exosome lysate but not ectosome lysate. Ex vivo, EGFP-tagged TG2 accumulated in globular elements (blebs) protruding/retracting from the plasma membrane of primary cortical TECs, and SDC4 knockout impaired bleb formation, affecting TG2 release. Through this combined in vivo and in vitro approach, we have dissected the pathway through which TG2 is secreted from TECs in CKD