496 research outputs found
Sliding mode observer with adaptive switching gain for estimating state of charge and internal temperature of a commercial Li-ion pouch cell
No full textAccurate estimation of the state of charge (SOC) and internal temperature is the essence of the battery management systems for lithium-ion batteries (LIBs). In this research, an improved sliding mode observer (SMO) is presented and evaluated for the estimation of SOC and internal temperature of LIBs by adapting the switching gain. The observer is meticulously designed, parametrized, and validated by combining modeling and experimentation on a commercial 64 Ah LIB pouch cell. The battery behavior is emulated by a coupled equivalent circuit model (CECM) composed of a dual-polarization and a novel thermal model. The proposed observer is showcased to estimate the SOC with an average error of <2 % even in the presence of a significant model mismatch. The results provide deep insight into the development process of the efficient and robust SMO observers for estimating the internal states of LIBs.This work was supported by funding from the European Union’s Horizon 2020 research and innovation program for the Current Direct project under grant agreement No.963603
Morphological peculiarities of the lithium electrode from the perspective of the Marcus-Hush-Chidsey model
No full textThis work was supported by the European Union?s Horizon 2020 Research and Innovation Program for the Solidify Project (875557)
sj-avi-1-pih-10.1177_09544119221122645 – Supplemental material for Thermometry using entropy imaging of ultrasound radio frequency signal time series
No full textSupplemental material, sj-avi-1-pih-10.1177_09544119221122645 for Thermometry using entropy imaging of ultrasound radio frequency signal time series by Ashkan Behnia, Hamid Behnam, Elyas Shaswary and Jahan Tavakkoli in Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine</p
Decentralized Approximate Bayesian Inference for Distributed Sensor Network
No full textBayesian models provide a framework for probabilistic modelling of complex datasets. Many such models are computationally demanding, especially in the presence of large datasets. In sensor network applications, statistical (Bayesian) parameter estimation usually relies on decentralized algorithms, in which both data and computation are distributed across the nodes of the network. In this paper we propose a framework for decentralized Bayesian learning using Bregman Alternating Direction Method of Multipliers (B-ADMM).We demonstrate the utility of our framework, with Mean Field Variational Bayes (MFVB) as the primitive for distributed affine structure from motion (SfM).Peer reviewe
Improvements to seismicity forecasting based on a Bayesian spatio-temporal ETAS model
No full textThe epidemic-type aftershock sequence (ETAS) model provides an effective tool for predicting the spatio-temporal evolution of aftershock clustering in short-term. Based on this model, a fully probabilistic procedure was previously proposed by the first two authors for providing spatio-temporal predictions of aftershock occurrence in a prescribed forecasting time interval. This procedure exploited the versatility of the Bayesian inference to adaptively update the forecasts based on the incoming information provided by the ongoing seismic sequence. In this work, this Bayesian procedure is improved: (1) the likelihood function for the sequence has been modified to properly consider the piecewise stationary integration of the seismicity rate; (2) the spatial integral of seismicity rate over the whole aftershock zone is calculated analytically; (3) background seismicity is explicitly considered within the forecasting procedure; (4) an adaptive Markov Chain Monte Carlo simulation procedure is adopted; (5) leveraging the stochastic sequences generated by the procedure in the forecasting interval, the N-test and the S-test are adopted to verify the forecasts. This framework is demonstrated and verified through retrospective early forecasting of seismicity associated with the 2017–2019 Kermanshah seismic sequence activities in western Iran in two distinct phases following the main events with Mw7.3 and Mw6.3, respectively
Interrelationships among Adjustable Design Parameters and Rate Performance of Porous Electrodes in Lithium‐Ion Batteries
No full textThe authors are grateful for the financial support to FWO-Vlaanderen (SBO XL-Lion, S005017N)
Theoretical and Experimental Insights into Dendrite Growth in Lithium-Metal Electrode
No full textA stable lithium-metal electrode can enable the shift from the Li-ion batteries to the next generation chemistries such as Li-S and Li-O2 with significant gains in the energy density and sustainability. This transition, however, is hindered by the dendrite formation, high chemical reactivity, and volume changes of the Li electrode. Although recent advancements in computational and experimental research have deepened our understanding of these issues, the primary obstacles to the commercialization of the lithium-metal batteries (LMBs) still persist. To address these challenges, a synergistic approach that combines computational and experimental strategies shows great promise. In this regard, this paper reviews the current experimental and theoretical understanding of the lithium-metal electrodes in view of the initiation and growth mechanisms of the lithium dendrites and interface instability. Leveraging the strengths of both approaches can offer a holistic insight into the LMB performance and guide the development of innovative designs for electrolytes and electrodes that can enhance the stability and performance of the LMBs
Monitoring Trends of CO, NO<sub>2</sub>, SO<sub>2</sub>, and O<sub>3</sub> Pollutants Using Time-Series Sentinel-5 Images Based on Google Earth Engine
No full textAir pollution (AP) is a significant risk factor for public health, and its impact is becoming increasingly concerning in developing countries where it is causing a growing number of health issues. It is therefore essential to map and monitor AP sources in order to facilitate local action against them. This study aims at assessing the suitability of Sentinel-5 AP products based on Google Earth Engine (GEE) to monitor air pollutants, including CO, NO2, SO2, and O3 in Arak city, Iran from 2018 to 2019. Our process involved feeding satellite images to a cloud-free GEE platform that identified pollutant-affected areas monthly, seasonally, and annually. By coding in the JavaScript language in the GEE, four pollution parameters of Sentinel-5 satellite images were obtained. Following that, images with clouds were filtered by defining cloud filters, and average maps were extracted by defining average filters for both years. The employed model, which solely used Sentinel-5 AP products, was tested and assessed using ground data collected from the Environmental Organization of Central Province. Our findings revealed that annual CO, NO2, SO2, and O3 were estimated with RMSE of 0.13, 2.58, 4.62, and 2.36, respectively, for the year 2018. The annual CO, NO2, SO2, and O3 for the year 2019 were also calculated with RMSE of 0.17, 2.41, 4.31, and 4.6, respectively. The results demonstrated that seasonal AP was estimated with RMSE of 0.09, 5.39, 0.70, and 7.81 for CO, NO2, SO2, and O3, respectively, for the year 2018. Seasonal AP was also estimated with RMSE of 0.12, 4.99, 1.33, and 1.27 for CO, NO2, SO2, and O3, respectively, for the year 2019. The results of this study revealed that Sentinel-5 data combined with automated-based approaches, such as GEE, can perform better than traditional approaches (e.g., pollution measuring stations) for AP mapping and monitoring since they are capable of providing spatially distributed data that is sufficiently accurate
An integrated approach of remote sensing and geospatial analysis for modeling and predicting the impacts of climate change on food security
No full textAbstract The agriculture sector provides the majority of food supplies, ensures food security, and promotes sustainable development. Due to recent climate changes as well as trends in human population growth and environmental degradation, the need for timely agricultural information continues to rise. This study analyzes and predicts the impacts of climate change on food security (FS). For 2002–2021, Landsat, MODIS satellite images and predisposing variables (land surface temperature (LST), evapotranspiration, precipitation, sunny days, cloud ratio, soil salinity, soil moisture, groundwater quality, soil types, digital elevation model, slope, and aspect) were used. First, we used a deep learning convolutional neural network (DL-CNN) based on the Google Earth Engine (GEE) to detect agricultural land (AL). A remote sensing-based approach combined with the analytical network process (ANP) model was used to identify frost-affected areas. We then analyzed the relationship between climatic, geospatial, and topographical variables and AL and frost-affected areas. We found negative correlations of − 0.80, − 0.58, − 0.43, and − 0.45 between AL and LST, evapotranspiration, cloud ratio, and soil salinity, respectively. There is a positive correlation between AL and precipitation, sunny days, soil moisture, and groundwater quality of 0.39, 0.25, 0.21, and 0.77, respectively. The correlation between frost-affected areas and LST, evapotranspiration, cloud ratio, elevation, slope, and aspect are 0.55, 0.40, 0.52, 0.35, 0.45, and 0.39. Frost-affected areas have negative correlations with precipitation, sunny day, and soil moisture of − 0.68, − 0.23, and − 0.38, respectively. Our findings show that the increase in LST, evapotranspiration, cloud ratio, and soil salinity is associated with the decrease in AL. Additionally, AL decreases with a decreasing in precipitation, sunny days, soil moisture, and groundwater quality. It was also found that as LST, evapotranspiration, cloud ratio, elevation, slope, and aspect increase, frost-affected areas increase as well. Furthermore, frost-affected areas increase when precipitation, sunny days, and soil moisture decrease. Finally, we predicted the FS threat for 2030, 2040, 2050, and 2060 using the CA–Markov method. According to the results, the AL will decrease by 0.36% from 2030 to 2060. Between 2030 and 2060, however, the area with very high frost-affected will increase by about 10.64%. In sum, this study accentuates the critical impacts of climate change on the FS in the region. Our findings and proposed methods could be helpful for researchers to model and quantify the climate change impacts on the FS in different regions and periods
An Incremental Capacity Analysis‐based State‐of‐health Estimation Model for Lithium‐ion Batteries in High‐power Applications
No full text& nbsp;This work was supported by funding from the European Union's Horizon 2020 research and innovation program for the Current Direct project under grant agreement No. 963603
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