Remaining Useful Life Prediction of Lithium-ion Batteries using Spatio-temporal Multimodal Attention Networks

Lithium-ion batteries are widely used in various applications, including electric vehicles and renewable energy storage. The prediction of the remaining useful life (RUL) of batteries is crucial for ensuring reliable and efficient operation, as well as reducing maintenance costs. However, determining the life cycle of batteries in real-world scenarios is challenging, and existing methods have limitations in predicting the number of cycles iteratively. In addition, existing works often oversimplify the datasets, neglecting important features of the batteries such as temperature, internal resistance, and material type. To address these limitations, this paper proposes a two-stage remaining useful life prediction scheme for Lithium-ion batteries using a spatio-temporal multimodal attention network (ST-MAN). The proposed model is designed to iteratively predict the number of cycles required for the battery to reach the end of its useful life, based on available data. The proposed ST-MAN is to capture the complex spatio-temporal dependencies in the battery data, including the features that are often neglected in existing works. Experimental results demonstrate that the proposed ST-MAN model outperforms existing CNN and LSTM-based methods, achieving state-of-the-art performance in predicting the remaining useful life of Li-ion batteries. The proposed method has the potential to improve the reliability and efficiency of battery operations and is applicable in various industries, including automotive and renewable energy

Quantifying the Congruence between Air and Land Surface Temperatures for Various Climatic and Elevation Zones of Western Himalaya

The authors would like to acknowledge National Snow and Ice Data Centre, USA and National Oceanic and Atmospheric Administration, USA for providing freely available MODIS satellite products and Global Historical Climatology Network station data, respectively. The authors are also grateful to India Meteorology Department (IMD), India, Bhakhra Beas Management Board (BBMB), India and Hendrik Wulf, University of Zurich, Switzerland for providing the station data. A.B. acknowledges the Swedish Research Council for supporting his research in Himalaya. M.S. acknowledges Director, Birbal Sahni Institute of Palaeosciences and Birbal Sahni Research Associate fellowship.Peer reviewedPublisher PD

Himalayan glaciers experienced significant mass loss during later phases of little ice age

We express our gratitude to Prof. Sunil Bajpai, Director, BSIP for providing official permission to publish (vide BSIP/RDCC/89/2016–2017) and necessary facilities to carry out this work. We also thank the PCCFs Uttarakhand, Himachal Pradesh and Jammu and Kashmir, and DFO Uttarkashi and other forest office staffs of the Indian Himalayan states for their help and providing necessary facilities during tree-ring sampling. We thank Mrs. Meenakshi Joshi (IFS) Uttarakhand for her insights on the topic and constructive suggestions. We thank Prof. Hans W. Linderholm and Prof. Dan J. Smith for sharing the mass balance time-series for Storglaciären (Sweden) and Canadian glaciers, respectively. M.S. acknowledges the financial support by the Department of Science and Technology, New Delhi vide SERB-DST Project No. SR/FTP/ES-127/2014 [Young Scientist Scheme]. P.S.R. extends his sincere acknowledgement to SERB–DST projects SR/DGH/44/2012 and SR/DGH/56/2013 for financial support to carry out this research work.Peer reviewedPublisher PD

Tree rings of Rhododendron arboreum portray signal of monsoon precipitation in the Himalayan region

The Himalayas has a significant impact not just on the Indian subcontinent’s monsoon patterns but also on the global climate. Monsoon failure causing drought has become more common in recent years. As a result, it poses a major threat to ecosystem sustainability. We reported for the first time, a climatic-sensitive tree ring chronology of a broadleaf tree, Rhododendron arboreum, spanning 1732–2017 CE from the Himalayan region. We discovered that the climate during the monsoon season limits the growth of this tree in this region. The correlation analysis between tree ring chronology and climate revealed a significant positive relationship with precipitation (r = 0.63, p < 0.001) and a negative relationship with temperature (r = −0.48, p < 0.01) during the months of June–August (JJA). This strong relationship allowed us to reconstruct monsoon precipitation spanning 1780 to 2017 CE which explained 40% of the variance of the observed climate data for the calibration period. The reconstructed data are validated by the existence of a significant association with the gridded JJA precipitation data of the Climate Research Unit (CRU) of this region. The monsoon rainfall record captured extremely wet years during 1793, 1950, 2011, 2013, and 2017 and extremely dry years during 1812, 1833, 1996, 2002, 2004, and 2005. The extremely dry and wet years well coincided with major catastrophic historical and instrumental droughts and floods in the region. Furthermore, the reconstructed data are also validated by the significant positive correlation (r = 0.36, p < 0.001, n = 163) with the all Indian summer monsoon rainfall series. Such data will be useful to predict the incidence of future droughts, which can help to assess the vulnerability of the forest ecosystem to extreme events

Water-gas shift catalysis over supported gold and platinum nanoparticles

The water-gas shift (WGS) reaction (CO + H2O → CO 2 + H2) is an important industrial chemical process for hydrogen production. Supported Au and Pt catalysts have turnover rates (TORs) comparable to the industrial Cu/ZnO/Al2O3 catalysts but are more robust. Our work focuses on determining the (1) active sites and (2) effect of support for supported Au and Pt nanoparticles. The WGS reaction rate per total mole of Au varies with average Au particle size (d) as d-2.7±0.1 for Au/TiO2, d-3.2±0.4 for Au/ZrO2, d-2.9±0.2 for Au/CeO 2, d-2.6±0.2 for Au/ZnO, and d-2.2±0.2 for Au/Al2O3 catalysts. The variations of reaction rate and apparent reaction orders with particle size were used to show that the active sites are low coordinated metallic corner and perimeter Au atoms. On the other hand, the WGS TORs, normalized by surface Pt atoms, for Pt/Al2O3, Pt/SiO2, Pt/ZrO2, and Pt/TiO2 catalysts are independent of average Pt particle size. Unlike for Au catalysts, the apparent reaction orders for Pt catalysts do not vary with particle size. Thus, all surface Pt atoms exhibit the same rate. The addition of Br at a level of 16% of the surface moles of Au to a 2.3%Au/TiO2 catalyst decreased its WGS reaction rate by six times. The addition of Br did not result in an appreciable change in the average Au particle size, apparent activation energy, or the reaction orders. From operando Fourier transform infrared (FTIR) spectroscopy experiments, the WGS reaction rate is proportional to the normalized peak area of CO adsorbed on metallic Au (IR peak at 2100 cm-1). Corner Au atoms were counted as the active sites by transient isotopic switch experiments. Thus, it was confirmed that metallic corner Au atoms are the dominant active sites for Au/TiO2 catalysts. The WGS reaction rate per total mole of Au and H2O order (in parenthesis) vary as Au/Al2O3 (∼ 0.6) \u3c Au/CeO 2 (∼ 0.3) \u3c Au/ZrO2 (∼ 0.0) \u3c Au/TiO2 (∼ -0.3) at the same Au particle size at 120 °C. Similarly, for Pt catalysts, the WGS TOR and H2O order (in parenthesis) vary as Pt/Al2O3 (0.93) ∼ Pt/SiO2 (0.84) \u3c Pt/TiO2 ∼ Pt/ZrO2 ∼ Pt/CeO2 (0.66-0.72) at 300 °C. The CO, CO2 and H2 orders do not vary with the support. Further, the TORs for Pt/ZrO2 and Pt/TiO 2 catalysts decrease by 125 and 10 times their original TOR, respectively, upon addition of 70 atomic layer deposition (ALD) cycles, ∼ 75 wt.% Pt. This is due to excess Pt coverage that limits the number of support sites, shown from their transmission electron microscopy (TEM) images, available for H2O activation. Density functional theory (DFT) results show that the H2O activation barrier on rutile sites adjacent to Au (0.25 eV) is lower than on corner sites of unsupported Au nanoparticles (1.48 eV) and clean rutile TiO2 (110) surface (0.33 eV). We interpret these data to show that the support plays a direct role in activating H 2O. In conclusion, metallic, low-coordinated corner and perimeter Au atoms and metallic surface Pt atoms were identified as the active sites of supported Au and Pt catalysts for the WGS reaction, respectively. The WGS reaction kinetics and DFT results were used to determine that the effect of support on WGS reaction rates over supported Au and Pt catalysts is caused by its direct participation in activating water molecules

ARCHITECTURE-AWARE HARD-REAL-TIME SCHEDULING ON MULTI-CORE ARCHITECTURES

The increasing dependency of man on machines have led to increase computational load on systems. The increasing computational load can be handled to some extent by scaling up processor frequencies. However, this approach has hit a frequency and power wall and the increasing awareness towards green computing discourages this solution. This leads us to use multi-core architectures. Due to the same reason, real-time systems are also migrating from single-core towards multi-core systems. While multi-core systems provide scalable high computational power, they also expose real-time systems to several challenges. Most of these challenges hamper the key property of real-time systems, i.e., predictability. In this work, we address some challenges imposed by multi-core architectures on real-time systems. We propose and evaluate several scheduling algorithms and demonstrate improved predictability and performance over existing methods. A unifying them in all our algorithms is that we explicitly consider the effects of architectural factors on the scheduling and schedulablity of real-time programs. As a case study, we use Tilera\u27s TilePro64 platform as an example multi-core platform and implement some of our algorithms on this platform. Through this case study, we derive several useful conclusions regarding performance, predictability and practical overheads on a multi-core architecture

Diagnostic et Pronostic de Systèmes Dynamiques Incertains dans un contexte Bond Graph

This thesis develops the approaches for diagnostics and prognostics of uncertain dynamic systems in Bond Graph (BG) modeling framework. Firstly, properties of Interval Arithmetic (IA) and BG in Linear Fractional Transformation, are integrated for representation of parametric and measurement uncertainties on an uncertain BG model. Robust fault detection methodology is developed by utilizing the rules of IA for the generation of adaptive interval valued thresholds over the nominal residuals. The method is validated in real time on an uncertain and highly complex steam generator system.Secondly, a novel hybrid prognostic methodology is developed using BG derived Analytical Redundancy Relationships and Particle Filtering algorithms. Estimations of the current state of health of a system parameter and the associated hidden parameters are achieved in probabilistic terms. Prediction of the Remaining Useful Life (RUL) of the system parameter is also achieved in probabilistic terms. The associated uncertainties arising out of noisy measurements, environmental conditions etc. are effectively managed to produce a reliable prediction of RUL with suitable confidence bounds. The method is validated in real time on an uncertain mechatronic system.Thirdly, the prognostic methodology is validated and implemented on the electrical electro-chemical subsystem of an industrial Proton Exchange Membrane Fuel Cell. A BG of the latter is utilized which is suited for diagnostics and prognostics. The hybrid prognostic methodology is validated, involving real degradation data sets.Cette thèse développe des approches pour le diagnostic et le pronostic de systèmes dynamiques incertains en utilisant la technique de modélisation Bond Graph (BG). Tout d'abord, une représentation par intervalles des incertitudes paramétriques et de mesures est intégrée à un modèle BG-LFT (Linear Fractional Transformation). Une méthode de détection robuste de défaut est développée en utilisant les règles de l'arithmétique d'intervalle pour la génération de seuils robustes et adaptatifs sur les résidus nominaux. La méthode est validée en temps réel sur un système de générateur de vapeur.Deuxièmement, une nouvelle méthodologie de pronostic hybride est développée en utilisant les Relations de Redondance Analytique déduites d'un modèle BG et les Filtres Particulaires. Une estimation de l'état courant du paramètre candidat pour le pronostic est obtenue en termes probabilistes. La prédiction de la durée de vie résiduelle est atteinte en termes probabilistes. Les incertitudes associées aux mesures bruitées, les conditions environnementales, etc. sont gérées efficacement. La méthode est validée en temps réel sur un système mécatronique incertain.Enfin, la méthodologie de pronostic développée est mise en œuvre et validée pour le suivi efficace de la santé d'un sous-système électrochimique d’une pile à combustible à membrane échangeuse de protons (PEMFC) industrielle à l’aide de données de dégradation réelles

Diagnostics and Prognostics of Uncertain Dynamical Systems in a Bond Graph Framework

Cette thèse développe des approches pour le diagnostic et le pronostic de systèmes dynamiques incertains en utilisant la technique de modélisation Bond Graph (BG). Tout d'abord, une représentation par intervalles des incertitudes paramétriques et de mesures est intégrée à un modèle BG-LFT (Linear Fractional Transformation). Une méthode de détection robuste de défaut est développée en utilisant les règles de l'arithmétique d'intervalle pour la génération de seuils robustes et adaptatifs sur les résidus nominaux. La méthode est validée en temps réel sur un système de générateur de vapeur.Deuxièmement, une nouvelle méthodologie de pronostic hybride est développée en utilisant les Relations de Redondance Analytique déduites d'un modèle BG et les Filtres Particulaires. Une estimation de l'état courant du paramètre candidat pour le pronostic est obtenue en termes probabilistes. La prédiction de la durée de vie résiduelle est atteinte en termes probabilistes. Les incertitudes associées aux mesures bruitées, les conditions environnementales, etc. sont gérées efficacement. La méthode est validée en temps réel sur un système mécatronique incertain.Enfin, la méthodologie de pronostic développée est mise en œuvre et validée pour le suivi efficace de la santé d'un sous-système électrochimique d’une pile à combustible à membrane échangeuse de protons (PEMFC) industrielle à l’aide de données de dégradation réelles.This thesis develops the approaches for diagnostics and prognostics of uncertain dynamic systems in Bond Graph (BG) modeling framework. Firstly, properties of Interval Arithmetic (IA) and BG in Linear Fractional Transformation, are integrated for representation of parametric and measurement uncertainties on an uncertain BG model. Robust fault detection methodology is developed by utilizing the rules of IA for the generation of adaptive interval valued thresholds over the nominal residuals. The method is validated in real time on an uncertain and highly complex steam generator system.Secondly, a novel hybrid prognostic methodology is developed using BG derived Analytical Redundancy Relationships and Particle Filtering algorithms. Estimations of the current state of health of a system parameter and the associated hidden parameters are achieved in probabilistic terms. Prediction of the Remaining Useful Life (RUL) of the system parameter is also achieved in probabilistic terms. The associated uncertainties arising out of noisy measurements, environmental conditions etc. are effectively managed to produce a reliable prediction of RUL with suitable confidence bounds. The method is validated in real time on an uncertain mechatronic system.Thirdly, the prognostic methodology is validated and implemented on the electrical electro-chemical subsystem of an industrial Proton Exchange Membrane Fuel Cell. A BG of the latter is utilized which is suited for diagnostics and prognostics. The hybrid prognostic methodology is validated, involving real degradation data sets

Reconstruction of January–April discharge of Zemu Chuu – A first stage of Teesta River North Sikkim Eastern Himalaya based on tree-ring data of fir

Study region: Zemu Chuu (river), Lachen, North Sikkim, Eastern Himalaya India. Study focus: Using tree-ring data of fir (Abies densa) the temporal variation of 222 years January–April mean discharge of Zemu Chuu, upper reaches of the Teesta River at Lachen, North Sikkim Eastern Himalaya was investigated. This was based on linear regression reconstruction model which explained variance of 50.1% during calibration period (AD 1976–1996). The model was verified by reduction of error (RE), sign test (ST), product mean test (Pmt), root mean square error (RMSE) and Durbin–Watson test (DW). The RE never falls below zero suggesting the model had explanatory power over the entire period of reconstruction. New hydrological insights for the region: The explored strong relationship between tree ring records and instrumental data enable to develop mean January–April months (premonsoon) river discharge of Zemu Chuu from remote area of Sikkim. Reconstructed data reveals high stream-flow when it is more than the mean plus one standard deviation and as low when flow is less than the mean minus one standard deviation. There were such 23 high discharge and 21 extremely low years over the past AD 1775–1996. This premonsoon reconstruction of river flow would be of great significance when scarcity of water is acute in the North East Himalaya

Semi-partitioned hard-real-time scheduling under locked cache migration in multicore systems

As real-time embedded systems integrate more and more functionality, they are demanding increasing amounts of computational power that can only be met by deploying multicore architectures. The use of multicore architectures with on-chip memory hierarchies and shared communication infrastructure in the context of real-time systems poses several challenges for task scheduling. In this paper, we present a predictable semi-partitioned strategy for scheduling a set of independent hard-real-time tasks on homogeneous multicore platforms using cache locking and locked cache migration. Semipartitioned scheduling strategies form a middle ground between the two extreme approaches, namely global and partitioned scheduling. By making most tasks non-migrating (partitioned), runtime migration overhead is minimized. On the other hand, by allowing some tasks to migrate among cores, schedulability of task sets may be improved. Simulation results demonstrate the effectiveness of our approach in improving task set schedulability over purely partitioned approaches while maintaining real-time predictability of migrating tasks. In our simulations, we achieve an average increase in utilization of 37.31 % and an average increase in density of 81.36 % compared to purely partitioned task allocation. 1