Real-world Operating Conditions Research Articles

Integrative BNN-LHS Surrogate Modeling and Thermo-Mechanical-EM Analysis for Enhanced Characterization of High-Frequency Low-Pass Filters in COMSOL.

This paper pioneers a novel approach in electromagnetic (EM) system analysis by synergistically combining Bayesian Neural Networks (BNNs) informed by Latin Hypercube Sampling (LHS) with advanced thermal-mechanical surrogate modeling within COMSOL simulations for high-frequency low-pass filter modeling. Our methodology transcends traditional EM characterization by integrating physical dimension variability, thermal effects, mechanical deformation, and real-world operational conditions, thereby achieving a significant leap in predictive modeling fidelity. Through rigorous evaluation using Mean Squared Error (MSE), Maximum Learning Error (MLE), and Maximum Test Error (MTE) metrics, as well as comprehensive validation on unseen data, the model's robustness and generalization capability is demonstrated. This research challenges conventional methods, offering a nuanced understanding of multiphysical phenomena to enhance reliability and resilience in electronic component design and optimization. The integration of thermal variables alongside dimensional parameters marks a novel paradigm in filter performance analysis, significantly improving simulation accuracy. Our findings not only contribute to the body of knowledge in EM diagnostics and complex-environment analysis but also pave the way for future investigations into the fusion of machine learning with computational physics, promising transformative impacts across various applications, from telecommunications to medical devices.

Fault detection and diagnosis in light commercial buildings’ HVAC systems: A comprehensive framework, application, and performance evaluation

The data-driven approach currently dominates the field of Automatic Fault Detection and Diagnosis (AFDD) in HVAC systems. However, a significant concern lies in the prevalent use of labeled experimental and simulation data, which often does not represent real-world operational conditions. This study unveils a comprehensive framework for AFDD in light commercial buildings, effectively leveraging unlabeled raw data extracted directly from their Building BAS. Its main goal is to provide a versatile methodology tailored for real-world applicability. Buildings classified as “light commercial” typically have less than 2,500 square meters of floor area and no more than six stories, such as small offices, medical facilities, banks, small manufacturing facilities, etc. A common feature of these buildings is the fact that the HVAC systems tend to be relatively simple and have similar configurations, thus making it easy to develop scalable and reproducible fault detection methods. The study focuses on a practical case study in a light commercial building HVAC system situated in Montreal, Canada, encompassing a single Air Handling Unit (AHU) and four Variable Air Volume (VAV) reheating boxes to evaluate the framework. This comprehensive framework encompasses a sequence of sub-objectives: creating a sizable, synchronized raw dataset from diverse BAS sensor tags, comprehensive data cleansing to address inconsistencies, developing an anomaly detection method, investigating these anomalies to extract underlying rules, and finally, dataset labeling. An AFDD classification model is then applied to evaluate its ability to distinguish normal from faulty conditions across various fault types. The study highlights the potential of dimensional reduction techniques and unsupervised clustering for effective anomaly detection in light commercial buildings, as well as the power of the Decision Tree classifier for uncovering hidden patterns, especially in anomaly conditions. It also highlights the significance of addressing imbalanced datasets in AFDD and the complexities of detecting sizing-related faults. Despite these challenges, the framework exhibits robust performance in detecting and diagnosing a range of HVAC faults. It offers a systematic and adaptable approach for handling real-world operational data in light commercial building HVAC systems, extendible to other building types, bridging the gap between data-driven methods and practical applications.

Suitability Study of Biofuel Blend for Light Commercial Vehicle Application under Real-World Transient Operating Conditions

<div>Driving schedule of every vehicle involves transient operation in the form of changing engine speed and load conditions, which are relatively unchanged during steady-state conditions. As well, the results from transient conditions are more likely to reflect the reality. So, the current research article is focused on analyzing the biofuel-like lemon peel oil (LPO) behavior under real-world transient conditions with fuel injection parameter MAP developed from steady-state experiments. At first, engine parameters and response MAPs are developed by using a response surface methodology (RSM)-based multi-objective optimization technique. Then, the vehicle model has been developed by incorporating real-world transient operating conditions. Finally, the developed injection parameters and response MAPs are embedded in the vehicle model to analyze the biofuel behavior under transient operating conditions. The results obtained for diesel-fueled light commercial vehicle (LCV) have shown better fuel economy than LPO biofuel with their developed fuel injection parameter MAP. The maximum BTE obtained was 29.7% for diesel and 29.5% for LPO at 2100 rpm and 20 Nm torque. The mean HC emissions were identified as 0.02046 g/km for diesel and 0.03488 g/km for LPO fuel over the modified Indian driving cycle (MIDC). Except for NOx emission, LPO biofuel exhibited diesel-like performance and emission characteristics under the MIDC.</div>

Study on the decompression behavior during large-scale pipeline puncture releases of CO2 with various N2 compositions: Experiments and mechanism analysis

Impurities in the captured CO2 stream from various industrial sources may introduce uncertainty into the transient releases of transportation pipelines. Due to limitations in the experimental scale, there is currently limited analysis of the vertical cross-sectional temperature distribution and the pressure response along the pipeline during the releases of impure CO2. The mechanism by which impurities affect the decompression wave and the alterations in phase-change pathways remains unclear. In this study, building upon the existing large-scale pipeline, we established an impurity injection system. Using the common impurity, N2, as a variable, the decompression behavior of the CO2+N2 mixture in the puncture releases and the influence mechanism of N2 were investigated. The results show that the effect of N2 on sound velocity causes a significant decrease in the decompression wave speed. N2 raises the minimum temperature, and it is more pronounced in large-diameter releases; for instance, the minimum temperature during the 100 mm release is always higher than that in the 50 mm release. The gas-liquid mixture closely follows the dew point line, conforming to the Gibbs equilibrium criterion. The experimental scale mirrors real-world operating conditions, endowing the research findings with practical applicability. Concurrently, it furnishes dependable data for numerical researches.

Service life estimation of electric vehicle lithium-ion battery pack using arrhenius mathematical model

Durability is a desired characteristic for all battery packs in Electric Vehicles. In this study, the service life of the EV battery pack under real-world operating conditions is projected using an Arrhenius mathematical simulation model. The model comprises a 39.2 kWh EV Lithium-Ion battery pack integrated with a three-phase inverter to convert the battery pack’s Direct Current output to Alternating Current. In addition, the Alternating Current output is coupled to a 100 kW permanent magnet synchronous motor, which is regarded as the load. A field-oriented controller provides pulse width-modulated output signals that are supplied back to the inverter to generate the correct driving current. Variable conditions of charge rate (C-rate: 1.25C − 4C), discharge rate (C-rate: 0.5C − 4C), temperature (25°C–60°C), and depth of discharge (30%–90%) are evaluated to determine the battery pack’s service life. Under a 4C charge rate/0.5C discharge rate and 50% depth of discharge, the modeling results indicate the battery pack has a service life of approximately 6,000 h at low temperatures (25°C) and roughly 3,000 h at high temperatures (60°C). The model has been validated by comparing the results with experimental data from the literature.

Experimental and numerical investigation of the aerodynamic characteristics of high-performance vehicle configurations under yaw conditions

This study investigates the impact of yaw conditions on the aerodynamic performance and flow field of three high-performance vehicle model configurations by means of wind tunnel testing and unsteady Reynolds-Averaged Navier–Stokes-based computational fluid dynamics simulations. While yaw effects on automotive vehicles have been explored, the effects on far more complex flow fields of high-performance vehicles remain insufficiently researched. This paper reveals that yaw conditions have a significant negative influence both downforce and drag performance. Spoiler and rear wing devices enhance downforce but increase the vehicle's sensitivity to yaw. Furthermore, yaw conditions significantly alter vortex structures and local flow velocities, affecting downstream flow behavior. Surface pressure measurements on the slant confirm these findings and highlight notable yaw effects and upstream effects from spoiler and rear wing devices. Wake analyses through total pressure measurements show that yaw induces a substantial deviation from straight-line wake characteristics, which become dominated by an inboard rotating vehicle body vortex. Overall, this research enhances the understanding of the effects of yaw conditions on high-performance vehicle aerodynamics and provides valuable data for future vehicle aerodynamics research in real-world operating conditions.

Online Adaptive Model Identification and State of Charge Estimation for Vehicle-Level Battery Packs

Accurate state of charge (SOC) estimation of traction batteries plays a crucial role in energy and safety management for electric vehicles. Existing studies focus primarily on cell battery SOC estimation. However, numerical instability and divergence problems might occur for a large-size lithium-ion battery pack consisting of many cells. This paper proposes a high-performance online model identification and SOC estimation method based on an adaptive square root unscented Kalman filter (ASRUKF) and an improved forgetting factor recursive least squares (IFFRLS) for vehicle-level traction battery packs. The model parameters are identified online through the IFFRLS, where the conventional method might encounter numerical stability problems. By updating the square root of the covariance matrix, the divergence problem in the traditional unscented Kalman filter is solved in the ASRUKF algorithm, where the positive semi-definiteness of the covariance matrix is guaranteed. Combined with the adaptive noise covariance matched filtering algorithm and real-time compensation of system error, the proposed method solves the problem of ever-degrading estimation accuracy in the presence of time-varying noise with unknown statistical characteristics. Using a 66.2-kWh vehicle battery pack, we experimentally verified that the proposed algorithm could achieve high estimation accuracy with guaranteed numerical stability. The maximum error of SOC estimation can be bounded by 1%, and the root-mean-square error is as low as 0.47% under real-world vehicle operating conditions.

Fair and Scalable Orchestration of Network and Compute Resources for Virtual Edge Services

The combination of service virtualization and edge computing allows for low latency services, while keeping data storage and processing local. However, given the limited resources available at the edge, a conflict in resource usage arises when both virtualized user applications and network functions need to be supported. Further, the concurrent resource request by user applications and network functions is often entangled, since the data generated by the former has to be transferred by the latter, and vice versa. In this paper, we first show through experimental tests the correlation between a video-based application and a vRAN. Then, owing to the complex involved dynamics, we develop a scalable reinforcement learning framework for resource orchestration at the edge, which leverages a Pareto analysis for provable fair and efficient decisions. We validate our framework, named VERA, through a real-time proof-of-concept implementation, which we also use to obtain datasets reporting real-world operational conditions and performance. Using such experimental datasets, we demonstrate that VERA meets the KPI targets for over <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$96\%$</tex-math></inline-formula> of the observation period and performs similarly when executed in our real-time implementation, with KPI differences below 12.4%. Further, its scaling cost is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$54\%$</tex-math></inline-formula> lower than a centralized framework based on deep-Q networks.

Testing the fragmentation of railway ballast material by laboratory methods using Proctor compactor

The physical classification of crushed stone and gravel used in railway construction is based on their strength and endurance and is performed by a laboratory test method using a rotating drum or a mortar method. The values of fracture resistance calculated using the Los Angeles method and abrasion calculated using the Micro-Deval method show a corresponding correlation and require further investigation. Purpose. The development of a new method for measuring rock material fracture that is consistent with widely used standards while also being more comparable to real-world railway operating conditions. Certainly, both standard tests are essential for ensuring product homogeneity during production, so the new recommended method is only a supplement. Methodology. The Proctor device was used to induce so-called shock loads from above, similar to railway loading conditions. Unlike the standard method, the andesite material was placed in a standard cylinder in these tests. The samples were pre-screened and sorted; the specified weight was approximately 1,300 g, and the specified sizes of the individual particles were 6.3, 8.0 and 11.2 mm. Only prewashed and dried materials of NZ (fine crushed stone) or KZ (special crushed stone) from four different quarries (Tállya, Szob, Nógrádkövesd, Recsk) with different rock physics characteristics were considered. The Proctor compactor machine was used because of its calculable labor (19.86 J/impact) and the crushing effect of the calculable impacts (64, 128, 256 and 1,028 blows). Even after loading different numbers of impacts, homogeneous samples from different quarries were sieved to measure the masses of fragments per fraction. Findings. The set of measurements made it possible to establish a series of fragmentation and degradation curves for each of the three repeated measurements based on the composition of the material and the number of blows, which showed the degradation of samples with different physical and mechanical properties of the rock material and particle sizes. With an increasing number of impacts, the amount of crushed material in the sample increased, but the distribution of crushed material did not decrease evenly and proportionally as the number of impacts increased. Parameters and indices were also computed to identify various correlations (i. e., FV, d &lt; 22.4, d &lt; 0.5, d &lt; 0.063 mm, CU, M ratio,  ratio). Some of them (e. g., FV) needed to be changed, but they were predefined due to the nature of the tests. Originality. While many standard and alternative railway track ballast fragmentation test methods and measurement tools are available, this paper proposes a new laboratory method and demonstrates the specific measurement and application effectiveness. Practical value. In addition to standard tests that are already widely used, the new method for measuring the fractional composition of railway ballast can help simulate real-world operating conditions of a railroad track in the laboratory. This method will improve the safety of railway operations.

Comparison of Modern Deep Learning Models for Speaker Verification

This research presents an extensive comparative analysis of a selection of popular deep speaker embedding models, namely WavLM, TitaNet, ECAPA, and PyAnnote, applied in speaker verification tasks. The study employs a specially curated dataset, specifically designed to mirror the real-world operating conditions of voice models as accurately as possible. This dataset includes short, non-English statements gathered from interviews on a popular online video platform. The dataset features a wide range of speakers, with 33 males and 17 females, making a total of 50 unique voices. These speakers vary in age from 20 to 70 years old. This variety helps in thoroughly testing speaker verification models. This dataset is especially useful for research on speaker verification with short recordings. It consists of 10 clips for each person, each clip being no longer than 10 s, adding up to 500 recordings in total. The total length of all recordings is about 1 h and 30 min, which averages to roughly 100 s for each speaker. This dataset is a valuable tool for research in speaker verification, particularly for studies involving short audio clips. The performance of these models is evaluated using common biometric metrics such as false acceptance rate (FAR), false rejection rate (FRR), equal error rate (EER) and detection cost function (DCF). The results reveal that the TitaNet and ECAPA models stand out by presenting the lowest EER (1.91% and 1.71%, respectively) and thus exhibiting higher discriminative features, ensuring, on the one hand, a reduction in intra-class distance (the same speaker), and, on the other hand, maximizing the distance between different speaker embeddings. This analysis also highlights the ECAPA model’s advantageous balance of performance and efficiency, achieving an inference time of 69.43 milliseconds, slightly longer than the PyAnnote models. This study not only compares the performance of models but also provides a comparative analysis of respective model embeddings, offering insights into their strengths and weaknesses. The presented findings serve as a foundation for guiding future research in speaker verification, especially in the context of short audio samples or limited data. This may be particularly relevant for applications requiring quick and accurate speaker identification from short voice clips.

Modeling and Control Simulation of Power Converters in Automotive Applications

This research introduces a model-based approach for the analysis and control of an onboard charger (OBC) system for contemporary electrified vehicles. The primary objective is to integrate the modeling of SiC/GaN MOSFETs electrothermal behaviors into a unified simulation framework. The motivation behind this project stems from the fact that existing literature often relies on finite element method (FEM) software to examine thermal dynamics, necessitating the development of complex models through partial derivative equations. Such intricate models are computationally demanding, making it difficult to integrate them with circuit equations in the same virtual environment. As a result, lengthy wait periods and a lack of communication between the electrothermal models limit the thorough study that can be conducted during the design stage. The selected case study for examination is a modular 1ϕ (single phase) onboard computer (OBC). This system comprises a dual active bridge (DAB) type DC/DC converter, which is positioned after a totem pole power factor correction (PFC) AC/DC converter. Specifically, the focus is directed toward a 7 kW onboard computer (OBC) utilizing high-voltage SiC/GaN MOSFETs to ensure optimal efficiency and performance. A systematic approach is presented for the assessment and selection of electronic components, employing circuit models for the totem pole power factor correction (PFC) and dual active bridge (DAB) converter. These models are employed in simulations closely mimicking real-world scenarios. Furthermore, rigorous testing of the generated models is conducted across a spectrum of real-world operating conditions to validate the stability of the implemented control algorithms. The validation process is bolstered by a comprehensive exploration of parametric variations relative to the nominal case. Notably, each simulation adheres to the recommended operational limits of the selected components and devices. Detailed data sheets encompassing electrothermal properties are provided for contextual reference.

Representative Real-Time Dataset Generation Based on Automated Fault Injection and HIL Simulation for ML-Assisted Validation of Automotive Software Systems

Recently, a data-driven approach has been widely used at various stages of the system development lifecycle thanks to its ability to extract knowledge from historical data. However, despite its superiority over other conventional approaches, e.g., approaches that are model-based and signal-based, the availability of representative datasets poses a major challenge. Therefore, for various engineering applications, new solutions to generate representative faulty data that reflect the real world operating conditions should be explored. In this study, a novel approach based on a hardware-in-the-loop (HIL) simulation and automated real-time fault injection (FI) method is proposed to generate, analyse and collect data samples in the presence of single and concurrent faults. The generated dataset is employed for the development of machine learning (ML)-assisted test strategies during the system verification and validation phases of the V-cycle development model. The developed framework can generate not only time series data but also a textual data including fault logs in an automated manner. As a case study, a high-fidelity simulation model of a gasoline engine system with a dynamic entire vehicle model is utilised to demonstrate the capabilities and benefits of the proposed framework. The results reveal the applicability of the proposed framework in simulating and capturing the system behaviour in the presence of faults occurring within the system’s components. Furthermore, the effectiveness of the proposed framework in analysing system behaviour and acquiring data during the validation phase of real-time systems under realistic operating conditions has been demonstrated.

A methodology to develop multi-physics dynamic fuel cell system models validated with vehicle realistic drive cycle data

Fuel cell (FC) technology has been identified as a technically attractive solution to decarbonize the transportation sector, especially for heavy-duty vehicles. In this context, the industry and the scientific community are in need of advanced fuel cell systems (FCS) models that are able to replicate real-world operating conditions. Due to the scarcity of said models in the open literature, this study aimed to develop a comprehensive methodology to calibrate and validate multi-physics dynamic FCS models. Therefore, the key contribution of this paper is the detailed description of the calibration process for each component and the calibration order. The specific focus here was to accurately describe the behavior of the FC stack as well as the cathode, anode, and cooling circuits of the balance of plant. The model was calibrated with the aid of experimental data from a Toyota Mirai FC electric vehicle, which was predominantly retrieved from the vehicle’s Controller Area Network (CAN) bus system thereby negating the need for major intrusion into the powertrain system. The validation process was deemed successful with the model being able to truthfully replicate the characteristics of the FC vehicle operated on the World-wide harmonized Light duty Test Cycle (WLTC) 3b and US06 driving cycle. The time-resolved physical parameters such as the cathode pressure, mass flow, or the FC stack temperature were captured with high fidelity, while the overall performance parameters such as the H2 consumption in the stack and the system, and the compressor energy consumption were predicted accurately with a deviation lower than 0.47%, 1.75% and 1.89% with respect to the experimental data, respectively.

PVT Analysis and Behavioral Modeling of Doherty and Envelope Tracking RF ULP Power Amplifiers using 65 nm CMOS Technology

This research encompasses both Process-Voltage-Temperature (PVT) considerations and behavioral modeling of two proposed Power Amplifier (PA) designs for wireless communication systems. Process variations, supply voltage changes, and temperature changes provide difficulties for the design and optimization of power amplifiers for wireless communication systems and may have a substantial influence on their effectiveness. PVT analysis and behavioral modeling have been conducted to address the aforementioned challenges and characterize the behavior and performance of the power amplifiers under real-world operating conditions. It investigates the impact of process variations, supply voltage variations, and temperature variations on the performance of the PAs. The insights gained from this analysis contribute to a deeper understanding of the power amplifiers' performance, efficiency, and suitability for specific wireless communication standards, laying the foundation for future advancements in RF power amplifier design. The first design focuses on a Doherty PA (DPA) tailored for low-power short-range applications complying with the IEEE Wireless Personal Area Network (WPAN) standard. The second design explores an Envelope Tracking (ET) supply bias control for low-power long-range applications conforming to the IEEE Wireless Local Area Network (WLAN) standard. By examining these factors, the research ensures that the PAs exhibit reliable and optimal performance under real-world operating conditions. The PVT corners show a change in gain of only 0.4 dB for DPA and 0.9 dB for ET PA. Furthermore, behavioral modeling is employed to characterize the power efficiency of the envelope tracking PA, along with the current efficiency and quotient current on the load current scale. These models provide valuable insights into the behavior and performance of the PAs at a higher level of abstraction. The results and findings contribute to a deeper understanding of the PA’s performance, efficiency, and suitability for specific wireless communication standards, laying the foundation for future advancements in RF power amplifier design.

State of Charge Estimation for Electric Vehicles Using Random Forest

This paper introduces an innovative approach to addressing a critical challenge in the electric vehicle (EV) industry—the accurate estimation of the state of charge (SOC) of EV batteries under real-world operating conditions. The electric mobility landscape is rapidly evolving, demanding more precise SOC estimation methods to improve range prediction accuracy and battery management. This study applies a Random Forest (RF) machine learning algorithm to improve SOC estimation. Traditionally, SOC estimation has posed a formidable challenge, particularly in capturing the complex dependencies between various parameters and SOC values during dynamic driving conditions. Previous methods, including the Extreme Learning Machine (ELM), have exhibited limitations in providing the accuracy and robustness required for practical EV applications. In contrast, this research introduces the RF model, for SOC estimation approach that excels in real-world scenarios. By leveraging decision trees and ensemble learning, the RF model forms resilient relationships between input parameters, such as voltage, current, ambient temperature, and battery temperatures, and SOC values. This unique approach empowers the model to deliver precise and consistent SOC estimates across diverse driving conditions. Comprehensive comparative analyses showcase the superiority of the RF over ELM. The RF model not only outperforms in accuracy but also demonstrates exceptional robustness and reliability, addressing the pressing needs of the EV industry. The results of this study not only underscore the potential of RF in advancing electric mobility but also suggest a promising integration of the SOC estimation approach into the battery management system of BMW i3. This integration holds the key to more efficient and dependable electric vehicle operations, marking a significant milestone in the ongoing evolution of EV technology. Importantly, the RF model demonstrates a lower Root Mean Squared Error (RMSE) of 5.9028% compared to 6.3127% for ELM, and a lower Mean Absolute Error (MAE) of 4.4321% versus 5.1112% for ELM across rigorous k-fold cross-validation testing, reaffirming its superiority in quantitative SOC estimation.

The impact of pre-compression and temperature on perfluoroalkoxy alkane springs: Insights into spring relaxation.

A predictive model was proposed for determining the high-temperature free height of perfluoroalkoxy alkane springs in air-operated double-bellow pumps with the aim of investigating their relaxation. The model incorporates classical spring deformation theory and considers the material, structure, and real-world operating conditions of the perfluoroalkoxy alkane springs. Experimental validation of the model is also conducted. This study examines the effects of varying temperatures and pre-compression values on the relaxation of perfluoroalkoxy alkane springs' free height. It collects relaxation curves under different temperatures and various pre-compression conditions. The results indicate that spring relaxation increases with higher temperatures when there is no pre-compression. Furthermore, increasing pre-compression at the same temperatures leads to greater spring relaxation. Pre-compression has a more significant impact on spring relaxation. By comparing the experimental data with the simulated curve generated by the model, it is evident that the predicted spring free height relaxation closely aligns with the actual measurements. This verification demonstrates the effectiveness and accuracy of the proposed model in evaluating the relaxation of perfluoroalkoxy alkane springs' free height. Moreover, the model provides a valuable tool for predicting the lifespan of similar perfluoroalkoxy alkane springs in engineering applications.

Bir Havacılık Elastoplastik Yapısal Delikli Silindirik Bileşenin Döngüsel Mekanik Yük Altında COMSOL Multiphysics ve Taguchi Metodu Optimizasyonu ile Yorulma Analizi

This research study focuses on the fatigue behavior of an aerospace elastoplastic cylindrical structural component with a hole subjected to cyclic mechanical loads. In the demanding operational environment of aerospace applications, the structural components, particularly those with stress concentrators like holes, experience cyclic loading conditions, leading to fatigue failure over time. The key objective of this study is to gain insights into this fatigue behavior, and to develop an optimized set of design and operational parameters that can enhance the fatigue performance of these components. Utilizing the robust finite element analysis capabilities of COMSOL Multiphysics, a comprehensive model of the elastoplastic cylindrical component is developed. The model captures the intricate effects of the hole, a typical stress raiser, on the fatigue performance under various cyclic mechanical loading conditions. A detailed fatigue analysis is then performed using this model, providing valuable insights into the fatigue life and failure patterns of the component. To enhance the fatigue performance, the Taguchi method, a statistical approach, is employed. This method helps to identify and optimize the key design and operational parameters influencing the fatigue life. The parameters are optimized based on their signal-to-noise ratio, with an aim to maximize the fatigue life and ensure the structural integrity of the component under operational cyclic loads. The findings of this research hold significant implications for the design and manufacturing of aerospace structural components, with potential benefits of improved safety, enhanced durability, and reduced maintenance requirements. However, the results' applicability might be limited by the complexity of real-world operational conditions and the assumptions made in the simulation model. Future studies can validate and enhance these results by incorporating more complex loading scenarios and real-world case studies.

Proposing a Model for Platinum Nanoparticles Dissolution in PEM Fuel Cells to Describe Unexplained Electrocatalyst Degradation Consequent to Low Cell Voltage Excursion

Polymer electrolyte membrane fuel cells (PEMFC) are required to achieve a durability target of more than 20’000 operating hours for application in the heavy-duty transport sector. To accelerate the wide-spread commercialization of this technology, specific attention is thus dedicated to the degradation mechanisms that affect the membrane and the cathode catalyst layer, the components mostly affected by ageing. A previous experimental analysis of durability [1] pointed out that degradation under real-world operating conditions for automotive applications induces a progressive decline in power density and efficiency, mainly associated to a loss in the electrocatalyst active surface area (ECSA). Further experimental tests confirmed that the cathode ECSA during the initial 1000 hours undergoes a stabilization of the nanoparticle distribution towards an average radius of 7 nm and this degradation is accelerated by excursions to low cell voltage values. Such events occur during regular operation while shutting-down the system for short time (the so called “short-stops”), when air is removed at the cathode side with hydrogen flowing at the anode to avoid air/hydrogen fronts. The aim of this work is to analyze this unexpected acceleration in the ECSA loss for oxygen reduction reaction by means of numerical modelling.Dissolution of platinum nanoparticles in acidic media was extensively analyzed over the last decade and several models were proposed to simulate both cathodic [2] and anodic [3-5] dissolution. These models were applied to the experimental voltage profiles reported in Figure 1(a), which included a low voltage period that mimics a short stop. A systematic study of the low voltage values was carried out in the range between 0.7 V to 0 V. As also observed in Figure 1(b) state-of-the-art models from literature were not able to explain the observed difference between the cases. Aiming to provide a physical description of this aspect a new ageing mechanism was introduced in the model starting from [2] and different model formulations were considered. Formation of PtO2 was assumed to occur, supported by a specific feature that was observed in cathodic scan of the cyclic voltammetry within the potential range of the short-stop (0.1V - 0.3V vs DHE. The electrocatalyst surface coverage was thus estimated from voltammetry. An additional cathodic dissolution reaction is finally proposed that involves PtO2 reduction and a kinetic rate was included in the model, in analogy with the existing reaction mechanism taken from [2]. Good consistency was observed with the experimental results reported in Figure 1(c) and also over a wide dataset reporting cathode ECSA loss consequent to different voltage profiles. ECSA stabilization was attributed to the formation of PtO2 over the corners of the nanoparticle that could explain a mitigation of the dissolution mechanism for particles larger than 7 nm, in which corner sites lead to minor coverage.The results of this work will allow to accelerate the development of mitigation strategies and innovative materials to improve the durability of State-of-the-Art electrocatalysts for PEMFC. This work received support from the Italian government (Progetto PERMANENT - BANDO MITE PNRR Missione 2 Investimento 3.5 A - RSH2A_0O0012).

Thermal Disorder-Induced Strain and Carrier Localization Activate Reverse Halide Segregation.

The reversal of halide ions is studied under various conditions. However, the underlying mechanism of heat-induced reversal remains unclear. This work finds that dynamic disorder-induced localization of self-trapped polarons and thermal disorder-induced strain (TDIS) can be co-acting drivers of reverse segregation. Localization of polarons results in an order of magnitude decrease in excess carrier density (polaron population), causing a reduced impact of the light-induced strain (LIS - responsible for segregation) on the perovskite framework. Meanwhile, exposing the lattice to TDIS exceeding the LIS can eliminate the photoexcitation-induced strain gradient, as thermal fluctuations of the lattice can mask the LIS strain. Under continuous 0.1Wcm⁻2 illumination (upon segregation), the strain disorder is estimated to be 0.14%, while at 80°C under dark conditions, the strain is 0.23%. However, in situ heating of the segregated film to 80°C under continuous illumination (upon reversal) increases the total strain disorder to 0.25%, where TDIS is likely to have a dominant contribution. Therefore, the contribution of entropy to the system's free energy is likely to dominate, respectively. Various temperature-dependent in situ measurements and simulations further support the results. These findings highlight the importance of strain homogenization for designing stable perovskites under real-world operating conditions.

A hierarchical enhanced data-driven battery pack capacity estimation framework for real-world operating conditions with fewer labeled data

Battery pack capacity estimation under real-world operating conditions is important for battery performance optimization and health management, contributing to the reliability and longevity of battery-powered systems. However, complex operating conditions, coupling cell-to-cell inconsistency, and limited labeled data pose great challenges to accurate and robust battery pack capacity estimation. To address these issues, this paper proposes a hierarchical data-driven framework aimed at enhancing the training of machine learning models with fewer labeled data. Unlike traditional data-driven methods that lack interpretability, the hierarchical data-driven framework unveils the “mechanism” of the black box inside the data-driven framework by splitting the final estimation target into cell-level and pack-level intermediate targets. A generalized feature matrix is devised without requiring all cell voltages, significantly reducing the computational cost and memory resources. The generated intermediate target labels and the corresponding features are hierarchically employed to enhance the training of two machine learning models, effectively alleviating the difficulty of learning the relationship from all features due to fewer labeled data and addressing the dilemma of requiring extensive labeled data for accurate estimation. Using only 10% of degradation data, the proposed framework outperforms the state-of-the-art battery pack capacity estimation methods, achieving mean absolute percentage errors of 0.608%, 0.601%, and 1.128% for three battery packs whose degradation load profiles represent real-world operating conditions. Its high accuracy, adaptability, and robustness indicate the potential in different application scenarios, which is promising for reducing laborious and expensive aging experiments at the pack level and facilitating the development of battery technology.