Electrochemical materials discovery and intelligence

Design and implementation of efficient and cost-effective electrochemical devices is a complex challenge. It hinges on big-data driven knowledge at the frontiers of multi-disciplinary efforts in materials discovery and design. These massive data–driven processes, however, require intensive cognitive, yet expensive systems, including human, to determine the best design decisions. A novel approach towards Artificial Intelligence (AI) and Machine Learning (ML) algorithms can overcome the complexity of selecting advanced new materials with the predictable and desired properties. Focusing on advanced electrocatalysts for CO2 conversion as a use case, we demonstrate an AI-driven “Virtual Materials Intelligence” platform (beta) for materials data management and intelligent design equipped with an advanced user interface and predictive capabilities in view of materials properties and function. The platform combines information originating from large data sets of different origins. The data storage, data analysis, and advanced analysis algorithms enable efficient and secure data flow between several different simulation and characterization activities. The cloud-based platform ultimately aims to manage all available materials databases and relevant modeling, simulation, performance, cost, and characterization data and how they can be communicated to materials fabrication and design teams

A Review of Lithium-Ion Battery Models in Techno-economic Analyses of Power Systems

The penetration of the lithium-ion battery energy storage system (BESS) into the power system environment occurs at a colossal rate worldwide. This is mainly because it is considered as one of the major tools to decarbonize, digitalize, and democratize the electricity grid. The economic viability and technical reliability of projects with batteries require appropriate assessment because of high capital expenditures, deterioration in charging/discharging performance and uncertainty with regulatory policies. Most of the power system economic studies employ a simple power-energy representation coupled with an empirical description of degradation to model the lithium-ion battery. This approach to modelling may result in violations of the safe operation and misleading estimates of the economic benefits. Recently, the number of publications on techno-economic analysis of BESS with more details on the lithium-ion battery performance has increased. The aim of this review paper is to explore these publications focused on the grid-scale BESS applications and to discuss the impacts of using more sophisticated modelling approaches. First, an overview of the three most popular battery models is given, followed by a review of the applications of such models. The possible directions of future research of employing detailed battery models in power systems' techno-economic studies are then explored

Simulation of cargo VOC emissions from petroleum tankers in transit in Canadian waters

The emissions of volatile organic compounds (VOCs) from petroleum product tankers potentially represent a significant source of VOCs in port cities. Emission factors are used to estimate the produced VOCs. VOC emissions from transit operations were simulated using a two part model of heat and mass transfer. Using local meteorological data of air temperatures, solar radiation and wind speed, the heat transfer within the tank was modeled. Results showed that bulk cargo temperature remained relatively steady at 25–28°C, the oil surface oscillated diurnally by 1–2°C, and the deck temperature oscillates diurnally by 15–20°C. The solar insolation had the largest effect on the tank temperatures. VOC emissions for two crude oils and gasoline, two tank configurations, and two meteorological conditions were estimated using a model derived from a mass balance on the tank and the obtained temperature profile. Only 3 of 8 scenarios had pressure increases large enough to cause venting of VOC. C2-C5 compounds constituted the majority of VOCs released from crude oils and ethanol made up the majority of the VOCs released from the gasoline carrying barge. The calculated daily emission factors for crude oil and gasoline (barge) were 10 mg/L/day and 135 mg/L/day respectively

Learning Point-wise Abstaining Penalty for Point Cloud Anomaly Detection

LiDAR-based semantic scene understanding is an important module in the modern autonomous driving perception stack. However, identifying Out-Of-Distribution (OOD) points in a LiDAR point cloud is challenging as point clouds lack semantically rich features when compared with RGB images. We revisit this problem from the perspective of selective classification, which introduces a selective function into the standard closed-set classification setup. Our solution is built upon the basic idea of abstaining from choosing any known categories but learns a point-wise abstaining penalty with a marginbased loss. Synthesizing outliers to approximate unlimited OOD samples is also critical to this idea, so we propose a strong synthesis pipeline that generates outliers originated from various factors: unrealistic object categories, sampling patterns and sizes. We demonstrate that learning different abstaining penalties, apart from point-wise penalty, for different types of (synthesized) outliers can further improve the performance. We benchmark our method on SemanticKITTI and nuScenes and achieve state-of-the-art results. Risk-coverage analysis further reveals intrinsic properties of different methods. Codes and models will be publicly available.Comment: codes is available at https://github.com/Daniellli/PAD.gi

Estimation of local relative humidity in cathode catalyst layers of PEFC

A simple method is presented to estimate the local relative humidity (RH) in cathode catalyst layers (CCLs) of polymer electrolyte fuel cells (PEFCs). Based on impedance measurements under different experimental conditions, this technique provides a means to estimate the average value for RH using a correlation with the catalyst layer effective proton resistance. At zero current, a fully humidified anode raises the RH inside the CCL from a nominal 30% to almost 70%. A current density of up to 0.4 A/cm2 also humidifies the cathode, while drying is observed between 0.4 and 1.0 A/cm2.On pr\ue9sente une m\ue9thode simple pour estimer l\u2019humidit\ue9 relative (HR) locale dans les couches de catalyseur de la cathode (CCC) de piles \ue0 combustible \ue0 \ue9lectrolyte polym\ue8re (PCEP). Bas\ue9e sur des mesures d\u2019imp\ue9dance dans diff\ue9rentes conditions exp\ue9rimentales, cette technique fournit un moyen d\u2019estimer la valeur moyenne de l\u2019humidit\ue9 relative au moyen d\u2019une corr\ue9lation avec la r\ue9sistance effective aux protons de la couche de catalyseur. Au courant z\ue9ro, une anode pleinement humidifi\ue9e fait passer l\u2019humidit\ue9 relative interne de la couche de 30 % (valeur nominale) \ue0 70 % (valeur maximale). Une densit\ue9 de courant maximale de 0,4 A/cm2 humidifie aussi la cathode, alors qu\u2019un ass\ue8chement est observ\ue9 entre 0,4 et 1,0 A/cm2.Peer reviewed: YesNRC publication: Ye

Effectiveness Factor of Pt Utilization in Cathode Catalyst Layer of PEFCs

NRC publication: Ye

CFD Modeling and Simulation of PEM Fuel Cells Report 6: Simulation of Hyteon New Design PEM Fuel Cell and Measurement of On-Board Humidifier

NRC publication: Ye

Effects of water vapor proportion in generated water on performance of polymer electrolyte fuel cells under dry operating conditions

A one-dimensional, non-isothermal, single-phase, steady-state model is developed for dry operating conditions to investigate the effects of different types of generated water as liquid or vapor in electrochemical reaction on the proton exchange membrane fuel cell (PEMFC) performance, A parameter (\u393) denoting vapor phase fraction in generated water is employed in the model. The agglomerate model with thin film was employed in catalyst layers. Water transport and its effects on cell performance were discussed under different operating conditions such as reactants relative humidity, vapor phase fraction and micro-porous layer thickness. The results indicated that the net water transport coefficients were positive under equal feeding gas humidity in both anode and cathode. At high current density, the more the proportion of water vapor in generated water is, the better cell performance is. However, at low current density, the effect of vapor phase proportion on cell performance is not very obvious.Peer reviewed: YesNRC publication: Ye