Showing papers by "Toyota published in 2021"

Challenges in applying highly active Pt-based nanostructured catalysts for oxygen reduction reactions to fuel cell vehicles.

Abstract: The past 30 years have seen progress in the development of Pt-based nanocatalysts for the oxygen reduction reaction, and some are now in production on a commercial basis and used for polymer electrolyte fuel cells (PEFCs) for automotives and other applications. Further improvements in catalytic activity are required for wider uptake of PEFCs, however. In laboratories, researchers have developed various catalysts that have much higher activities than commercial ones, and these state-of-the-art catalysts have potential to improve energy conversion efficiencies and reduce the usage of platinum in PEFCs. There are several technical issues that must be solved before they can be applied in fuel cell vehicles, which require a high power density and practical durability, as well as high efficiency. In this Review, the development history of Pt-based nanocatalysts and recent analytical studies are summarized to identify the origin of these technical issues. Promising strategies for overcoming those issues are also discussed. This Review summarizes the development history of Pt-based nanocatalysts and recent analytical studies to identify the technical issues in the automobile application, proposing promising strategies for overcoming the trade-offs among the efficiency,power density, and durability of polymer electrolyte fuel cells.

The rise of electric vehicles—2020 status and future expectations

Abstract: Electric vehicles (EVs) are experiencing a rise in popularity over the past few years as the technology has matured and costs have declined, and support for clean transportation has promoted awareness, increased charging opportunities, and facilitated EV adoption. Suitably, a vast body of literature has been produced exploring various facets of EVs and their role in transportation and energy systems. This paper provides a timely and comprehensive review of scientific studies looking at various aspects of EVs, including: (a) an overview of the status of the light-duty-EV market and current projections for future adoption; (b) insights on market opportunities beyond light-duty EVs; (c) a review of cost and performance evolution for batteries, power electronics, and electric machines that are key components of EV success; (d) charging-infrastructure status with a focus on modeling and studies that are used to project charging-infrastructure requirements and the economics of public charging; (e) an overview of the impact of EV charging on power systems at multiple scales, ranging from bulk power systems to distribution networks; (f) insights into life-cycle cost and emissions studies focusing on EVs; and (g) future expectations and synergies between EVs and other emerging trends and technologies. The goal of this paper is to provide readers with a snapshot of the current state of the art and help navigate this vast literature by comparing studies critically and comprehensively and synthesizing general insights. This detailed review paints a positive picture for the future of EVs for on-road transportation, and the authors remain hopeful that remaining technology, regulatory, societal, behavioral, and business-model barriers can be addressed over time to support a transition toward cleaner, more efficient, and affordable transportation solutions for all.

Autonomous experimentation systems for materials development: A community perspective

Abstract: Summary Solutions to many of the world's problems depend upon materials research and development. However, advanced materials can take decades to discover and decades more to fully deploy. Humans and robots have begun to partner to advance science and technology orders of magnitude faster than humans do today through the development and exploitation of closed-loop, autonomous experimentation systems. This review discusses the specific challenges and opportunities related to materials discovery and development that will emerge from this new paradigm. Our perspective incorporates input from stakeholders in academia, industry, government laboratories, and funding agencies. We outline the current status, barriers, and needed investments, culminating with a vision for the path forward. We intend the article to spark interest in this emerging research area and to motivate potential practitioners by illustrating early successes. We also aspire to encourage a creative reimagining of the next generation of materials science infrastructure. To this end, we frame future investments in materials science and technology, hardware and software infrastructure, artificial intelligence and autonomy methods, and critical workforce development for autonomous research.

Perspective—Combining Physics and Machine Learning to Predict Battery Lifetime

Abstract: Forecasting the health of a battery is a modeling effort that is critical to driving improvements in and adoption of electric vehicles. Purely physics-based models and purely data-driven models have advantages and limitations of their own. Considering the nature of battery data and end-user applications, we outline several architectures for integrating physics-based and machine learning models that can improve our ability to forecast battery lifetime. We discuss the ease of implementation, advantages, limitations, and viability of each architecture, given the state of the art in the battery and machine learning fields. © 2021 The Author(s). Published on behalf of The Electrochemical Society by IOP Publishing Limited. This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 License (CC BY, http://creativecommons.org/licenses/ by/4.0/), which permits unrestricted reuse of the work in any medium, provided the original work is properly cited. [DOI: 10.1149/ 1945-7111/abec55]

py4DSTEM: A Software Package for Four-Dimensional Scanning Transmission Electron Microscopy Data Analysis.

Abstract: Scanning transmission electron microscopy (STEM) allows for imaging, diffraction, and spectroscopy of materials on length scales ranging from microns to atoms. By using a high-speed, direct electron detector, it is now possible to record a full two-dimensional (2D) image of the diffracted electron beam at each probe position, typically a 2D grid of probe positions. These 4D-STEM datasets are rich in information, including signatures of the local structure, orientation, deformation, electromagnetic fields, and other sample-dependent properties. However, extracting this information requires complex analysis pipelines that include data wrangling, calibration, analysis, and visualization, all while maintaining robustness against imaging distortions and artifacts. In this paper, we present py4DSTEM, an analysis toolkit for measuring material properties from 4D-STEM datasets, written in the Python language and released with an open-source license. We describe the algorithmic steps for dataset calibration and various 4D-STEM property measurements in detail and present results from several experimental datasets. We also implement a simple and universal file format appropriate for electron microscopy data in py4DSTEM, which uses the open-source HDF5 standard. We hope this tool will benefit the research community and help improve the standards for data and computational methods in electron microscopy, and we invite the community to contribute to this ongoing project.

Probabilistic 3D Multi-Modal, Multi-Object Tracking for Autonomous Driving

Abstract: Multi-object tracking is an important ability for an autonomous vehicle to safely navigate a traffic scene. Current state-of-the-art follows the tracking-by-detection paradigm where existing tracks are associated with detected objects through some distance metric. Key challenges to increase tracking accuracy lie in data association and track life cycle management. We propose a probabilistic, multi-modal, multiobject tracking system consisting of different trainable modules to provide robust and data-driven tracking results. First, we learn how to fuse features from 2D images and 3D LiDAR point clouds to capture the appearance and geometric information of an object. Second, we propose to learn a metric that combines the Mahalanobis and feature distances when comparing a track and a new detection in data association. And third, we propose to learn when to initialize a track from an unmatched object detection. Through extensive quantitative and qualitative results, we show that when using the same object detectors our method outperforms state-of-the-art approaches on the NuScenes and KITTI datasets.

The role of oxygen-permeable ionomer for polymer electrolyte fuel cells.

Abstract: In recent years, considerable research and development efforts are devoted to improving the performance of polymer electrolyte fuel cells. However, the power density and catalytic activities of these energy conversion devices are still far from being satisfactory for large-scale operation. Here we report performance enhancement via incorporation, in the cathode catalyst layers, of a ring-structured backbone matrix into ionomers. Electrochemical characterizations of single cells and microelectrodes reveal that high power density is obtained using an ionomer with high oxygen solubility. The high solubility allows oxygen to permeate the ionomer/catalyst interface and react with protons and electrons on the catalyst surfaces. Furthermore, characterizations of single cells and single-crystal surfaces reveal that the oxygen reduction reaction activity is enhanced owing to the mitigation of catalyst poisoning by sulfonate anion groups. Molecular dynamics simulations indicate that both the high permeation and poisoning mitigation are due to the suppression of densely layered folding of polymer backbones near the catalyst surfaces by the incorporated ring-structured matrix. These experimental and theoretical observations demonstrate that ionomer’s tailored molecular design promotes local oxygen transport and catalytic reactions. Polymer electrolyte fuel cells are promising but suffer from low performance. Here, the authors use a combination of electrochemical measurements and molecular dynamics simulations to reveal the role of the highly oxygen permeable ionomer in polymer electrolyte fuel cells that enhances the oxygen transport and catalytic activity.

Eco-Driving at Signalized Intersections: A Multiple Signal Optimization Approach

Abstract: Consecutive traffic signalized intersections can increase vehicle stops, producing vehicle accelerations on arterial roads and potentially increasing vehicle fuel consumption levels. Eco-driving systems are one method to improve vehicle energy efficiency with the help of vehicle connectivity. In this paper, an eco-driving system is developed that computes a fuel-optimized vehicle trajectory while traversing more than one signalized intersection. The system is designed in a modular and scalable fashion allowing it to be implemented in large networks without significantly increasing the computational complexity. The proposed system utilizes signal phasing and timing (SPaT) data that are communicated to connected vehicles (CVs) together with real-time vehicle dynamics to compute fuel-optimum trajectories. The proposed algorithm is incorporated in the INTEGRATION microscopic traffic assignment and simulation software to conduct a comprehensive sensitivity analysis of various variables, including: system market penetration rates (MPRs), demand levels, phase splits, offsets and traffic signal spacings on the system performance. The analysis shows that at 100% MPR, fuel consumption can be reduced by as high as 13.8%. Moreover, higher MPRs and shorter phase lengths result in larger fuel savings. Optimum demand levels and traffic signal spacings exist that maximize the effectiveness of the algorithm. Furthermore, the study demonstrates that the algorithm works less effective when the traffic signal offset is closer to its optimal value. Finally, the study highlights the need for further work to enhance the algorithm to deal with over-saturated traffic conditions.

Impact ionization coefficients and critical electric field in GaN

Abstract: Avalanche multiplication characteristics in a reverse-biased homoepitaxial GaN p–n junction diode are experimentally investigated at 223–373 K by novel photomultiplication measurements utilizing above- and below-bandgap illumination. The device has a non-punch-through one-side abrupt p–-n+ junction structure, in which the depletion layer mainly extends to the p-type region. For above-bandgap illumination, the light is absorbed at the surface p+-layer, and the generated electrons diffuse and reach the depletion layer, resulting in an electron-injected photocurrent. On the other hand, for below-bandgap illumination, the light penetrates a GaN layer and is absorbed owing to the Franz–Keldysh effect in the high electric field region (near the p–n junction interface), resulting in a hole-induced photocurrent. The theoretical (non-multiplicated) photocurrents are calculated elaborately, and the electron- and hole-initiated multiplication factors are extracted as ratios of the experimental data to the calculated values. Through the mathematical analyses of the multiplication factors, the temperature dependences of the impact ionization coefficients of electrons and holes in GaN are extracted and formulated by the Okuto–Crowell model. The ideal breakdown voltage and the critical electric field for GaN p–n junctions of varying doping concentration are simulated using the obtained impact ionization coefficients, and their temperature dependence and conduction-type dependence were discussed. The simulated breakdown characteristics show good agreement with data reported previously, suggesting the high accuracy of the impact ionization coefficients obtained in this study.

Cooperative Ramp Merging Design and Field Implementation: A Digital Twin Approach Based on Vehicle-to-Cloud Communication

Abstract: Ramp merging is considered as one of the most difficult driving scenarios due to the chaotic nature in both longitudinal and lateral driver behaviors (namely lack of effective coordination) in the merging area. In this study, we have designed a cooperative ramp merging system for connected vehicles, allowing merging vehicles to cooperate with others prior to arriving at the merging zone. Different from most of the existing studies that utilize dedicated short-range communication, we adopt a Digital Twin approach based on vehicle-to-cloud communication. On-board devices upload the data to the cloud server through the 4G/LTE cellular network. The server creates Digital Twins of vehicles and drivers whose parameters are synchronized in real time with their counterparts in the physical world, processes the data with the proposed models in the digital world, and sends advisory information back to the vehicles and drivers in the physical world. A real-world field implementation has been conducted in Riverside, California, with three passenger vehicles. The results show the proposed system addresses the issues of safety and environmental sustainability with an acceptable communication delay, compared to the baseline scenario where no advisory information is provided during the merging process.

Dirac-Type Nodal Spin Liquid Revealed by Refined Quantum Many-Body Solver Using Neural-Network Wave Function, Correlation Ratio, and Level Spectroscopy

Abstract: New machine-learning methods show evidence of a quantum spin liquid in a 2D model, offering a guide to search for materials hosting this exotic state, in which electrons splinter into spinons, with potential use in quantum devices.

High-temperature superconductivity

Abstract: Despite decades of intense theoretical, experimental and computational effort, a microscopic theory of high-temperature superconductivity is not yet established. Eight researchers share their contributions to the search for a better understanding of unconventional superconductivity and their hopes for the future of the field.

Sparse Auxiliary Networks for Unified Monocular Depth Prediction and Completion

Abstract: Estimating scene geometry from data obtained with cost-effective sensors is key for robots and self-driving cars. In this paper, we study the problem of predicting dense depth from a single RGB image (monodepth) with optional sparse measurements from low-cost active depth sensors. We introduce Sparse Auxiliary Networks (SANs), a new module enabling monodepth networks to perform both the tasks of depth prediction and completion, depending on whether only RGB images or also sparse point clouds are available at inference time. First, we decouple the image and depth map encoding stages using sparse convolutions to process only the valid depth map pixels. Second, we inject this information, when available, into the skip connections of the depth prediction network, augmenting its features. Through extensive experimental analysis on one indoor (NYUv2) and two outdoor (KITTI and DDAD) benchmarks, we demonstrate that our proposed SAN architecture is able to simultaneously learn both tasks, while achieving a new state of the art in depth prediction by a significant margin.

Vision-Cloud Data Fusion for ADAS: A Lane Change Prediction Case Study

Abstract: With the rapid development of intelligent vehicles and some successes in Advanced Driving Assistance Systems (ADAS), a new trend is that mixed levels of human driver engagements will be involved in the transportation system. Therefore, necessary visual guidance for drivers is vitally important under this situation to prevent potential risks. To advance the development of visual guidance systems, we introduce a novel vision-cloud data fusion methodology, integrating camera image and Digital Twin information from the cloud to help intelligent vehicles make better decisions. Target vehicle bounding box is drawn and matched by combining results of object detector running on ego vehicle and position information from the cloud. The best matching result, a 79.2% accuracy under 0.7 intersection over union threshold, is obtained with depth images served as an additional feature source. A case study on lane change prediction is conducted to show the effectiveness of the proposed data fusion methodology, where a multi-layer perceptron algorithm is proposed with modified approaches. Human-in-the-loop simulation results conducted in Unity game engine reveal that the proposed model can improve highway driving performance significantly in terms of safety, comfort, and environmental sustainability.

Digital Twin Simulation of Connected and Automated Vehicles with the Unity Game Engine

Abstract: A Digital Twin is defined as a digital replica of a real entity in the physical world. In this study, the Digital Twin simulation is developed for connected and automated vehicles (CAVs) by leveraging the Unity game engine. A Digital Twin simulation architecture is proposed, which contains the physical world and the digital world. Particularly, the digital world consists of three layers, where the Unity game objects are built to simulate the “hardware”, the Unity scripting API are used to simulate the “software”, and external tools (e.g., SUMO, MATLAB, python, and/or AWS) are leveraged to enhance the simulation functionalities. A case study of personazlied adaptive cruise control (P-ACC) is conducted to showcase the effectiveness of the proposed Digital Twin simulation, where the ACC system can be designed to satisfy each driver's preference with the help of cloud computing.

Laccase-Catalyzed Reactions in Ionic Liquids for Green Sustainable Chemistry

Abstract: Biocatalysis is an important and enabling tool in organic synthesis, degradation of chemical compounds, and biosensors. Laccases are copper ions containing oxidase and catalyze the conversion of po...

PDAN: Pyramid Dilated Attention Network for Action Detection

Abstract: Handling long and complex temporal information is an important challenge for action detection tasks. This challenge is further aggravated by densely distributed actions in untrimmed videos. Previous action detection methods fail in selecting the key temporal information in long videos. To this end, we introduce the Dilated Attention Layer (DAL). Compared to previous temporal convolution layer, DAL allocates attentional weights to local frames in the kernel, which enables it to learn better local representation across time. Furthermore, we introduce Pyramid Dilated Attention Network (PDAN) which is built upon DAL. With the help of multiple DALs with different dilation rates, PDAN can model short-term and long-term temporal relations simultaneously by focusing on local segments at the level of low and high temporal receptive fields. This property enables PDAN to handle complex temporal relations between different action instances in long untrimmed videos. To corroborate the effectiveness and robustness of our method, we evaluate it on three densely annotated, multi-label datasets: Mul-tiTHUMOS, Charades and Toyota Smarthome Untrimmed (TSU) dataset. PDAN is able to outperform previous state-of-the-art methods on all these datasets."Time abides long enough for those who make use of it."Leonardo da Vinci

Rational Solid-State Synthesis Routes for Inorganic Materials.

Abstract: The rational solid-state synthesis of inorganic compounds is formulated as catalytic nucleation on crystalline reactants, where contributions of reaction and interfacial energies to the nucleation barriers are approximated from high-throughput thermochemical data and structural and interfacial features of crystals, respectively. Favorable synthesis reactions are then identified by a Pareto analysis of relative nucleation barriers and phase selectivities of reactions leading to the target. We demonstrate the application of this approach in reaction planning for the solid-state synthesis of a range of compounds, including the widely studied oxides LiCoO2, BaTiO3, and YBa2Cu3O7, as well as other metal oxide, oxyfluoride, phosphate, and nitride targets. Pathways for enabling the retrosynthesis of inorganics are also discussed.