Robert Bosch (United States)

The lithium-sulfur battery holds a high theoretical energy density, 4-5 times that of today's lithium-ion batteries, yet its applications have been hindered by poor electronic conductivity of the sulfur cathode and, most importantly, the rapid fading of its capacity due to the formation of soluble polysulfide intermediates (Li(2)S(n), n = 4-8). Despite numerous efforts concerning this issue, combatting sulfur loss remains one of the greatest challenges. Here we show that this problem can be effectively diminished by controlling the sulfur as smaller allotropes. Metastable small sulfur molecules of S(2-4) were synthesized in the confined space of a conductive microporous carbon matrix. The confined S(2-4) as a new cathode material can totally avoid the unfavorable transition between the commonly used large S(8) and S(4)(2-). Li-S batteries based on this concept exhibit unprecedented electrochemical behavior with high specific capacity, good cycling stability, and superior rate capability, which promise a practicable battery with high energy density for applications in portable electronics, electric vehicles, and large-scale energy storage systems.

This work presents Neural Equivariant Interatomic Potentials (NequIP), an E(3)-equivariant neural network approach for learning interatomic potentials from ab-initio calculations for molecular dynamics simulations. While most contemporary symmetry-aware models use invariant convolutions and only act on scalars, NequIP employs E(3)-equivariant convolutions for interactions of geometric tensors, resulting in a more information-rich and faithful representation of atomic environments. The method achieves state-of-the-art accuracy on a challenging and diverse set of molecules and materials while exhibiting remarkable data efficiency. NequIP outperforms existing models with up to three orders of magnitude fewer training data, challenging the widely held belief that deep neural networks require massive training sets. The high data efficiency of the method allows for the construction of accurate potentials using high-order quantum chemical level of theory as reference and enables high-fidelity molecular dynamics simulations over long time scales.

In order to achieve autonomous operation of a vehicle in urban situations with unpredictable traffic, several realtime systems must interoperate, including environment perception, localization, planning, and control. In addition, a robust vehicle platform with appropriate sensors, computational hardware, networking, and software infrastructure is essential. We previously published an overview of Junior, Stanford's entry in the 2007 DARPA Urban Challenge. This race was a closed-course competition which, while historic and inciting much progress in the field, was not fully representative of the situations that exist in the real world. In this paper, we present a summary of our recent research towards the goal of enabling safe and robust autonomous operation in more realistic situations. First, a trio of unsupervised algorithms automatically calibrates our 64-beam rotating LIDAR with accuracy superior to tedious hand measurements. We then generate high-resolution maps of the environment which are subsequently used for online localization with centimeter accuracy. Improved perception and recognition algorithms now enable Junior to track and classify obstacles as cyclists, pedestrians, and vehicles; traffic lights are detected as well. A new planning system uses this incoming data to generate thousands of candidate trajectories per second, choosing the optimal path dynamically. The improved controller continuously selects throttle, brake, and steering actuations that maximize comfort and minimize trajectory error. All of these algorithms work in sun or rain and during the day or night. With these systems operating together, Junior has successfully logged hundreds of miles of autonomous operation in a variety of real-life conditions.

Lithium/air batteries, based on their high theoretical specific energy, are an extremely attractive technology for electrical energy storage that could make long-range electric vehicles widely affordable. However, the impact of this technology has so far fallen short of its potential due to several daunting challenges. In nonaqueous Li/air cells, reversible chemistry with a high current efficiency over several cycles has not yet been established, and the deposition of an electrically resistive discharge product appears to limit the capacity. Aqueous cells require water-stable lithium-protection membranes that tend to be thick, heavy, and highly resistive. Both types of cell suffer from poor oxygen redox kinetics at the positive electrode and deleterious volume and morphology changes at the negative electrode. Closed Li/air systems that include oxygen storage are much larger and heavier than open systems, but so far oxygen- and OH−-selective membranes are not effective in preventing contamination of cells. In this review we discuss the most critical challenges to developing robust, high-energy Li/air batteries and suggest future research directions to understand and overcome these challenges. We predict that Li/air batteries will primarily remain a research topic for the next several years. However, if the fundamental challenges can be met, the Li/air battery has the potential to significantly surpass the energy storage capability of today's Li-ion batteries.

Abstract This article presents the architecture of Junior, a robotic vehicle capable of navigating urban environments autonomously. In doing so, the vehicle is able to select its own routes, perceive and interact with other traffic, and execute various urban driving skills including lane changes, U‐turns, parking, and merging into moving traffic. The vehicle successfully finished and won second place in the DARPA Urban Challenge, a robot competition organized by the U.S. Government. © 2008 Wiley Periodicals, Inc.

This report describes a set of scientific procedures used to assess the impact of foods and food ingredients on the expression of appetite (psychological and behavioural). An overarching priority has been to enable potential evaluators of health claims about foods to identify justified claims and to exclude claims that are not supported by scientific evidence for the effect cited. This priority follows precisely from the principles set down in the PASSCLAIM report. The report allows the evaluation of the strength of health claims, about the effects of foods on appetite, which can be sustained on the basis of the commonly used scientific designs and experimental procedures. The report includes different designs for assessing effects on satiation as opposed to satiety, detailed coverage of the extent to which a change in hunger can stand alone as a measure of appetite control and an extensive discussion of the statistical procedures appropriate for handling data in this field of research. Because research in this area is continually evolving, new improved methodologies may emerge over time and will need to be incorporated into the framework. One main objective of the report has been to produce guidance on good practice in carrying out appetite research, and not to set down a series of commandments that must be followed.

Safe handling of dynamic highway and inner city scenarios with autonomous vehicles involves the problem of generating traffic-adapted trajectories. In order to account for the practical requirements of the holistic autonomous system, we propose a semi-reactive trajectory generation method, which can be tightly integrated into the behavioral layer. The method realizes long-term objectives such as velocity keeping, merging, following, stopping, in combination with a reactive collision avoidance by means of optimal-control strategies within the Frenét-Frame of the street. The capabilities of this approach are demonstrated in the simulation of a typical high-speed highway scenario.

A summary of the most important technological applications employing reduced graphene oxide.

Identifying a person by his or her voice is an important human trait most take for granted in natural human-to-human interaction/communication. Speaking to someone over the telephone usually begins by identifying who is speaking and, at least in cases of familiar speakers, a subjective verification by the listener that the identity is correct and the conversation can proceed. Automatic speaker-recognition systems have emerged as an important means of verifying identity in many e-commerce applications as well as in general business interactions, forensics, and law enforcement. Human experts trained in forensic speaker recognition can perform this task even better by examining a set of acoustic, prosodic, and linguistic characteristics of speech in a general approach referred to as structured listening. Techniques in forensic speaker recognition have been developed for many years by forensic speech scientists and linguists to help reduce any potential bias or preconceived understanding as to the validity of an unknown audio sample and a reference template from a potential suspect. Experienced researchers in signal processing and machine learning continue to develop automatic algorithms to effectively perform speaker recognition?with ever-improving performance?to the point where automatic systems start to perform on par with human listeners. In this article, we review the literature on speaker recognition by machines and humans, with an emphasis on prominent speaker-modeling techniques that have emerged in the last decade for automatic systems. We discuss different aspects of automatic systems, including voice-activity detection (VAD), features, speaker models, standard evaluation data sets, and performance metrics. Human speaker recognition is discussed in two parts?the first part involves forensic speaker-recognition methods, and the second illustrates how a na?ve listener performs this task from a neuroscience perspective. We conclude this review with a comparative study of human versus machine speaker recognition and attempt to point out strengths and weaknesses of each.

A simultaneously accurate and computationally efficient parametrization of the potential energy surface of molecules and materials is a long-standing goal in the natural sciences. While atom-centered message passing neural networks (MPNNs) have shown remarkable accuracy, their information propagation has limited the accessible length-scales. Local methods, conversely, scale to large simulations but have suffered from inferior accuracy. This work introduces Allegro, a strictly local equivariant deep neural network interatomic potential architecture that simultaneously exhibits excellent accuracy and scalability. Allegro represents a many-body potential using iterated tensor products of learned equivariant representations without atom-centered message passing. Allegro obtains improvements over state-of-the-art methods on QM9 and revMD17. A single tensor product layer outperforms existing deep MPNNs and transformers on QM9. Furthermore, Allegro displays remarkable generalization to out-of-distribution data. Molecular simulations using Allegro recover structural and kinetic properties of an amorphous electrolyte in excellent agreement with ab-initio simulations. Finally, we demonstrate parallelization with a simulation of 100 million atoms.

Lithium-ion (Li-ion) batteries are ubiquitous sources of energy for portable electronic devices. Compared to alternative battery technologies, Li-ion batteries provide one of the best energy-to-weight ratios, exhibit no memory effect, and have low self-discharge when not in use. These beneficial properties, as well as decreasing costs, have established Li-ion batteries as a leading candidate for the next generation of automotive and aerospace applications. In the automotive sector, increasing demand for hybrid electric vehicles (HEVs), plug-in HEVs (PHEVs), and EVs has pushed manufacturers to the limits of contemporary automotive battery technology. This limitation is gradually forcing consideration of alternative battery technologies, such as Li-ion batteries, as a replacement for existing leadacid and nickel-metal-hydride batteries. Unfortunately, this replacement is a challenging task since automotive applications demand large amounts of energy and power and must operate safely, reliably, and durably at these scales. The article presents a detailed description and model of a Li-ion battery. It begins the section "Intercalation-Based Batteries" by providing an intuitive explanation of the fundamentals behind storing energy in a Li-ion battery. In the sections "Modeling Approach" and "Li-Ion Battery Model," it present equations that describe a Li-ion cell's dynamic behavior. This modeling is based on using electrochemical principles to develop a physics-based model in contrast to equivalent circuit models. A goal of this article is to present the electrochemical model from a controls perspective.

BACKGROUND: Electrocardiographic markers of left ventricular hypertrophy (LVH) predict poor prognosis. We determined whether the ACE inhibitor ramipril prevents the development and causes regression of ECG-LVH and whether these changes are associated with improved prognosis independent of blood pressure reduction. METHODS AND RESULTS: In the Heart Outcomes Prevention Evaluation (HOPE) study, patients at high risk were randomly assigned to ramipril or placebo and followed for 4.5years. ECGs were recorded at baseline and at study end. We compared prevention/regression and development/persistence of ECG-LVH in the two groups and related these changes to outcomes. At baseline, 676 patients had LVH (321 in the ramipril group and 355 in the placebo group) and 7605 patients did not have LVH (3814 in the ramipril group and 3791 in the placebo group). By study end, 336 patients in the ramipril group (8.1%) compared with 406 in the placebo group (9.8%) had development/persistence of LVH; in contrast, 3799 patients in the ramipril group (91.9%) compared with 3740 in the placebo group (90.2%) had regression/prevention of LVH (P=0.007). The effect of ramipril on LVH was independent of blood pressure changes. Patients who had regression/prevention of LVH had a lower risk of the predefined primary outcome (cardiovascular death, myocardial infarction, or stroke) compared with those who had development/persistence of LVH (12.3% versus 15.8%, P=0.006) and of congestive heart failure (9.3% versus 15.4%, P<0.0001). CONCLUSIONS: The ACE inhibitor ramipril decreases the development and causes regression of ECG-LVH independent of blood pressure reduction, and these changes are associated with reduced risk of death, myocardial infarction, stroke, and congestive heart failure.

The thermal conductivity of disordered silicon-germanium alloys is computed from density-functional perturbation theory and with relaxation times that include both harmonic and anharmonic scattering terms. We show that this approach yields an excellent agreement at all compositions with experimental results and provides clear design rules for the engineering of nanostructured thermoelectrics. For Si(x)Ge(1-x), more than 50% of the heat is carried at room temperature by phonons of mean free path greater than 1 μm, and an addition of as little as 12% Ge is sufficient to reduce the thermal conductivity to the minimum value achievable through alloying. Intriguingly, mass disorder is found to increase the anharmonic scattering of phonons through a modification of their vibration eigenmodes, resulting in an increase of 15% in thermal resistivity.

It is expected that autonomous vehicles capable of driving without human supervision will be released to market within the next decade. For user acceptance, such vehicles should not only be safe and reliable, they should also provide a comfortable user experience. However, individual perception of comfort may vary considerably among users. Whereas some users might prefer sporty driving with high accelerations, others might prefer a more relaxed style. Typically, a large number of parameters such as acceleration profiles, distances to other cars, speed during lane changes, etc., characterize a human driver's style. Manual tuning of these parameters may be a tedious and error-prone task. Therefore, we propose a learning from demonstration approach that allows the user to simply demonstrate the desired style by driving the car manually. We model the individual style in terms of a cost function and use feature-based inverse reinforcement learning to find the model parameters that fit the observed style best. Once the model has been learned, it can be used to efficiently compute trajectories for the vehicle in autonomous mode. We show that our approach is capable of learning cost functions and reproducing different driving styles using data from real drivers.

This paper studies a state estimation scheme for a reduced electrochemical battery model, using voltage and current measurements. Real-time electrochemical state information enables high-fidelity monitoring and high-performance operation in advanced battery management systems, for applications such as consumer electronics, electrified vehicles, and grid energy storage. This paper derives a single particle model (SPM) with electrolyte that achieves higher predictive accuracy than the SPM. Next, we propose an estimation scheme and prove estimation error system stability, assuming that the total amount of lithium in the cell is known. The state estimation scheme exploits the dynamical properties, such as marginal stability, local invertibility, and conservation of lithium. Simulations demonstrate the algorithm's performance and limitations.

It is well known that controlling the attitude of a rigid body is subject to topological constraints. We illustrate, with examples, the problems that arise when using continuous and (memoryless) discontinuous quaternion-based state-feedback control laws for global attitude stabilization. We propose a quaternion-based hybrid feedback scheme that solves the global attitude tracking problem in three scenarios: full state measurements, only measurements of attitude, and measurements of attitude with angular velocity measurements corrupted by a constant bias. In each case, the hybrid feedback is dynamic and incorporates hysteresis-based switching using a single binary logic variable for each quaternion error state. When only attitude measurements are available or the angular rate is corrupted by a constant bias, the proposed controller is observer-based and incorporates an additional quaternion filter and bias observer. The hysteresis mechanism enables the proposed scheme to simultaneously avoid the “unwinding phenomenon” and sensitivity to arbitrarily small measurement noise that is present in discontinuous feedbacks. These properties are shown using a general framework for hybrid systems, and the results are demonstrated by simulation.

&lt;div class="htmlview paragraph"&gt;Although the total number of car occupants involved in accidents in Germany has not significantly reduced during the past 10 years, the number of fatalities has steadily decreased. Most of the severe accidents result from a loss of control of the car. The problem of the driver losing control of his car will be explained. This problem is then used to formulate the goal for the vehicle dynamics control system ESP (&lt;u&gt;E&lt;/u&gt;lectronic &lt;u&gt;S&lt;/u&gt;tability &lt;u&gt;P&lt;/u&gt;rogram, also known as VDC). The approach chosen to reach this goal will then be shown. It will be shown that the vehicle slip angle is a crucial indicator for the maneuverability of the automobile. Since the complete vehicle state is not readily available, estimation algorithms are used to supply the control algorithms with sufficient information. With the automatic control of the slip angle the required yaw moment can be generated by individual wheel slip control. By using two examples it will be shown, that ESP can significantly improve vehicle handling in extreme maneuvers by automatically controlling the brakes and the engine.&lt;/div&gt;

The Raven-II is a platform for collaborative research on advances in surgical robotics. Seven universities have begun research using this platform. The Raven-II system has two 3-DOF spherical positioning mechanisms capable of attaching interchangeable four DOF instruments. The Raven-II software is based on open standards such as Linux and ROS to maximally facilitate software development. The mechanism is robust enough for repeated experiments and animal surgery experiments, but is not engineered to sufficient safety standards for human use. Mechanisms in place for interaction among the user community and dissemination of results include an electronic forum, an online software SVN repository, and meetings and workshops at major robotics conferences.

The identification and management of work dependencies is a fundamental challenge in software development organizations. This paper argues that modularization, the traditional technique intended to reduce interdependencies among components of a system, has serious limitations in the context of software development. We build on the idea of congruence, proposed in our prior work, to examine the relationship between the structure of technical and work dependencies and the impact of dependencies on software development productivity. Our empirical evaluation of the congruence framework showed that when developers' coordination patterns are congruent with their coordination needs, the resolution time of modification requests was significantly reduced. Furthermore, our analysis highlights the importance of identifying the "right" set of technical dependencies that drive the coordination requirements among software developers. Call and data dependencies appear to have far less impact than logical dependencies.

Defects are important features in two-dimensional (2D) materials that have a strong influence on their chemical and physical properties. Through the enhanced chemical reactivity at defect sites (point defects, line defects, etc.), one can selectively functionalize 2D materials via chemical reactions and thereby tune their physical properties. We demonstrate the selective atomic layer deposition of LiF on defect sites of h-BN prepared by chemical vapor deposition. The LiF deposits primarily on the line and point defects of h-BN, thereby creating seams that hold the h-BN crystallites together. The chemically and mechanically stable hybrid LiF/h-BN film successfully suppresses lithium dendrite formation during both the initial electrochemical deposition onto a copper foil and the subsequent cycling. The protected lithium electrodes exhibit good cycling behavior with more than 300 cycles at relatively high coulombic efficiency (>95%) in an additive-free carbonate electrolyte.