Toyota

About: Toyota is a company organization based out in Safenwil, Switzerland. It is known for research contribution in the topics: Internal combustion engine & Battery (electricity). The organization has 40032 authors who have published 55003 publications receiving 735317 citations. The organization is also known as: Toyota Motor Corporation & Toyota Jidosha KK.

Sorption of water in nylon 6-clay hybrid

Abstract: Various nylon 6-clay hybrids, such as molecular composites of nylon 6 and silicate layers of montmorillonite and saponite, NCHs and NCHPs, respectively, have been synthesized. Sorption of water in those hybrids was measured to estimate the resistance to water permeation. The diffusion coefficient D and the partition coefficient K were obtained from the sorption curves. The resistance to water permeation of NCH was superior to that of nylon 6 because of a decrease in D. In NCHP, saponite had a smaller effect on the increase of the resistance. The diffusion coefficients in NCH and NCHP decreased with an increase in the amount of clay minerals. It was found that those diffusion coefficients were well described by the contribution of the constrained region calculated from the storage and the loss modulus at the glass transition temperature. According to the mixing law, the following equation was obtained between the diffusion coefficient D and the fraction of the constrained region C, D = Da(1 – C)1/n, where the values of Da (diffusion coefficient in amorphous nylon 6) and n (morphology parameter) were 12.6 X 10−7cm2/s and 0.585, respectively. © 1993 John Wiley & Sons, Inc.

Understanding and applying coulombic efficiency in lithium metal batteries

Abstract: Coulombic efficiency (CE) has been widely used in battery research as a quantifiable indicator for the reversibility of batteries While CE helps to predict the lifespan of a lithium-ion battery, the prediction is not necessarily accurate in a rechargeable lithium metal battery Here, we discuss the fundamental definition of CE and unravel its true meaning in lithium-ion batteries and a few representative configurations of lithium metal batteries Through examining the similarities and differences of CE in lithium-ion batteries and lithium metal batteries, we establish a CE measuring protocol with the aim of developing high-energy long-lasting practical lithium metal batteries The understanding of CE and the CE protocol are broadly applicable in other rechargeable metal batteries including Zn, Mg and Na batteries Coulombic efficiency (CE) has been frequently used to assess the cyclability of newly developed materials for lithium metal batteries The authors argue that caution must be exercised during the assessment of CE, and propose a CE testing protocol for the development of lithium metal batteries

A strategy to apply machine learning to small datasets in materials science

Abstract: There is growing interest in applying machine learning techniques in the research of materials science. However, although it is recognized that materials datasets are typically smaller and sometimes more diverse compared to other fields, the influence of availability of materials data on training machine learning models has not yet been studied, which prevents the possibility to establish accurate predictive rules using small materials datasets. Here we analyzed the fundamental interplay between the availability of materials data and the predictive capability of machine learning models. Instead of affecting the model precision directly, the effect of data size is mediated by the degree of freedom (DoF) of model, resulting in the phenomenon of association between precision and DoF. The appearance of precision–DoF association signals the issue of underfitting and is characterized by large bias of prediction, which consequently restricts the accurate prediction in unknown domains. We proposed to incorporate the crude estimation of property in the feature space to establish ML models using small sized materials data, which increases the accuracy of prediction without the cost of higher DoF. In three case studies of predicting the band gap of binary semiconductors, lattice thermal conductivity, and elastic properties of zeolites, the integration of crude estimation effectively boosted the predictive capability of machine learning models to state-of-art levels, demonstrating the generality of the proposed strategy to construct accurate machine learning models using small materials dataset. Machine learning can be useful for materials prediction if crude estimations of the outcome are integrated in the code. Machine learning has been attracting tremendous attention lately due to its predictive power; evidence suggests it is directly proportional to the size of the available datasets. Machine learning can be useful in predicting new materials and novel properties, but materials sets tend to be smaller and more diverse than other fields. Ying Zhang and Chen Ling from the Toyota Research Institute of North America report that these small datasets affect the freedom of the algorithms and thus limit their predictive capabilities. In order to counterbalance the effect, they suggest introducing in the code crude estimations of the targeted property, obtained by other means.

Closed-loop optimization of fast-charging protocols for batteries with machine learning.

Abstract: Simultaneously optimizing many design parameters in time-consuming experiments causes bottlenecks in a broad range of scientific and engineering disciplines1,2. One such example is process and control optimization for lithium-ion batteries during materials selection, cell manufacturing and operation. A typical objective is to maximize battery lifetime; however, conducting even a single experiment to evaluate lifetime can take months to years3-5. Furthermore, both large parameter spaces and high sampling variability3,6,7 necessitate a large number of experiments. Hence, the key challenge is to reduce both the number and the duration of the experiments required. Here we develop and demonstrate a machine learning methodology to efficiently optimize a parameter space specifying the current and voltage profiles of six-step, ten-minute fast-charging protocols for maximizing battery cycle life, which can alleviate range anxiety for electric-vehicle users8,9. We combine two key elements to reduce the optimization cost: an early-prediction model5, which reduces the time per experiment by predicting the final cycle life using data from the first few cycles, and a Bayesian optimization algorithm10,11, which reduces the number of experiments by balancing exploration and exploitation to efficiently probe the parameter space of charging protocols. Using this methodology, we rapidly identify high-cycle-life charging protocols among 224 candidates in 16 days (compared with over 500 days using exhaustive search without early prediction), and subsequently validate the accuracy and efficiency of our optimization approach. Our closed-loop methodology automatically incorporates feedback from past experiments to inform future decisions and can be generalized to other applications in battery design and, more broadly, other scientific domains that involve time-intensive experiments and multi-dimensional design spaces.