Spinel LiMn2O4 Nanorods via Template–Engaged Method and as Cathode Materials for Li-Ion Batteries

Spinel LiMn2O4 nanorods were prepared by a hydrothermal method followed by solid-state lithiation. The produce β-MnO2 nanowire as template, and LiOH·H2O was used as lithium source. The spinel LiMn2O4 nanorods samples were characterized by SEM, XRD, (HR)TEM, and galvanostatic charge/discharge profile measurement. Compared with the LiMn2O4 nanoparticles, the LiMn2O4 nanorods showed superior cycling stability, better rate capability, good high temperature performance, and delivered a discharge capacity of 122 mAh/g (at 1 C, 100 cycles).

A computational approach for generating continuous estimates of motor unit discharge rates and visualizing population discharge characteristics

Objective: Successive improvements in high density surface electromyography and decomposition techniques have facilitated an increasing yield in decomposed motor unit (MU) spike times. Though these advancements enhance the generalizability of findings and promote the application of MU discharge characteristics to inform the neural control of motor output, limitations remain. Specifically, 1) common approaches for generating smooth estimates of MU discharge rates introduce artifacts in quantification, which may bias findings, and 2) discharge characteristics of large MU populations are often difficult to visualize. Approach: In the present study, we propose support vector regression (SVR) as an improved approach for generating smooth continuous estimates of discharge rate and compare the fit characteristics of SVR to traditionally used methods, including Hanning window filtering and polynomial regression. Furthermore, we introduce ensembles as a method to visualize the discharge characteristics of large MU populations. We define ensembles as the average discharge profile of a subpopulation of MUs, composed of a time normalized ensemble average of all units within this subpopulation. Analysis was conducted with MUs decomposed from the tibialis anterior (N = 2128), medial gastrocnemius (N = 2673), and soleus (N = 1190) during isometric plantarflexion and dorsiflexion contractions. Main Result: Compared to traditional approaches, we found SVR to alleviate commonly observed inaccuracies and produce significantly less absolute fit error in the initial phase of MU discharge and throughout the entire duration of discharge. Additionally, we found the visualization of MU populations as ensembles to intuitively represent population discharge characteristics with appropriate accuracy for visualization. Significance: The results and methods outlined here provide an improved method for generating estimates of MU discharge rate with SVR and present a unique approach to visualizing MU populations with ensembles. In combination, the use of SVR and generation of ensembles represent an efficient method for rendering population discharge characteristics.

Neural Network-Based Electric Vehicle Range Prediction for Smart Charging Optimization

Range prediction is a standard feature in most modern road vehicles, allowing drivers to make informed decisions about when to refuel. Most vehicles make range predictions through data- or model-driven means, monitoring the average fuel consumption rate or using a tuned vehicle model to predict fuel consumption. The uncertainty of future driving conditions makes the range prediction problem challenging, particularly for less pervasive battery electric vehicles (BEV). Most contemporary machine learning-based methods attempt to forecast the battery SOC discharge profile to predict vehicle range. In this work, we propose a novel approach using two recurrent neural networks (RNNs) to predict the remaining range of BEVs and the minimum charge required to safely complete a trip. Each RNN has two outputs which can be used for statistical analysis to account for uncertainties; the first loss function leads to mean and variance estimation (MVE), while the second results in bounded interval estimation (BIE). These outputs of the proposed RNNs are then used to predict the probability of a vehicle completing a given trip without charging, or if charging is needed, the remaining range and minimum charging required to finish the trip with high probability. Training data was generated using a low-order physics model to estimate vehicle energy consumption from historical drive cycle data collected from medium-duty last-mile delivery vehicles. The proposed method demonstrated high accuracy in the presence of day-to-day route variability, with the root-mean-square error (RMSE) below 6% for both RNN models.

Ultrahigh Temperature Flash Sintering of Binder-Less Tungsten Carbide within 6 s

We report on an ultrarapid (6 s) consolidation of binder-less WC using a novel Ultrahigh temperature Flash Sintering (UFS) approach. The UFS technique bridges the gap between electric resistance sintering (≪1 s) and flash spark plasma sintering (20–60 s). Compared to the well-established spark plasma sintering, the proposed approach results in improved energy efficiency with massive energy and time savings while maintaining a comparable relative density (94.6%) and Vickers hardness of 2124 HV. The novelty of this work relies on (i) multiple steps current discharge profile to suit the rapid change of electrical conductivity experienced by the sintering powder, (ii) upgraded low thermal inertia CFC dies and (iii) ultra-high consolidation temperature approaching 2750 °C. Compared to SPS process, the UFS process is highly energy efficient (≈200 times faster and it consumes ≈95% less energy) and it holds the promise of energy efficient and ultrafast consolidation of several conductive refractory compounds.

Modeling of Battery Management for Standalone PV System

This work is focused on developing a model for a Battery management unit for a Solar PV based off grid standalone system and implementing the same using MATLAB tool. A secondary storage device in form a battery is essential to provide an energy backup in any autonomous system. In this work Lithium ion (Li Ion) battery has been considered for modeling as it offers good charge and discharge profile, high power density, occupies less space and less maintenance. It is essential to ensure secure function of such batteries by closely monitoring and control of their State of Charge (SOC). Determination of SOC ensures the remaining energy available in the battery which further helps in discharging the same based on system requirements. The major components of this work include representation of PV source, employing MPPT, regulating the DC output via boost converter, inverter and controlling the flow of energy between the source to battery, load and vice versa.

Hierarchical Mo2C@CNT Hybrid Structure Formation for the Improved Lithium-Ion Battery Storage Performance

2-D transition metal carbides (TMCs)-based anode materials offer competitive performance in lithium-ion batteries (LIBs) owing to its excellent conductivity; cheaper, flexible uses; and superior mechanical stability. However, the electrochemical energy storage of TMCs is still the major obstacle due to their modest capacity and the trends of restacking/aggregation. In this report, the Mo2C nanosheets were attached on conductive CNT network to form a hierarchical 2D hybrid structure, which not only alleviated the aggregation of the Mo2C nanoparticle and facilitated the rapid transference of ion/electron, but also adapted effectually to the hefty volume expansion of Mo2C nanosheets and prevented restacking/collapse of Mo2C structure. Benefitting from the layered Mo2@CNT hybrid structure, the charge/discharge profile produced a 200 mAh g−1 discharge-specific capacity (second cycle) and 132 mAh g−1 reversible-discharge discharge-specific capacity (after 100 cycles) at 50 mA g−1 current density, with high-speed competency and superior cycle stability. The improved storage kinetics for Mo2@CNT hybrid structure are credited to the creation of numerous active catalytic facets and association reaction between the CNT and Mo2C, promoting the efficient electron transfer and enhancing the cycling stability.

A computational approach for generating smooth estimates of motor unit discharge rates and visualizing population discharge characteristics

Objective: Successive improvements in high density surface electromyography and decomposition techniques have facilitated an increasing yield in decomposed motor unit (MU) spike times. Though these advancements enhance the generalizability of findings and promote the application of MU discharge characteristics to inform the neural control of motor output, limitations remain. Specifically, 1) common approaches for generating smooth estimates of MU discharge rates introduce artifacts in quantification, which may bias findings, and 2) discharge characteristics of large MU populations are often difficult to visualize. Approach: In the present study, we propose support vector regression (SVR) as an improved approach for generating continuous estimates of discharge rate and compare the fit characteristics of SVR to traditionally used methods, including Hanning window filtering and polynomial regression. Furthermore, we introduce ensembles as a method to visualize the discharge characteristics of large MU populations. We define ensembles as the average discharge profile of a subpopulation of MUs, composed of a time normalized ensemble average of all units within this subpopulation. Analysis was conducted with MUs decomposed from the tibialis anterior (N = 2128), medial gastrocnemius (N = 2673), and soleus (N = 1190) during isometric plantarflexion and dorsiflexion contractions. Main Result: Compared to traditional approaches, we found SVR to alleviate commonly observed inaccuracies and produce significantly less absolute fit error in the initial phase of MU discharge and throughout the entire duration of discharge. Additionally, we found the visualization of MU populations as ensembles to intuitively represent population discharge characteristics with appropriate accuracy for visualization. Significance: The results and methods outlined here provide an improved method for generating smooth estimates of MU discharge rate with SVR and present a unique approach to visualizing MU populations with ensembles. In combination, the use of SVR and generation of ensembles represent an efficient method for rendering population discharge characteristics.

Numerical Investigation of a Flash Flood Process that Occurred in Zhongdu River, Sichuan, China

In 2018, a flash flood occurred in the Zhongdu river, which lies in Yibin, Sichuan province of China. The flood caused many casualties and significant damage to people living nearby. Due to the difficulty in predicting where and when flash floods will happen, it is nearly impossible to set up monitors in advance to detect the floods in detail. Field investigations are usually carried out to study the flood propagation and disaster-causing mechanism after the flood’s happening. The field studies take the relic left by the flash flood to deduce the peak level, peak discharge, bed erosion, etc. and further revel the mechanism between water and sediment transport during the flash flood This kind of relic-based study will generate bigger errors in regions with great bed deformation. In this study, we come up with numerical simulations to investigate the flash flood that happened in the Zhongdu river. The simulations are based on two-dimensional shallow water models coupled with sediment transport and bed deformation models. Based on the real water level and discharge profile measured by a hydrometric station nearby, the numerical simulation reproduced the flash flood in the valley. The results show the flood coverage, water level variation, and velocity distribution during the flood. The simulation offers great help in studying the damage-causing process. Furthermore, simulations without considering sediment transport are also carried out to study the impact of bed erosion and sedimentation. The study proved that, without considering bed deformation, the flood may be greatly underestimated, and the sediment lying in the valley has great impact on flood power.