Cathode chemistries and electrode parameters affecting the fast charging performance of li-ion batteries

Li-ion battery fast-charging technology plays an important role in popularizing electric vehicles (EV), which critically need a charging process that is as simple and quick as pumping fuel for conventional internal combustion engine vehicles. To ensure stable and safe fast charging of Li-ion battery, understanding the electrochemical and thermal behaviors of battery electrodes under high rate charges is crucial, since it provides insight into the limiting factors that restrict the battery from acquiring energy at high rates. In this work, charging simulations are performed on Li-ion batteries that use the LiCoO2 (LCO), LiMn2O4 (LMO), and LiFePO4 (LFP) as the cathodes. An electrochemical-thermal coupling model is first developed and experimentally validated on a 2.6Ah LCO based Li-ion battery and is then adjusted to study the LMO and LFP based batteries. LCO, LMO, and LFP based Li-ion batteries exhibited different thermal responses during charges due to their different entropy profiles, and results show that the entropy change of the LCO battery plays a positive role in alleviating its temperature rise during charges. Among the batteries, the LFP battery is difficult to be charged at high rates due to the charge transfer limitation caused by the low electrical conductivity of the LFP cathode, which, however, can be improved through doping or adding conductive additives. A parametric study is also performed by considering different electrode thicknesses and secondary particle sizes. It reveals that the concentration polarization at the electrode and particle levels can be weaken by using thin electrodes and small solid particles, respectively. These changes are helpful to mitigate the diffusion limitation and improve the performance of Li-ion batteries during high rate charges, but careful consideration should be taken when applying these changes since they can reduce the energy density of the batteries

An advanced meshless method for time fractional diffusion equation

Recently, because of the new developments in sustainable engineering and renewable energy, which are usually governed by a series of fractional partial differential equations (FPDEs), the numerical modelling and simulation for fractional calculus are attracting more and more attention from researchers. The current dominant numerical method for modeling FPDE is Finite Difference Method (FDM), which is based on a pre-defined grid leading to inherited issues or shortcomings including difficulty in simulation of problems with the complex problem domain and in using irregularly distributed nodes. Because of its distinguished advantages, the meshless method has good potential in simulation of FPDEs. This paper aims to develop an implicit meshless collocation technique for FPDE. The discrete system of FPDEs is obtained by using the meshless shape functions and the meshless collocation formulation. The stability and convergence of this meshless approach are investigated theoretically and numerically. The numerical examples with regular and irregular nodal distributions are used to validate and investigate accuracy and efficiency of the newly developed meshless formulation. It is concluded that the present meshless formulation is very effective for the modeling and simulation of fractional partial differential equations

Assessment of traffic impact on future cooperative driving systems: challenges and considerations

Connect & Drive is a start-up project to develop a cooperative driving system and improve the traffic performance on Dutch highways. It consists of two interactive subsystems: cooperative adaptive cruise control (CACC) and connected cruise control (CCC). To assess the traffic performance, a traffic simulation model will be established for large-scale evaluation and providing feedbacks to system designs. This paper studies the factors determining the traffic performance and discusses challenges and difficulties to establish such a traffic simulation model

Quantum Manifestation of Elastic Constants in Nanostructures

Generally, there are two distinct effects in modifying the properties of low-dimensional nanostructures: surface effect (SS) due to increased surface-volume ratio and quantum size effect (QSE) due to quantum confinement in reduced dimension. The SS has been widely shown to affect the elastic constants and mechanical properties of nanostructures. Here, using Pb nanofilm and graphene nanoribbon as model systems, we demonstrate the QSE on the elastic constants of nanostructures by first-principles calculations. We show that generally QSE is dominant in affecting the elastic constants of metallic nanostructures while SS is more pronounced in semiconductor and insulator nanostructures. Our findings have broad implications in quantum aspects of nanomechanics

Understanding the different rotational behaviors of 252^{252}No and 254^{254}No

Total Routhian surface calculations have been performed to investigate rapidly rotating transfermium nuclei, the heaviest nuclei accessible by detailed spectroscopy experiments. The observed fast alignment in $^{252}$No and slow alignment in $^{254}$No are well reproduced by the calculations incorporating high-order deformations. The different rotational behaviors of $^{252}$No and $^{254}$No can be understood for the first time in terms of $\beta_6$ deformation that decreases the energies of the $\nu j_{15/2}$ intruder orbitals below the N=152 gap. Our investigations reveal the importance of high-order deformation in describing not only the multi-quasiparticle states but also the rotational spectra, both providing probes of the single-particle structure concerning the expected doubly-magic superheavy nuclei.Comment: 5 pages, 4 figures, the version accepted for publication in Phys. Rev.

Fast and adaptive fractal tree-based path planning for programmable bevel tip steerable needles

© 2016 IEEE. Steerable needles are a promising technology for minimally invasive surgery, as they can provide access to difficult to reach locations while avoiding delicate anatomical regions. However, due to the unpredictable tissue deformation associated with needle insertion and the complexity of many surgical scenarios, a real-time path planning algorithm with high update frequency would be advantageous. Real-time path planning for nonholonomic systems is commonly used in a broad variety of fields, ranging from aerospace to submarine navigation. In this letter, we propose to take advantage of the architecture of graphics processing units (GPUs) to apply fractal theory and thus parallelize real-time path planning computation. This novel approach, termed adaptive fractal trees (AFT), allows for the creation of a database of paths covering the entire domain, which are dense, invariant, procedurally produced, adaptable in size, and present a recursive structure. The generated cache of paths can in turn be analyzed in parallel to determine the most suitable path in a fraction of a second. The ability to cope with nonholonomic constraints, as well as constraints in the space of states of any complexity or number, is intrinsic to the AFT approach, rendering it highly versatile. Three-dimensional (3-D) simulations applied to needle steering in neurosurgery show that our approach can successfully compute paths in real-time, enabling complex brain navigation

Warped embeddings between Einstein manifolds

Warped embeddings from a lower dimensional Einstein manifold into a higher dimensional one are analyzed. Explicit solutions for the embedding metrics are obtained for all cases of codimension 1 embeddings and some of the codimension n>1 cases. Some of the interesting features of the embedding metrics are pointed out and potential applications of the embeddings are discussed.Comment: 12 pages, to appear in Mod. Phys. Lett.