Thermal explosion energy evaluated by thermokinetic analysis for series- and parallel-circuit NMC lithium battery modules

Abstract CEMP-no stars, a subset of carbon enhanced metal poor (CEMP) stars ($\rm [C/Fe]\ge 0.7$ and $\rm [Fe/H]\lesssim -1$) have been discovered in ultra-faint dwarf (UFD) galaxies, with Mvir ≈ 108 M⊙ and M* ≈ 103 − 104 M⊙ at z = 0, as well as in the halo of the Milky Way (MW). These CEMP-no stars are local fossils that may reflect the properties of the first (Pop III) and second (Pop II) generation of stars. However, cosmological simulations have struggled to reproduce the observed level of carbon enhancement of the known CEMP-no stars. Here we present new cosmological hydrodynamic zoom-in simulations of isolated UFDs that achieve a gas mass resolution of mgas ≈ 60 M⊙. We include enrichment from Pop III faint supernovae (SNe), with ESN = 0.6 × 1051 erg, to understand the origin of CEMP-no stars. We confirm that Pop III and Pop II stars are mainly responsible for the formation of CEMP and C-normal stars respectively. New to this study, we find that a majority of CEMP-no stars in the observed UFDs and the MW halo can be explained by Pop III SNe with normal explosion energy (ESN = 1.2 × 1051 erg) and Pop II enrichment, but faint SNe might also be needed to produce CEMP-no stars with $\rm [C/Fe]\gtrsim 2$, corresponding to the absolute carbon abundance of $\rm A(C)\gtrsim 6.0$. Furthermore, we find that while we create CEMP-no stars with high carbon ratio $\rm [C/Fe]\approx 3-4$, by adopting faint SNe, it is still challenging to reproduce CEMP-no stars with extreme level of carbon abundance of $\rm A(C)\approx 7.0-7.5$, observed both in the MW halo and UFDs.

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Organosulfide‐Based Deep Eutectic Electrolyte for Lithium Battery

Angewandte Chemie ◽

10.1002/ange.202016875 ◽

2021 ◽

Author(s):

Jiahan Song ◽

Yubing Si ◽

Wei Guo ◽

Donghai Wang ◽

Yongzhu Fu

Keyword(s):

Lithium Battery

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Estimation of the Hot Swap Circulation Current of a Multiple Parallel Lithium Battery System with an Artificial Neural Network Model

Electronics ◽

10.3390/electronics10121448 ◽

2021 ◽

Vol 10 (12) ◽

pp. 1448

Author(s):

Nam-Gyu Lim ◽

Jae-Yeol Kim ◽

Seongjun Lee

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Lithium Battery ◽

Battery Pack ◽

Battery Management ◽

Ann Model ◽

Battery System ◽

Load Current ◽

Circulating Current ◽

Artificial Neural

Battery applications, such as electric vehicles, electric propulsion ships, and energy storage systems, are developing rapidly, and battery management issues are gaining attention. In this application field, a battery system with a high capacity and high power in which numerous battery cells are connected in series and parallel is used. Therefore, research on a battery management system (BMS) to which various algorithms are applied for efficient use and safe operation of batteries is being conducted. In general, maintenance/replacement of multi-series/multiple parallel battery systems is only possible when there is no load current, or the entire system is shut down. However, if the circulating current generated by the voltage difference between the newly added battery and the existing battery pack is less than the allowable current of the system, the new battery can be connected while the system is running, which is called hot swapping. The circulating current generated during the hot-swap operation is determined by the battery’s state of charge (SOC), the parallel configuration of the battery system, temperature, aging, operating point, and differences in the load current. Therefore, since there is a limit to formulating a circulating current that changes in size according to these various conditions, this paper presents a circulating current estimation method, using an artificial neural network (ANN). The ANN model for estimating the hot-swap circulating current is designed for a 1S4P lithium battery pack system, consisting of one series and four parallel cells. The circulating current of the ANN model proposed in this paper is experimentally verified to be able to estimate the actual value within a 6% error range.

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