Revealing the potential of apparent critical current density of Li/garnet interface with capacity perturbation strategy

Apparent critical current density(j_(Ac)^(a))of garnet all-solid-state lithium metal symmetric cells(ASSLSCs)is a fundamental parameter for designing all-solid-state lithium metal batteries.Nevertheless,how much the possible maximum apparent current density that a given ASSLSC system can endure and how to reveal this potential still require study.Herein,a capacity perturbation strategy aiming to better measure the possible maximum j_(Ac)^(a)is proposed for the first time.With garnet-based plane-surface structure ASSLSCs as an exemplification,the j_(Ac)^(a)is quite small when the capacity is dramatically large.Under a perturbed capacity of 0.001 mA h cm^(-2),the j_(Ac)^(a)is determined to be as high as 2.35 mA cm^(-2)at room temperature.This investigation demonstrates that the capacity perturbation strategy is a feasible strategy for measuring the possible maximum j_(Ac)^(a)of Li/solid electrolyte interface,and hopefully provides good references to explore the critical current density of other types of electrochemical systems.

A User-Transformer Relation Identification Method Based on QPSO and Kernel Fuzzy Clustering

User-transformer relations are significant to electric power marketing,power supply safety,and line loss calculations.To get accurate user-transformer relations,this paper proposes an identification method for user-transformer relations based on improved quantum particle swarm optimization(QPSO)and Fuzzy C-Means Clustering.The main idea is:as energymeters at different transformer areas exhibit different zero-crossing shift features,we classify the zero-crossing shift data from energy meters through Fuzzy C-Means Clustering and compare it with that at the transformer end to identify user-transformer relations.The proposed method contributes in three main ways.First,based on the fuzzy C-means clustering algorithm(FCM),the quantum particle swarm optimization(PSO)is introduced to optimize the FCM clustering center and kernel parameters.The optimized FCM algorithm can improve clustering accuracy and efficiency.Since easily falls into a local optimum,an improved PSO optimization algorithm(IQPSO)is proposed.Secondly,considering that traditional FCM cannot solve the linear inseparability problem,this article uses a FCM(KFCM)that introduces kernel functions.Combinedwith the IQPSOoptimization algorithm used in the previous step,the IQPSO-KFCM algorithm is proposed.Simulation experiments verify the superiority of the proposed method.Finally,the proposed method is applied to transformer detection.The proposed method determines the class members of transformers and meters in the actual transformer area,and obtains results consistent with actual user-transformer relations.This fully shows that the proposed method has practical application value.