Controllable construction of La_(2)Li_(0.5)Co_(0.5)O_(4)multifunctional“armor”to stabilize Li-rich layered oxide cathode for highperformance lithium-ion batteries

Lithium-rich manganese-based cathodes(LR)are valuable cathode materials for the next generation of lithium-ion batteries(LIBs)with high-energy density.However,the fast voltage/capacity decay on cycling is the major obstacle for the practical application induced by the less-than-ideal anionic redox reactions and structure distortion.Herein,in order to tackle these challenges,a perovskite-like La_(2)Li_(0.5)Co_(0.5)O_(4)(LLCO)material is selected as protective surface to stabilize the Li_(1.2)Mn_(0.54)Ni_(0.13)Co_(0.13)O_(2)(LR)substrate through wet chemical coating method.Versatile structure/phase characterizations and electrochemical tests exhibit that the LLCO can not only minish the oxygen evolution and enhance the structure stability,but also restrain the electrolyte corrosion and increase the mechanical strength of cathode materials.Moreover,the coated LLCO with high electronic/ionic conductivity dramatically accelerates the energy storage kinetic,thereby displaying the improved rate performance.Specifically,the optimized LR@LLCO sample(1LLCO)exhibits a high capacity of 250.6 mAh·g^(-1)after 100 cycles at 0.1 C and excellent capacity retention of 82.6%after 200 cycles at 2 C.This work provides a new idea for the modification of LR cathodes toward commercial high-performance LIBs.

Probabilistic load flow method considering large-scale wind power integration

The increasing penetration of wind power brings great uncertainties into power systems,which poses challenges to system planning and operation.This paper proposes a novel probabilistic load flow(PLF)method based on clustering technique to handle large fluctuations from large-scale wind power integration.The traditional cumulant method(CM)for PLF is based on the linearization of load flow equations around the operation point,therefore resulting in significant errors when input random variables have large fluctuations.In the proposed method,the samples of wind power and loads are first generated by the inverse Nataf transformation and then clustered using an improved K-means algorithm to obtain input variable samples with small variances in each cluster.With such pre-processing,the cumulant method can be applied within each cluster to calculate cumulants of output random variables with improved accuracy.The results obtained in each cluster are combined according to the law of total probability to calculate the final cumulants of output random variables for the whole samples.The proposed method is validated on modified IEEE 9-bus and 118-bus test achieve a better performance with the consideration of both traditional CM,2 m+1 point estimate method(PEM),Monte Carlo simulation(MCS)and Latin hypercube sampling(LHS)based MCS,the proposed method can achieve a better performance with the consideration of bothcomputational efficiency and accuracy.