Progress in electrolyte and interface of hard carbon and graphite anode for sodiumion battery

It is essential to replace lithium-ion batteries(LIBs)from the perspective of the Earth's resources and the sustainable development of mankind.Sodium-ion batteries(SIBs)are important candidates due to their low price and abundant storage capacity.Hard carbon(HC)and graphite have important applications in anode materials of SIBs.In this review,the research progress in electrolyte and interface between HC and graphite anode for SIBs is summarized.The properties and performance of three types of widely used electrolytes(carbo nate ester,ether,and ionic liquid)with additives,as well as the formation of solid electrolyte interface(SEI),which are crucial to the reversible capacity and rate capability of HC anodes,are also discussed.In this review,the co-intercalation performance and mechanism of solvation Na+into graphite are summarized.Besides,the faced challenges and existing problems in this field are also succinctly highlighted.

Machine learning and neural network supported state of health simulation and forecasting model for lithium-ion battery

As the intersection of disciplines deepens,the field of battery modeling is increasingly employing various artificial intelligence(AI)approaches to improve the efficiency of battery management and enhance the stability and reliability of battery operation.This paper reviews the value of AI methods in lithium-ion battery health management and in particular analyses the application of machine learning(ML),one of the many branches of AI,to lithium-ion battery state of health(SOH),focusing on the advantages and strengths of neural network(NN)methods in ML for lithium-ion battery SOH simulation and prediction.NN is one of the important branches of ML,in which the application of NNs such as backpropagation NN,convolutional NN,and long short-term memory NN in SOH estimation of lithium-ion batteries has received wide attention.Reports so far have shown that the utilization of NN to model the SOH of lithium-ion batteries has the advantages of high efficiency,low energy consumption,high robustness,and scalable models.In the future,NN can make a greater contribution to lithium-ion battery management by,first,utilizing more field data to play a more practical role in health feature screening and model building,and second,by enhancing the intelligent screening and combination of battery parameters to characterize the actual lithium-ion battery SOH to a greater extent.The in-depth application of NN in lithium-ion battery SOH will certainly further enhance the science,reliability,stability,and robustness of lithium-ion battery management.