Experimental Study on Long Cycling Performance of NCM523 Lithium-Ion Batteries and Optimization of Charge-Discharge Strategy

With the increasing demand for clean renewable energy and electric cars,people have put forward higher requirement for the energy storage system.One of the most successful lithium-ion batteries with a cathode combination of lithium nickel manganese cobalt oxide(also called NCM lithium-ion battery),has been playing an increasingly important role.So far,numerous research has been done on the fabrication of cathode material with optimization of its composition,design,and assembly of the battery system in order to improve the energy storage performance.However,most of the previous studies were conducted based on relatively short cycling time of testing,with limited charge-discharge cycles of no more than 1000.Thus the conclusions were insufficient to be applied in the practical working condition.In this work,by using the developed NCM523 lithium-ion batteries,we have performed a series of ultra-long cycling tests on the individual cell and its module,with a comprehensive study on the relationship between the retained capacity after long cycling time and the depth of discharge(DOD),charge-discharge rate and operating temperature.Optimization of the charge-discharge strategies on a single cell and the whole module was also made to effectively improve the overall energy storage efficiency.This experimental study offers a guideline for the efficient use of similar types of lithium-ion batteries in the practical working condition.The developed batteries together with the optimized charge-discharge strategy proposed here are promising to meet the requirements for applications of stationary energy storage and electric cars.

A Deep Reinforcement Learning Bidding Algorithm on Electricity Market

In this paper,we design a new bidding algorithm by employing a deep reinforcement learning approach.Firms use the proposed algorithm to estimate conjectural variation of the other firms and then employ this variable to generate the optimal bidding strategy so as to pursue maximal profits.With this algorithm,electricity generation firms can improve the accuracy of conjectural variations of competitors by dynamically learning in an electricity market with incomplete information.Electricity market will reach an equilibrium point when electricity firms adopt the proposed bidding algorithm for a repeated game of power trading.The simulation examples illustrate the overall energy efficiency of power network will increase by 9.90%as the market clearing price decreasing when all companies use the algorithm.The simulation examples also show that the power demand elasticity has a positive effect on the convergence of learning process.

Quasi-freestanding, striped WS2 monolayer with an invariable band gap on Au（001）

Revealing the structural/electronic features and interfacial interactions of monolayer MoS2 and WS2 on metals is essential to evaluating the performance of related devices.In this study,we focused on the atomic-scale features of monolayer WS2 on Au（001） synthesized via chemical vapor deposition.Scanning tunneling microscopy and spectroscopy reveal that the WS2/Au（001） system exhibits a striped superstructure similar to that of MoS2/Au（001） but weaker interfacial interactions,as evidenced by experimental and theoretical investigations.Specifically,the WS2/Au（001） band gap exhibits a relatively intrinsic value of ～ 2.0 eV.However,the band gap can gradually decrease to ～ 1.5 eV when the sample annealing temperature increases from ～370 to 720 ℃.In addition,the doping level （or Fermi energy） of monolayer WS2/Au（001） varies little over the valley and ridge regions of the striped patterns because of the homogenous distributions of point defects introduced by annealing.Briefly,this work provides an in-depth investigation into the interfacial interactions and electronic properties of monolayer MX2 on metal substrates.