Te0.045S0.955PAN composite with high average discharge voltage for Li–S battery

As a sulfur-containing cathode material,sulfide polyacrylonitrile(SPAN)is expected to be used for longlife lithium-sulfur battery because there is no shuttle effect occurred in its charge process.However,its specific capacity and discharge potential need to be further improved to satisfy the urgent demands for high-performance batteries.In this paper,Te0.045S0.955PAN composite was synthesized by co-heating TexS1-x and PAN,and the superior electrochemical performance to that of SPAN was obtained because of doping Te with high conductivity.The as-prepared Te0.045S0.955PAN composite possessed the specific capacity of 675 mAh g^-1 after 100 cycles at the current density of 0.1 A g^-1 with high capacity retention of96.6%compared to the second cycle.Especially,during cycling,Te0.045S0.955PAN showed average discharge voltages of 1.88-1.91 V,which were higher than 1.85-1.88 V for SPAN at the same current density.Thus doping Te provides a new strategy for increasing the energy density of SPAN.

Breakthrough detection in electrochemical discharge drilling to enhance machining stability

Film cooling holes are widely used in aero-engine turbine blades.These blades feature large numbers of holes with complex angles and require a high level of surface integrity.Electrochemical discharge drilling(ECDD)combines the high efficiency of electrical discharge drilling(EDD)with high quality of electrochemical drilling(ECD).However,due to the existence of a variety of energy for material removal,accurate and timely detection of breakthroughs is fraught with difficulties.An insufficient preset setting distance results in a tiny exit aperture,influencing the structure's shape.In addition,the electrode is prone to bending at a large overfeeding distance,causing secondary discharge damaging sidewall surface integrity.This paper compares and analyzes the characteristics of processing waveforms using EDD and ECDD.A novel breakthrough detection method is proposed based on the variance signal of average voltage(VSAV)to increase machining stability and achieve fabrication without a recast layer.This method extracts the fluctuation transformation by calculating the variance of the average.Following signal detection,the overfeeding distance is quantified.An experiment is used to validate the breakthrough detection with 100%accuracy in all tests.The optimum overfeeding distances for hole angles of 0°,30°,and 60° are obtained,and the stable removal of the recast layer is realized.Finally,the effectiveness of the method is verified on a typical workpiece with a double-wall structure and a nickel-based single crystal blade.

Fault diagnosis for lithium-ion batteries in electric vehicles based on signal decomposition and two-dimensional feature clustering

Battery fault diagnosis is essential for ensuring the reliability and safety of electric vehicles(EVs).The existing battery fault diagnosis methods are difficult to detect faults at an early stage based on the real-world vehicle data since lithium-ion battery systems are usually accompanied by inconsistencies,which are difficult to distinguish from faults.A fault diagnosis method based on signal decomposition and two-dimensional feature clustering is introduced in this paper.Symplectic geometry mode decomposition(SGMD)is introduced to obtain the components characterizing battery states,and distance-based similarity measures with the normalized extended average voltage and dynamic time warping distances are established to evaluate the state of batteries.The 2-dimensional feature clustering based on DBSCAN is developed to reduce the number of feature thresholds and differentiate flaw cells from the battery pack with only one parameter under a wide range of values.The proposed method can achieve fault diagnosis and voltage anomaly identification as early as 43 days ahead of the thermal runaway.And the results of four electric vehicles and the comparison with other traditional methods validated the proposed method with strong robustness,high reliability,and long time scale warning,and the method is easy to implement online.