A"Concentrated lonogel-in-Ceramic"Silanization Composite Electrolyte with Superior Bulk Conductivity and Low Interfacial Resistance for Quasi-Solid-State Li Metal Batteries

The ideal composite electrolyte for the pursued safe and high-energy-density lithium metal batteries(LMBs)is expected to demonstrate peculiarity of superior bulk conductivity,low interfacial resistances,and good compatibility against both Li-metal anode and high-voltage cathode.There is no composite electrolyte to synchronously meet all these requirements yet,and the battery performance is inhibited by the absence of effective electrolyte design.Here we report a unique"concentrated ionogel-in-ceramic"silanization composite electrolyte(SCE)and validate an electrolyte design strategy based on the coupling of high-content silane-conditioning garnet and concentrated ionogel that builds well-percolated Li+transport pathways and tackles the interface issues to respond all the aforementioned requirements.It is revealed that the silane conditioning enables the uniform dispersion of garnet nanoparticles at high content(70 wt%)and forms mixed-lithiophobic-conductive LiF-Li3N solid electrolyte interphase.Notably,the yielding SCE delivers an ultrahigh ionic conductivity of 1.76 X 10^(-3)S cm^(-1)at 25℃,an extremely low Li-metal/electrolyte interfacial area-specific resistance of 13Ωcm^(2),and a distinctly excellent long-term 1200 cycling without any capacity decay in 4.3 V Li‖LiNi_(0.5)Co_(0.2)Mn_(0.3)O_(2)(NCM523)quasi-solid-state LMB.This composite electrolyte design strategy can be extended to other quasi-/solid-state LMBs.

Progress and challenges in multi-stack fuel cell system for high power applications:Architecture and energy management

With the development of fuel cells,multi-stack fuel cell system(MFCS)for high power application has shown tremendous development potential owing to their obvious advantages including high efficiency,durability,reliability,and pollution-free.Accordingly,the state-of-the-art of MFCS is summarized and analyzed to advance its research.Firstly,the MFCS applications are presented in high-power scenarios,especially in transportation applications.Then,to further investigate the MFCS,MFCS including hydrogen and air subsystem,thermal and water subsystem,multi-stack architecture,and prognostics and health monitoring are reviewed.It is noted that prognostics and health monitoring are investigated rarely in MFCS compared with previous research.In addition,the efficiency and durability of MFCS are not only related to the application field and design principle but also the energy management strategy(EMS).The reason is that the EMS is crucial for lifespan,cost,and efficiency in the multi-stack fuel cell system.Finally,the challenge and development potential of MFCS is proposed to provide insights and guidelines for future research.

Contact fatigue life prediction of a bevel gear under spectrum loading

Rolling contact fatigue(RCF)issues,such as pitting,might occur on bevel gears because load fluctuation induces considerable subsurface stress amplitudes.Such issues can dramatically affect the service life of associated machines.An accurate geometry model of a hypoid gear utilized in the main reducer of a heavy-duty vehicle is developed in this study with the commercial gear design software MASTA.Multiaxial stress–strain states are simulated with the finite element method,and the RCF life is predicted using the Brown–Miller–Morrow fatigue criterion.The patterns of fatigue life on the tooth surface are simulated under various loading levels,and the RCF S–N curve is numerically generated.Moreover,a typical torque–time history on the driven axle is described,followed by the construction of program load spectrum with the rain flow method and the Goodman mean stress equation.The effects of various fatigue damage accumulation rules on fatigue life are compared and discussed in detail.Predicted results reveal that the Miner linear rule provides the most optimistic result among the three selected rules,and the Manson bilinear rule produces the most conservative result.

Modeling and Decentralized Predictive Control of Ejector Circulation‑Based PEM Fuel Cell Anode System for Vehicular Application

The dynamic response of fuel cell vehicle is greatly affected by the pressure of reactants.Besides,the pressure difference between anode and cathode will also cause mechanical damage to proton exchange membrane.For maintaining the relative stability of anode pressure,this study proposes a decentralized model predictive controller(DMPC)to control the anodic supply system composed of a feeding and returning ejector assembly.Considering the important influence of load current on the system,the piecewise linearization approach and state space with current-induced disturbance compensation are com-paratively analyzed.Then,an innovative switching strategy is proposed to prevent frequent switching of the sub-model-based controllers and to ensure the most appropriate predictive model is applied.Finally,simulation results demonstrate the better stability and robustness of the proposed control schemes compared with the traditional proportion integration differentia-tion controller under the step load current,variable target and purge disturbance conditions.In particular,in the case of the DC bus load current of a fuel cell hybrid vehicle,the DMPC controller with current-induced disturbance compensation has better stability and target tracking performance with an average error of 0.15 kPa and root mean square error of 1.07 kPa.

Study of degradation of fuel cell stack based on the collected high-dimensional data and clustering algorithms calculations

Accurate perception of the performance degradation of fuel cell is very important to detect its health state.However,inconsistent operating conditions of fuel cell vehicles in the test result in errors in the data.In order to obtain a more credible degradation rate,this study proposes a novel method to classify the experimental data collected under different working conditions into similar operating conditions by using dimensionality reduction and clustering algorithms.Firstly,the experimental data collected from fuel cell vehicles belong to high-dimensional data.Then projecting high-dimensional data into three-dimensional feature vector space via principal component analysis(PCA).The dimension-reduced three-dimensional feature vectors are input into the clustering algorithm,such as K-means and density-based noise application spatial clustering(DBSCAN).According to the clustering results,the fuel cell voltage data with similar operating conditions can be classified.Finally,the selected voltage data can be used to precisely represent the true performance degradation of an on-board fuel cell stack.The results show that the voltage using the K-means algorithm declines the fastest,followed by the DBSCAN algorithm, finally the original data, which indicates that the performance of the fuel cell actually declines faste. Early intervention can prolong its life to the greatest extent.