21:I[432972,["/_next/static/chunks/bb923814e1f4d2e9.js","/_next/static/chunks/dd3ee9f5b7716ca6.js","/_next/static/chunks/4204a0ae82542ca8.js","/_next/static/chunks/30036bedc5269936.js","/_next/static/chunks/6e1aa89a642f40be.js","/_next/static/chunks/cd5e572bc3e4eb92.js","/_next/static/chunks/8ffd0b71567920d8.js","/_next/static/chunks/67ea6dfd21f16c5f.js","/_next/static/chunks/24eb4379f68013fd.js","/_next/static/chunks/b8cd775ffdf935bd.js","/_next/static/chunks/983103cd9eb7b428.js","/_next/static/chunks/d4ac9dcf27f6ff52.js","/_next/static/chunks/294b9525248c8e5f.js"],"default"] 22:T4bb,Lithium-ion battery State of Health (SOH) estimation is an essential issue in battery management systems. In order to better estimate battery SOH, Extreme Learning Machine (ELM) is used to establish a model to estimate lithium-ion battery SOH. The Swarm Optimization algorithm (PSO) is used to automatically adjust and optimize the parameters of ELM to improve estimation accuracy. Firstly, collect cyclic aging data of the battery and extract five characteristic quantities related to battery capacity from the battery charging curve and increment capacity curve. Use Grey Relation Analysis (GRA) method to analyze the correlation between battery capacity and five characteristic quantities. Then, an ELM is used to build the capacity estimation model of the lithium-ion battery based on five characteristics, and a PSO is introduced to optimize the parameters of the capacity estimation model. The proposed method is validated by the degradation experiment of the lithium-ion battery under different conditions. The results show that the battery capacity estimation model based on ELM and PSO has better accuracy and stability in capacity estimation, and the average absolute percentage error is less than 1%.23:T44d,MXenes have extensive applications due to their different properties determined by intrinsic structures and various functional groups. Exploring different functional groups of MXenes leads to improved performance or potential applications. In this work, we prepared new Ti₃C₂PBr_x (x=0.4-0.6) MXene with phosphorus functional groups (-P) through a two-step gas-phase reaction. The acquisition of -P is achieved by replacing bromine functional groups (-Br) of Ti₃C₂Br₂ in the phosphorus vapor. After -Br is replaced with -P, Ti₃C₂PBr_x MXene shows an improved areal capacitance (360 mF cm^-2) at 20 mV s^-1 compared with Ti₃C₂Br₂ MXene (102 mF cm^-2). At a current density of 5 mA cm^-2 after 10000 cycles, the capacitance retention of Ti₃C₂PBr_x MXene has not decreased. The pseudocapacitive enhancement mechanism has been discovered based on the dual redox sites of the functional groups -P and Ti.24:T570,

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