Liqin Qian

Lithium-ion pouch cells exhibit significant irreversible expansion during long-term cycling, which determines overall performance and induces degradation failure without an appropriate mechanical fixture. However, the synergistic mechanism of mechanical pre-torque and battery state on battery electrochemical performance is unclear. To address this issue, this study reveals the electrochemical characteristic evolution of commercial lithium-ion pouch cells during cycling degradation, under varying mechanical pre-torques (0 N·m, 0.5 N·m, 1 N·m, and 1.5 N·m) and at different states of charge (SOCs, 0%, 25%, 50%, 75%, and 100%). Results indicate that moderate pressure (0.5 N·m) optimizes the electrode–electrolyte contact, reducing solid–electrolyte interphase resistance (R_SEI), ohmic resistance (R_O), charge transfer resistance (R_ct), and Warburg coefficient (W) by over 55%, 60%, 30% and 20%, respectively, compared with the free state. High pressure (1.5 N·m) induces impedance rebound due to pore compression, with the increment ranging from 20% to 40%. Furthermore, synergistic impact analysis proves that pressure alters impedance sensitivity to SOC, with changing rates amplifying from <5% per SOC unit under low pressure to 10–15% under high pressure, particularly exacerbating interface passivation at low SOC and side reactions at high SOC. Moreover, a Gaussian process regression (GPR) based adaptive SOC estimation model is developed, incorporating impedance features and pressure paths, achieving a root mean square error of 2.1% and enhancing accuracy by 10–15% over conventional methods in high-pressure scenarios. This study provides guidance for the next-generation pouch cell module design and management.

This paper develops a finite element analysis model to investigate the seepage characteristics of cement sheaths, considering the flow properties of their porous medium. The model’s applicability under various conditions was evaluated through grid sensitivity tests and model validation, indicating that it effectively captures the seepage behavior of cement sheaths with a reasonable degree of reliability. Key parameters, including cement sheath length, permeability, gap structure, pressure differential, and fluid properties, were analyzed using finite element methods to determine their impact on seepage flow. The findings reveal that crack width, permeability, and cement sheath length significantly influence seepage flow in both liquid and gas media. These insights enhance the understanding and prediction of cement sheath seepage characteristics under diverse conditions.

PDC (polycrystalline diamond compact) drill bits are widely employed for rock-breaking in many industries like underground engineering and building constructions. The cutter layout would directly affect the overall performance of the drill bits. Field applications show that the staggered cutter layout strategy of dual-cutter can increase the drilling efficiency of the PDC bit. In order to explore the rock breaking mechanism of this type of drill bit, a numerical model of a dual-cutter and rock breaking with damage evolution based on a hybrid finite and cohesive element method (FCEM) has been established in this work. The model is verified through Brazilian disk tests. The rock breaking processes of this type of bit have been analyzed, including crack initiation, propagation, and the formation of rock debris. Moreover, the effects of horizontal and vertical offset of the back cutter on the MSE (mechanical special energy) have been investigated. Results demonstrate that the dual-cutter can prominently reduce the MSE compared to a single-cutter. The vertical offset of the back cutter has a minor effect on the MSE, while the horizontal offset is of great significance on the MSE. On this basis, the relationships between the MSE and both the vertical and horizontal offset coefficients have been built based on the response surface methodology (RSM). Finally, an optimized layout solution, with optimal vertical and horizontal offset coefficients of 0.641 and 0.497, is determined via the Gray Wolf algorithm.